RoboCup – Robohub https://robohub.org Connecting the robotics community to the world Mon, 19 Oct 2026 23:05:18 +0000 en-US hourly 1 https://wordpress.org/?v=7.1.3 First 11 vs 11 humanoid soccer game played at RoboCup 2026 https://robohub.org/first-11-vs-11-humanoid-soccer-game-played-at-robocup-2026/ Fri, 28 Aug 2026 06:25:01 +0000 https://aihub.org/?p=21143 Action from the 11 vs 11 humanoid match at RoboCup 2026. Photo credit: RoboCup Federation.

RoboCup 2026 saw history made, as two teams of 11 humanoids took to the soccer field, the first time a full complement of robots has competed. The game saw B-Human (Bremen, Germany) take on HTWK Robots (Leipzig, Germany), with both sides using machines designed by Booster Robotics.

You can watch highlights of the match below:

Back in 1997, RoboCup’s founders set the lofty goal of developing a team of autonomous robots that could beat the human World Cup champions by 2050. There has been significant progress since those early days, and this match saw another step towards that ambition.

“This match shows how far humanoid robotics has come,” said Ubbo Visser, President of the RoboCup Federation. “We have seen increased teamwork and advanced skills over the years already, but the new humanoid hardware paired with the new level of intelligence provided by AI puts humanoid robot soccer on another level.”

Find out more:

]]> Robots in society, business and culture: July 2026 https://robohub.org/robots-in-society-business-and-culture-july-2026/ Thu, 06 Aug 2026 07:30:38 +0000 https://robohub.org/?p=220835

By Emmet Cole

This new monthly series from IEEE RAS showcases a selection of robotics stories from society, business, research, and culture, as we track the field’s ongoing journey from specialized industrial machines to an increasingly visible social phenomenon.

Technological isolationism or prudence?

On July 28, the United States’ Federal Communications Commission blocked new foreign-made “advanced robotic devices” from receiving the equipment authorization needed for sale in the United States, citing supply-chain vulnerabilities and cybersecurity risks.

The block applies to networked humanoids, quadrupeds, other qualifying mobile robots weighing more than 4.4 lb. In a parallel action, the FCC also restricted foreign-produced connected power inverters.

Existing authorized models are unaffected for now, and exemptions or conditional approvals may be available.

Reaction is predictably mixed. Georg Stieler, a global robotics advisor and managing director for Asia at STM, told The Robot Report:

“In the near term, the measure could slow U.S. physical AI innovation by cutting startups and researchers off from future low-cost Chinese platforms before comparable Western alternatives exist. It also opens opportunities for U.S. suppliers and allied manufacturers able to localize production in the U.S. or obtain conditional approval. Yet excluding foreign products is not the same as building a competitive industrial base.”

Meanwhile, John Moolenaar, a Michigan Republican who chairs the House ​Select Committee on China, told Reuters that the FCC move “protects our country and strengthens our nation’s robotics industry.”

On July 30, Reuters reported that China’s Commerce Ministry threatened to “resolutely retaliate” if the United States maintains the restrictions and urged the United States to immediately withdraw the ban.

Expect this story to run and run over the coming weeks, with many peaks and valleys for various stakeholders.

Speaking of peaks and valleys, Unitree Robotics’ newly launched Super Athlete AS2-W seems to handle them quite well…

Ghostly smudges, puffins & growing ivy

Researchers at Northwestern University have developed a drone that almost disappears while in plain view. Dubbed ‘Phantom Twist’, the drone spins up to 25 times per second, which is too fast for the human eye to see clearly. Instead, the drone becomes a “ghostly smudge” that blends into its environment.

The device could be used to monitor wildlife, survey the environment and inspect infrastructure with less visual disruption. Covert surveillance applications may be a little while off though, as the propellors announce the drone’s presence with great gusto.

Meanwhile, researchers at MIT and EPFL unveiled an ingenious 250-gram robot that can fly, dive beneath the water, swim and then launch itself back into the air using the same pair of flexible, flapping wings.

Inspired by diving birds including puffins and petrels, the Flapping-wing Aerial Aquatic Vehicle cruises through the air at 6.3 metres per second and swims at almost one metre per second. The team envisages future versions flying to remote or hazardous waters to monitor algal blooms, coral reefs, pollutants, fisheries and coastal erosion.

Have you ever been out cycling in the rain and found yourself wishing for a raincoat that could put itself on automatically while you ride? Kim Nam Gyun at South Korea’s KAIST did. And in July that thought culminated in the unveiling of a robotic technology, inspired by climbing ivy, that enables a person to suit up without using their ​hands or requiring aid from others.

The vine robot, which turns the clothing inside out as it moves up the body, was developed in collaboration with researchers from Stanford University, and could find applications in chip cleanrooms and among ‌emergency services personnel.

Humanoid, all too humanoid

Returning to the topic of valleys for a moment, it was uncanny valley time on social media in July following a showcase of the Origin F1 at WAIC 2026 in Shanghai.

Boasting “natural eye contact,” “subtle expressions,” and “new skins, new souls” [sic] the robot provoked both awe and revulsion, and often at the same time, as is the custom in that strange region of robot design.

LG Display provided a contrasting design vision at K-Display 2026, presenting a curved OLED “face” for humanoid robots that communicates through deliberately artificial expressions rather than attempting to pass as human.

A new Bayesian model of the uncanny valley published in July might provide robot designers with a framework for handling uncanny valley design issues in future.

The team from the University of Tokyo note that existing guidelines, “such as adopting robot-like appearances, avoiding excessive realism, and reducing cross-modal mismatches, remain difficult to use for algorithmic design because they are not expressed as manipulable variables.”

The researchers propose a hierarchical Bayesian generative model that operationalizes these guidelines as mathematical design variables. In effect, the model attempts to turn broad advice such as “avoid excessive realism” into variables that designers can manipulate and test.

Separate from robot design questions and complexities, it is, as a general rule in life and robotics, better to keep your head while all around you are losing theirs. Although that message didn’t seem to get through a humanoid fighting robot at a recent event in China, with Pythonesque results…

Historic RoboCup match

On July 5, at RoboCup 2026, two full teams of full-sized humanoid robots played an 11-vs-11 soccer match for the first time, bringing one of robotics’ most ambitious long-term visions closer to reality. The milestone match featured B-Human from Bremen, Germany vs HTWK Robots from Leipzig, Germany.

RoboCup’s long-term objective is gloriously ambitious:

By the middle of the 21st century, a team of fully autonomous humanoid robot soccer players shall win a soccer game, complying with the official rules of FIFA, against the winner of the most recent World Cup.

Do you think RoboCup’s objective is attainable? Will robots ever prevail over human tiki-taka? Drop us a line at ras@ieee.org.

]]> #RoboCup2026 social media round-up https://robohub.org/robocup2026-social-media-round-up/ Wed, 08 Jul 2026 06:56:38 +0000 https://aihub.org/?p=20640

This year, RoboCup took place in Incheon, South Korea, from 2-6 July. The event saw teams take part in competitions, training sessions, and a symposium. Take a look at what the participants got up to in our round up from social media.

Little Booster K1, trying to get up after falling mid football game!
#robocup2026

[image or embed]

— Raghav Arora (@ra-aurora.bsky.social) 6 July 2026 at 05:05

Straight from RoboCup 2026, at Incheon, Korea.
We added an external lidar, and a literal backpack to the booster T1 to achieve accurate indoor localisation and navigation.
@texasrobotics.bsky.social #Robocup2026

[image or embed]

— Raghav Arora (@ra-aurora.bsky.social) 6 July 2026 at 05:01

]]> #RoboCup2026 – humanoid league knockout stages https://robohub.org/robocup2026-humanoid-league-knockout-stages/ Mon, 06 Jul 2026 10:15:49 +0000 https://aihub.org/?p=20629

This weekend saw the finale of the league competitions at RoboCup 2026 in Incheon, South Korea, with the winners in the small, middle, and large humanoid divisions decided. Congratulations to the following teams, who finished in the top three positions in each size class:

Small division

  1. Invic, Wuhan University, China
  2. Hamburg Bit-Bots, Universität Hamburg, Germany
  3. GeoHBots, School of Artificial Intelligence, China University of Geosciences, China

Middle division

  1. B-Human, Universität Bremen and German Research Center for Artificial Intelligence (DFKI), Germany
  2. HTWK Robots, Leipzig University of Applied Sciences, Germany
  3. Rhoban, University of Bordeaux, France

Large division

  1. Tsinghua Hephaestus, Tsinghua University, China
  2. CAU Mountain&Sea, China Agricultural University, China
  3. Water, Beijing Information Science & Technology University, China

You can watch the action from one of the semi-finals in the middle division, which saw HTWK take on Rhoban.

In the final of the middle division, HTWK took on B-Human:

In addition to the main competitions, there were five league-wide awards:

  • Best Customized Humanoid Award: HERoEHS (ALICE 4th version)
  • Best Humanoid Software Award: B-Human (Game Controller)
  • Open Research Challenge: Ruhrbot Devils (AI Camera Platform for Embedded 2D/3D Game Analysis in RoboCup HSL)
  • Best Innovation Award: Bahia Robotics Team
  • Best Referee Award: Anastasia Prisacaru (Berlin United)

Hear from Team Hephaestus of Tsinghua University, who won the large division:

Although the competitions have drawn to a close, RoboCup 2026 continues today with a symposium, which brings together researchers and practitioners from around the world to present and discuss innovative research in robotics and artificial intelligence. You can find out more here.

]]> #RoboCup2026 – humanoid league day 2 https://robohub.org/robocup2026-humanoid-league-day-2/ Fri, 03 Jul 2026 18:36:55 +0000 https://aihub.org/?p=20591

The second day’s play at RoboCup 2026 has drawn to a close with another bumper set of matches. Teams have come from far and wide to take part in the humanoid soccer competition this year, with 17 different countries represented. China is the most represented country, boasting 15 teams across the three divisions. Other countries taking part are geographically widespread, ranging from Colombia to Malaysia, from Germany to Australia.

In advance of the competition, all applying teams provided a video, team description paper, and information about the robots and software that they use. You can see the complete set of these here. As a taster, here is the qualification video from team CAU Mountain&Sea (from China Agricultural University) who are currently leading the small division competition, and are in fourth place in the large division.

Yesterday’s play saw the first issuing of a red card. The robot received two yellow cards for unsafe challenges and was removed from play on safety grounds. You can see a clip of the second foul in question here.

In terms of the vital statistics of the robots, the heaviest and tallest robot is HERoEHS’s ALICE 4 robot, weighing in at 48kg and measuring 160cm tall. There is a big jump down in weight to the second heaviest bot, which is team BigHeroX’s Z4 bipedal humanoid, at 37kg. A number of teams are using the 35kg Unitree G1. At the other end of the spectrum, the lightest and shortest robot is ITAndroids’ Chape, at just 3.8kg and 53cm tall.

With the seeding rounds almost complete (the knockout rounds start tomorrow) the competitions are hotting up nicely. After four competed rounds in the small division, CAU Mountain&Sea is the only team to win all four matches, and sits atop the table with 12 points, and impressively only conceding one goal. Hamburg Bit-Bots and GeoHBots are in second and third place respectively, having both won three games and lost one.

The middle division has fast established itself as one of the most compelling competitions. B-Human has taken a commanding lead, winning all four matches so far with a +35 goal difference. Behind them, there are four teams on nine points: HTWK Robots, Rhoban, whIRLwind Amsterdam, and THMOS.

Over in the large division, teams have completed three rounds of seeding, with just one further round to come tomorrow. At this stage Tsinghua Hephaestus is the only perfect team, sitting on nine points. Behind them, two teams have each won two matches and drawn the third: PCMS-HRG and Robo-Erectus.

Once the seeding rounds have been completed, 12 teams from each division will make it forward to the knockout stages, with the quarter finals taking place in the evening.

Many thanks to JT Genter for providing the photos, videos, and facts for this article.

]]> #RoboCup2026 – humanoid league day 1 https://robohub.org/robocup2026-humanoid-league-day-1/ Thu, 02 Jul 2026 14:14:34 +0000 https://aihub.org/?p=20573 Image credit: RoboCup Federation.

RoboCup 2026 kicked off today in Incheon, South Korea, with the league competitions running until 5 July. It’s an exciting time for RoboCup, as there have been some updates to the leagues and competition format. Most prominently, the soccer leagues will have a primary focus on humanoid robots. In a series of daily updates, we’ll be bringing you the latest results, videos, and news from the humanoid soccer league.

This year, the humanoid league is split into three sizes: large division, middle division, and small division. There are 18 teams participating the small division, 16 in the middle, and an impressive 22 competing in the large division.

The first two days of competition are be devoted to the seeding round. Teams play using the Swiss-system of ranking to decide who gets through to the knockout stages.

Livestreams from the different fields of play can be found here.

At the end of the first day of competition, teams in the small and middle divisions have played two games each, with the large division ending the day part way through the second round. Early leaders in the small division, with the full six points from two games are: GeoHBots, CAU Mountain&Sea, and Hamburg Bit-Bots. There are also three teams in the middle division who have claimed the six point haul: B-Human, RoboRoos, and HTWK Robots.

You can watch a short summary of the first day, including some of the robots in action, from KBS News:

This short video from RoboCup gives a flavour of the day’s happenings, which also featured the opening ceremony.

We will be back tomorrow with further updates on competition results, some highlights from the day, and insights into how the event is progressing.

Useful links

]]> What’s coming up at #RoboCup2026? https://robohub.org/whats-coming-up-at-robocup2026/ Mon, 29 Jun 2026 08:03:25 +0000 https://robohub.org/?p=220072

This year, RoboCup will be held in Incheon, South Korea, from 2-6 July. The event will see teams take part in competitions, training sessions, and a symposium.

It’s an exciting time for RoboCup, as there have been some updates to the leagues and competition format. Most prominently, the soccer leagues will have a primary focus on humanoid robots.

The leagues and their competitions

You can find out more about the different leagues and the competition schedules and details at these links:

WEROB

A workshop focused on sharing projects, experiences, and innovations in educational robotics. This session is geared towards students, mentors, and educators. Find out more here.

Symposium

The RoboCup symposium will take place on 6 July. More information can be found here.

There will be two keynote talks:

  • Hyun Myung, Spatial Intelligence for Autonomous Robot Navigation in the Wild
  • Gentiane Venture, From function to meaning: Making robots that understand and belong

Find out more at the event website.

]]> A history of RoboCup with Manuela Veloso https://robohub.org/a-history-of-robocup-with-manuela-veloso/ Tue, 24 Mar 2026 12:43:44 +0000 https://aihub.org/?p=19758

RoboCup is an international competition that promotes and advances robotics and AI through the challenges presented by its various leagues. We got the chance to sit down with Professor Manuela Veloso, one of RoboCup’s founders, to find out more about how it all started, how the community has grown over the years, and the vision for the future.

I think it would be very interesting to go right back to the beginning and hear how RoboCup got started. What was the initial idea, and how did it get set up?

So we are talking about the mid-90s. In terms of the research in those days, it was the beginning of the internet and many AI and computer science researchers were focused on the internet, first on sophisticated search algorithms, on natural language understanding, on information retrieval, and then on software agents and machine learning applied to digital information. From what I recall, there was a smaller group of researchers who were interested in actual, physical robots, and in particular in AI and robotics. I myself was specifically interested in the problem of creating autonomous robots with perception (get information from the world), cognition (select action to achieve goals), and then act (execute the planned actions). This combination of perception, cognition, and action is a very good framework for autonomous robots, because they have to get their information from their sensors, they have to reason about actions to achieve their goals, and then execute them. So, during the 90s, I was at Carnegie Mellon with this AI research goal of integrating perception, cognition, and action in autonomous robots.

Over in Canada there was Alan Mackworth, who jointly with his wonderful student, Michael Sahota, built a one-on-one little autonomous robot soccer set-up. Two robots ran on a small field that had a camera overhead, and aimed at scoring each in one of the two little goals. This work showed that this task of kicking a ball and defending and aiming at a goal could be done autonomously. So it was a tremendous demonstration that a robot soccer world could exist. Mostly at the same time, in Japan, Minoru Asada was showing that a big robot could learn with reinforcement learning how to push a ball into a goal. So you have these one-on-one, fully autonomous little robot cars that were pushing balls around in Canada, and then there was this effort of learning to score with a larger robot in Japan. The learning robot didn’t have a team, it was not a real game, but it was showing that reinforcement learning could learn the skill of aiming into a goal. And then there was also Hiroaki Kitano at Sony who was very interested in little humanoids.

So this is very beautiful because all these things came into play – all of us had different interests.

Alan Mackworth did not get involved with RoboCup, but he gave a demonstration of these one-on-one robots at AAAI in 1994. And in those days, I had a PhD student who had just joined – Peter Stone. And Peter was a serious and passionate soccer player. He saw this little game and he came to me and said, “this is what I want to do for my thesis research, robot soccer!” And for me, I was trying to find a research environment where autonomy was needed in the robot world. I had already a student, Karen Haigh, who was working with autonomous office robots, and learning to plan and execute. But with these soccer robots and Peter Stone’s interest everything came to play, and we started robot soccer research in my lab.

In 1996, there was also a robot soccer effort in South Korea, called MIROSOT, and that’s the first competition we participated in. So Peter Stone, myself, and the team we built at Carnegie Mellon – Sorin Achim and Kwun Han – went to South Korea to participate. From South Korea, we flew to a robot soccer workshop in Japan organized by Minoru Asada and Hiroaki Kitano. Also in attendance were Dominique Duhaut, Itsuki Noda, Silvia Coradeschi, and Enrico Pagello. And that’s where RoboCup really started – we decided to do a competition. And the good thing was that Kitano was the chair of IJCAI, which was going to happen in Osaka in the summer of 1997. So we are there in Osaka and literally we came up with this idea of having a robot soccer competition, RoboCup. It was a big moment for us as researchers. We had to come up with the rules of this competition so that people would be able to participate seven months later. We came up with the three leagues that we were interested in and had expertise in.

The small size league, building upon our Carnegie Mellon interests, would have a field with a camera overhead connected to a computer and then the computer would remotely control the robots through radio.

Then Minoru Asada had these bigger robots with wheels, and we created a league that we call the middle-size league to include the robotics research of Minoru Asada and others. And then Itsuki Noda was interested in creating a simulation environment. We thought that this would help get more people participating in this task of robot soccer.

So that’s how the three leagues started: the small size, the middle size, and the simulation. Hiroaki Kitano, Dominique Duhaut and I were in charge of the small-size league, Minoru Asada was in charge of the middle-size league, and Itsuki Noda ran the simulation league.

One of the challenges was to come up with the rules and define the robots and playing fields. I remember my own pragmatism in suggesting that we play on a ping-pong table for the small-size, as a ping-pong table is something that exists in the whole world. That meant that we would have a playing surface, with defined size and texture, anywhere in the world. We decided one ping-pong table for the small-size league, and nine ping-pong tables for the middle-size league.

In the summer of 1997, at IJCAI, when we all went to the actual first RoboCup competition, the space was gorgeous. Hiroaki Kitano had made these beautiful fields and white bleachers around the fields. It was a very beautiful space with an area with computers for the simulation league. There were 80 teams that had joined the simulation online. We were, I believe, five teams for the small size league and about eight to ten for the middle size league.

That’s how it all started. And 1997 it was in Osaka, in 1998 Dominique Duhaut organized RoboCup in Paris, at a modern Science Center, La Villette. And then in 1999, RoboCup was organized by Silvia Coradeschi, in Stockholm, again co-located with the IJCAI conference. In these three years 1997, ’98, and ’99, there were only these three leagues, small size, middle size, and simulation. It was the foundation of everything. RoboCup grew consistently every year in terms of the number of teams, the number of participants, and the number of participating countries.

Some of the pitches in the main soccer arena at RoboCup 2024, held in Eindhoven, The Netherlands.

So how did RoboCup expand and could you talk about the decisions to add extra leagues?

Well, nothing was necessarily planned, it was more related to the research interests that people had. So when we were in Melbourne in 2000, there were two things that were added. One was RoboCupJunior. There was a professor, Elizabeth Sklar, who had a tremendous interest in robotics education for children. She proposed the RoboCupJunior competitions for children K-12. The goal of RoboCupJunior was to train all these young people to do research in robotics. It was and it still is extremely successful. In Melbourne there were probably the same number of children as there were people competing in what we now call the major leagues. As well as the soccer leagues, Elizabeth also ran a dance competition. This was very impressive – the children would dance on stage with their designed, built, and programmed mobile robots.

I believe that the rescue league was also introduced in 2000. The reason why we came up with rescue was because in those days, there was a lot of research on robots in disaster environments. So many people in our groups had an interest in developing robots that were able to handle disaster environments. So, we included that interest. And later on, @home was also introduced because people had an interest in service robots.

The logistics league came later to cover robots inside factories. The reason for introducing new leagues was always to include the community. And we didn’t want anyone to feel they could not come to RoboCup because they did different research with their robots. And so it was a very intellectually inclusive environment for AI and robotics researchers that could perform tasks autonomously – with perception, cognition, and action mostly in teams for particular tasks.

Rescue league arena at RoboCup 2024.

Could you talk about the changes that were announced last year, and the decision behind those?

Back in 1997, Hiroaki Kitano came up with this goal of a robot soccer team being able to beat the human World Cup winners by 2025. (I later tried, in the early 2000s, to rephrase that goal so that it was not about the robots beating the humans, but instead having robots playing alongside human players by 2050.) However, for this to happen, we have started the humanoid league so that the robots would have legs, not wheels. Our roadmap hence moves towards humanoid robots. It also happens that currently there are several robot companies who are producing humanoid robots that are easy to acquire, so researchers do not have the design and build their own humanoid robots, which was a difficult task that was pursued only by a limited group of researchers. In parallel, events started aiming at including humanoid robot soccer. We believed that RoboCup should be visible and a reference for humanoid robot soccer. We then proposed and changed that, in the international RoboCup competitions, robot soccer will focus solely on humanoid robots. That doesn’t mean that the other RoboCup leagues aren’t valuable. But if they continue at a regional level, there will still be venues to foster these other types of interests. The RoboCup international competitions would be solely focused on humanoid robot soccer.

Action from the humanoid soccer league at RoboCup 2025. Image courtesy of Alessandra Rossi.

RoboCup was tremendously successful in terms of including a variety of different autonomous robot research interests from the community. But I also believe that now we should move into just humanoid soccer at the international level. This will help with visibility of the competition and also for consolidation. We can now buy humanoid robots that we didn’t have before. Previously, you had to build them in your universities, and that was not a research direction for many people. But there is not that excuse anymore. You can buy humanoid robots, and increasingly, you can talk with these humanoid robots using GenAI. So it became a different challenge, a much more accessible challenge than before. The challenge will be: can these platforms play soccer in the presence of another team? I think it’s a fascinating new direction to try to focus on this challenge of the game of soccer – multiple players, two teams in a large space, eventually coordinating, collaborating, or playing with and against humans. So that’s the rationale.

When people don’t know the story, they tend to wonder why we have all these other leagues, such as rescue and logistics. It was always driven by the interests of the community and a big heart for a research approach. And we always thought that because we were organizing an event with a venue, that we could include these people that have interests outside of soccer. That has always been our drive. However, it’s true that it diluted a little bit the goal of 2050 – the humanoid soccer robots. Now I think it’s time to go back to soccer as a main focus, and still keep supporting the other interests that we have fostered, more at a regional level.

There are still a lot of details to be sorted out. And, of course, people may be upset by leagues terminating at the international level. I have always participated in the small-size league myself, 20 years of participating, and I am very attached to it. I was always the trustee representing the small-size league, and I am not sure I am happy with the small-size not being in the international event. But I need to think beyond what my personal interests are and try to understand what would have a bigger impact for AI, robotics, and RoboCup. I think that that’s demonstrating a group of humanoids playing soccer. And if we don’t do it at RoboCup, maybe someone else will.

The way I think about it is that we have made this decision and we can reevaluate it in five or ten years. But not making a change and never having the courage to deviate from what we have been pursuing is less exciting. Things have stalled a bit, with the same number of teams, the same people, the same rules, the same type of intellectual and research accomplishments. We need to excite people about something major again. So I think that the community will greatly embrace this new RoboCup international. And we are so well organized locally, that we can support other leagues at the local events.

I do think that, from a scientific and research point of view, it’s the right moment to target the soccer humanoid robots, because of their availability and our RoboCup ultimate goal. I think it would be amazing if people knew RoboCup international as the humanoid robot soccer competition. Can you imagine in 2027 having 100 teams all playing soccer with humanoids? We’ll see.

About Manuela Veloso

  • Herbert A. Simon University Professor Emerita, School of Computer Science, Carnegie Mellon University
  • 2018-2026 Founder and Head, JPMorganChase AI Research, Managing Director

For the last eight years, Manuela Veloso has been the founder and Head of JPMorganChase AI Research and Herbert A. Simon University Professor Emerita at Carnegie Mellon University, where she was faculty in the Computer Science Department and then Head of the Machine Learning Department.

At JPMorganChase, she built a team of 100 top talented members with graduate education (PhD and Masters) in AI and related disciplines. The team focused on pillar areas of AI in finance, including data-driven optimization, planning and search, document analysis, trustworthy AI, AI and mathematical reasoning, continual learning, and multiagent systems. The team published their research in academic venues and addressed and contributed to business needs and vision.

Veloso has a licenciatura degree in Electrical Engineering and an M.Sc. in Electrical and Computer Engineering from Instituto Superior Técnico, Lisbon, an M.A. in Computer Science from Boston University, and a Ph.D. in Computer Science from Carnegie Mellon University. Veloso has Doctorate Honoris Causa degrees from the Örebro University, Sweden, the Instituto Universitário de Lisboa (ISCTE), Portugal, the Université de Bordeaux, France, and the Universidade Católica of Portugal.

She served as president of the Association for the Advancement of Artificial Intelligence (AAAI), and she is co-founder and a Past President of the RoboCup Federation. She is a fellow of main professional organizations in her area, namely AAAI, IEEE, AAAS, and ACM. She is the recipient of the ACM/SIGART Autonomous Agents Research Award, the Einstein Chair of the Chinese Academy of Sciences, an NSF Career Award, and the Allen Newell Medal for Excellence in Research. Veloso is a member of the National Academy of Engineering with a citation “for contributions to artificial intelligence and its applications in robotics and the financial services industry.” She is also a member of the Academy of Sciences of Portugal.

Her research interests are in AI, including Multiagent Systems, Autonomous Robots, Continual Learning Agents, and AI in Finance. For further details, see Manuela’s webpage.

]]> Taking humanoid soccer to the next level: An interview with RoboCup trustee Alessandra Rossi https://robohub.org/taking-humanoid-soccer-to-the-next-level-an-interview-with-robocup-trustee-alessandra-rossi/ Wed, 14 Jan 2026 09:07:58 +0000 https://aihub.org/?p=19175

A core objective of RoboCup is to promote and advance robotics and AI research through the challenges offered by its various leagues. The ultimate goal of the soccer competition is that, by 2050, a team of fully autonomous humanoid robots will defeat the most recent winner of the FIFA World Cup. To bring this vision closer to reality, the RoboCup Federation has announced several changes to the leagues. We spoke with Alessandra Rossi, a trustee who has been involved in the humanoid soccer league for many years, to learn more.

Could you start by introducing yourself and tell us how you’ve been involved in RoboCup throughout the years, because you’ve been involved in so many aspects of the competition!

I am Alessandra Rossi from the University of Naples “Federico II”, where I am an Assistant Professor of Computer Science. I began working with and collaborating in RoboCup in 2016, when I started my PhD at the University of Hertfordshire in the UK. I am still affiliated with the University of Hertfordshire, as I remain a member of the humanoid KidSize team Bold Hearts, the longest continuously active team in the UK. After a few years, I became the team leader of Bold Hearts.

In 2019, I became a member of both the Technical Committee and the Organizing Committee of the Humanoid League. After serving on the Technical Committee for two years, I was elected to the Executive Committee of the Humanoid League. In 2025, I was elected to the Board of Trustees for the first time.

Over the years, I have steadily increased my involvement and commitment to RoboCup. I have always sought to actively engage the RoboCup community, both during competitions and outside of competition periods. I also work to encourage engagement between the major and junior leagues and to participate in regional RoboCup events.

While working at the University of Hertfordshire as a Visiting Lecturer, we launched an online module that uses RoboCup as a benchmark for teaching robotics to undergraduate students. The module is still running. I initially served as the module leader, and this role has since been taken over by our Bold Hearts teammate, Bente Riegler.

Last year, Maike Paetzel-Prüsmann, Merel Keijsers, and I (as lead authors), in collaboration with several trustees and many members from different leagues, published a paper on the current and future challenges in humanoid robotics. The paper was published in Autonomous Robots and is, to the best of my knowledge, the first to involve such a large and diverse group of contributors from across the RoboCup leagues. It discusses research within RoboCup and the collaboration and synergies between the leagues.

Group photo of the humanoid league teams at RoboCup 2025.

I understand that there are some changes planned for the leagues. Could say something about that, and specifically the changes that affect the soccer and the humanoid side.

The 2050 goal of the RoboCup Federation, as many people are probably aware, is for a team of humanoid robots to play against the winners of the FIFA World Cup. To achieve this, it is necessary to push further in that direction. One of the key changes, therefore, will be a stronger focus on humanoid robots.

Another major change will be the merger of the Standard Platform League (SPL) and the KidSize Humanoid League. This merged league will have the freedom to define its exact format and to develop a new roadmap that aligns the entire league toward a shared objective. While the 2050 goal itself remains unchanged, the path toward achieving it will need to be adjusted.

It is crucial to continue fostering the engagement of teams in the leagues that will be affected by these changes. At the same time, we must recognize that technology is advancing rapidly. Over the past year, in particular, we have seen significant progress in both hardware platforms and large language models. As RoboCup serves as a global benchmark for robotics research, we should continuously strive to advance technology and research—while still having fun.

Soccer is the complex task and behavior we are studying, and it is complex in many dimensions: from physical control and robot motion, to communication and strategy, and even human-like interactions. These include responding to the referee’s whistle, verbal and non-verbal communication among team members, interactions with the coach, and communication with the referee. All of these aspects will ultimately need to be incorporated into the humanoid league.

The RoboCup Federation has agreed some new partnerships with Unitree, Fourier and Booster. What impact will this have on the humanoid league? Will there be a standard platform element with teams using a specific humanoid robot?

I believe we will see a mix of different robots. With the three companies currently sponsoring RoboCup, we have already seen that their robots can achieve a wide range of behaviors, and there have been significant improvements in robot control. Some of these robots can walk very quickly—almost to the point of running.

Initially, there may be the possibility of multiple teams using the same platform. However, we must keep in mind that both hardware and software can become obsolete very quickly, so we need to remain open to multiple options. A robot that is state of the art today may no longer be so in a year or two. As a result, committing to a single standard platform could limit future progress.

For this reason, the current idea is to remain open to multiple platforms. Many teams already have excellent custom-built robots, and further improvements to these platforms should be encouraged. That said, the exact structure has not yet been decided, and these decisions will be made in consultation with the teams. It is important to give the RoboCup community the time it needs to adapt and move forward.

There have been some big advances in the humanoid adult-size league in the past couple of years. What improvements stood out to you at RoboCup2025 in Brazil?

One major change is that we have added extra robots to each team. Previously, teams played with just two robots per side, but matches are now played three versus three.

Another important improvement is the reduced presence of humans on the field. There is no longer a handler assigned to each robot. In the past, a team member had to walk behind the robots in case they fell and risked being damaged.

I have actually played in a match against the winning humanoid team. Naturally, the human team won, but it was an enjoyable and very interesting game, as the robots were surprisingly fast.


Action from the human vs humanoid match at RoboCup 2025.


Further action from the human vs humanoid match at RoboCup 2025.

What has been the general reaction from the RoboCup community to the changes? I guess it depends on which league you’re in as to how much it affects you.

Yes, it depends on which league you are part of. The reactions have been a mix of excitement and passion. Of course, everyone is keen to see improvements, and participants have always been prepared for changes to the rules and the league structures. However, there are still some open questions, and teams are waiting to see how things will evolve. Tomorrow, there will be a meeting with the President and several trustees to address questions raised by the leagues.

The overall direction of RoboCup, guided by the 2050 goal has not changed. Each league has been extremely valuable and has contributed in different ways toward achieving that goal. RoboCup has also been immensely valuable for robotics research more broadly. Beyond being fun, the challenges involved in making robots play soccer are extraordinarily complex. The research and solutions developed within RoboCup can be applied to many other fields and applications.

About Alessandra Rossi

Alessandra is Assistant Professor at the University of Naples Federico II, Italy. Her PhD thesis was part of the Marie Sklodowska-Curie Research ETN SECURE project at the University of Hertfordshire (UK). Her research interests include Human–(Multi) Robot Interaction, social robotics, trust, XAI, multi-agent systems and user profiling. She is Project Manager and co-supervisor of the MSCA PERSEO (955778), TRAIL (101072488) and SWEET (101168792). She is also co-PI of the project ERROR (FA8655-23-1-7060), and part of several national and international projects. Alessandra is also trustee member of RoboCup Federation, and member of the Humanoid League team called Bold Hearts. She is Chair of the IEEE P3108™ “Study Design”, and a member of the “Appendix” groups, she is Program Chair of IEEE RO-MAN 2027, she has been Robotic Challenge Chair at ICSR 2025, Special Session Chair of IEEE RO-MAN 2024, Publicity Chair of IEEE RO-MAN 2022 and 2023, Organising Chair of the 26th RoboCup International Symposium 2023, and she is on the program committee of several international conferences on human–robot interaction and artificial intelligence.

]]> Robohub highlights 2025 https://robohub.org/robohub-highlights-2025/ Mon, 29 Dec 2025 11:19:26 +0000 https://robohub.org/?p=218611

Over the course of the year, we’ve had the pleasure of working with many talented researchers from across the globe. As 2025 draws to a close, we take a look back at some of the excellent blog posts, interviews and podcasts from our contributors.

Teaching robot policies without new demonstrations: interview with Jiahui Zhang and Jesse Zhang

Jiahui Zhang and Jesse Zhang to tell us about their framework for learning robot manipulation tasks solely from language instructions without per-task demonstrations.

CoRL2025 – RobustDexGrasp: dexterous robot hand grasping of nearly any object

Hui Zhang writes about work presented at CoRL2025 on RobustDexGrasp, a novel framework that tackles different grasping challenges with targeted solutions.

Robot Talk Episode 133 – Creating sociable robot collaborators, with Heather Knight

Robot Talk host Claire Asher chatted to Heather Knight from Oregon State University about applying methods from the performing arts to robotics.

Generations in Dialogue: Human-robot interactions and social robotics with Professor Marynel Vasquez

In this podcast from AAAI, host Ella Lan asked Professor Marynel Vázquez about what inspired her research direction, how her perspective on human-robot interactions has changed over time, robots navigating the social world, and more.

Learning robust controllers that work across many partially observable environments

In this blog post, Maris Galesloot summarizes work presented at IJCAI 2025, which explores designing controllers that perform reliably even when the environment may not be precisely known.

Robot Talk Episode 130 – Robots learning from humans, with Chad Jenkins

Claire Asher chatted to Chad Jenkins from University of Michigan about how robots can learn from people and assist us in our daily lives.

Interview with Zahra Ghorrati: developing frameworks for human activity recognition using wearable sensors

Zahra Ghorrati is pursuing her PhD at Purdue University, where her dissertation focuses on developing scalable and adaptive deep learning frameworks for human activity recognition (HAR) using wearable sensors.

Self-supervised learning for soccer ball detection and beyond: interview with winners of the RoboCup 2025 best paper award

We caught up with some of the authors of the RoboCup 2025 best paper award to find out more about the work, how their method can be transferred to applications beyond RoboCup, and their future plans for the competition.

#IJCAI2025 distinguished paper: Combining MORL with restraining bolts to learn normative behaviour

Agata Ciabattoni and Emery Neufeld introduce a framework for guiding reinforcement learning agents to comply with social, legal, and ethical norms.

Robot Talk Episode 114 – Reducing waste with robotics, with Josie Gotz

Claire Asher chatted to Josie Gotz from the Manufacturing Technology Centre about robotics for material recovery, reuse and recycling.

Multi-agent path finding in continuous environments

Kristýna Janovská and Pavel Surynek write about how can a group of agents minimise their journey length whilst avoiding collisions.

RoboCupRescue: an interview with Adam Jacoff

Find out what’s new in the RoboCupRescue League this year.

An interview with Nicolai Ommer: the RoboCup Soccer Small Size League

We caught up with Nicolai to find out more about the Small Size League, how the auto referees work, and how teams use AI.

Interview with Kate Candon: Leveraging explicit and implicit feedback in human-robot interactions

Hear from PhD student Kate about her work on human-robot interactions.

AIhub coffee corner: Agentic AI

The AIhub coffee corner captures the musings of AI experts over a short conversation.

Generations in Dialogue: Multi-agent systems and human-AI interaction with Professor Manuela Veloso

Host Ella Lan chats to Professor Manuela Veloso about her research journey and path into AI, the history and evolution of AI research, inter-generational collaborations, and more.

Preparing for kick-off at RoboCup2025: an interview with General Chair Marco Simões

We spoke to Marco Simões, one of the General Chairs of RoboCup 2025 and President of RoboCup Brazil.

Gearing up for RoboCupJunior: Interview with Ana Patrícia Magalhães

RoboCup Junior Rescue @ WK RoboCup 2024. Photo: RoboCup/Bart van Overbeeke


We heard from the organiser of RoboCupJunior 2025 and find out more about the event.

]]> RoboCup Logistics League: an interview with Alexander Ferrein, Till Hofmann and Wataru Uemura https://robohub.org/robocup-logistics-league-an-interview-with-alexander-ferrein-till-hofmann-and-wataru-uemura/ Thu, 25 Sep 2025 11:34:59 +0000 https://aihub.org/?p=18370 The Industrial League arena at RoboCup2025.

RoboCup is an international scientific initiative with the goal of advancing the state of the art of intelligent robots, AI and automation. The annual RoboCup event took place from 15-21 July in Salvador, Brazil. The Logistics League forms part of the Industrial League and is an application-driven league inspired by the industrial scenario of a smart factory. Ahead of the Brazil meeting, we spoke with three key members of the league to find out more. Alexander Ferrein is a RoboCup Trustee overseeing the Industrial League, and Till Hofmann and Wataru Uemura are Logistics League Executive Committee members.

Could you start by giving us an introduction to the Logistics League?

Alexander Ferrein: The idea of the Logistics League is to have robots helping in intra-production logistics. The playing field is set up with different machines and the robots need to bring raw materials and products to the machines and pick products up from them. There are orders coming in for different products of different complexities. The idea is that the robots deliver these products after they have been machined, at a certain handover point, and then the team will be awarded points. The setup is that we have six machines per team, and three robots operating in the smart factory.

There are two teams competing at the same time on different sides of the field. Most of the machines are on the home side of the field, but some machines are also on the opponent’s side of the field. The teams need to show basic robotics skills like navigation skills and collision avoidance. For the production of products we have little discs that have different colours and can be stacked on top of each other, and they have different caps. We have around 550 different items that can be produced. We don’t focus so much on the handling of the parts, therefore the manipulating mechanisms are quite simple and are usually custom built by the teams. They just need to grab these discs and drive them around and put them on conveyor belts on the machines.

The main focus is on the production logistics and the planning phase. The complex products need to be produced or machined by a team of robots – without this planning and team work it wouldn’t be possible to deliver the products within the allotted time in the competition.

Until now, we had been supported by Festo didactics, who supplied the machines. However, they pulled out in February and told us that they won’t support us in bringing the machines to Brazil. Our team in Aachen has a complete field set up, so we are in the (not so easy) process of packing the machines up in pallets and shipping them to Brazil.

Till Hofmann: One important detail is that all the products that need to be manufactured, the orders for those come online, and the number of possible products is very high. Therefore, you can’t do any planning in advance – you can’t just create a big database that contains one sequence that you execute for every possible product, that doesn’t really work. So the robots need to do online planning. Due to the fact that we have multiple robots in the team and then also the opponents teams’ robots on the field, many things go differently than planned, so a very big aspect of the competition is execution monitoring and online replanning. Basically you create an initial plan, but you need to constantly adapt that plan to what actually happens during execution. I just want to stress that in contrast to other RoboCup competitions, it’s really a long-horizon planning task in the sense that we usually need to do actions on a time horizon of five to ten minutes to actually get to an intermediate goal of producing one of those products.

Wataru Uemura: The focus of our league is on how to handle the production line. The three mobile robots are a very important part. At first these were autonomous guided robots, but now they are completely autonomous mobile robots. The robots need to decide on their path to make the product.

Could you talk about some of the challenges the teams face in the league, and is there a challenge or aspect of the competition that the teams have found particularly difficult?

Alexander: First of all, one needs to say that it is a really tough problem that we are facing here. So the teams that are starting new, they have to deal with all of the robotic aspects, so mobile robots, autonomous intelligence systems, they need to drive around, map, interact with the machines. As these items that we are pushing around are non-standard things, they also need to build their own manipulating devices. And then there is this massive planning aspect of the league, which is also not so easy. When we started this in 2011, 2012, we were thinking that this must be a solved problem, that one could use scheduling systems, that production is digital, and that everything would be easy. However, we found out it’s not actually easy, and there are no off-the-shelf solutions for a fleet of robots doing planning and production.

Looking at our team (which has become particularly successful over the years) and from observing the other teams as a trustee, I think that the integration aspect of all the different tasks is really a hard thing. Having a software system that is capable of so many things, communicating with the centralized referee box, and making it run within the time limit of a match, is the major challenge. Today, navigation of a robot is not the big issue, basically, but getting it integrated into your software system and building all the rest around the planning components and so on, this is, from my point of view, the major challenge.

Till: As I mentioned, I think the combination of long-horizon planning and execution monitoring is particularly difficult. We do have a lot of failures during one production run, because of hardware limitations and problems with the robots. Sometimes the machines themselves fail and they need to deal with this, without being able to solve the problem itself, because it’s not in their control. So they need to do a lot of reasoning that considers all the different cases that may happen. For example, suddenly you have a product appearing in a machine and you no longer know the configuration because this information was lost on the way. How do you deal with this? Another example is that the robot drops a piece while it is driving around and then tries to feed it into the machine. Then the machine reports a failure and the team needs to keep its world model up-to-date to know that this product is no longer where they thought it was. How do we deal with this?


Will there be any new challenges introduced for RoboCup 2025?

Alexander: Because the problem we are trying to solve is so hard, we don’t have many teams in the league. We had a new team at RoboCup2023, in Bordeaux, and they are now consistently coming to the RoboCups, which is nice. Apart from this, we have a core of three to five teams that participate in the league. As the challenge itself is hard to get into, the skills only develop slowly, so new challenges are not really introduced. There are slight changes here and there. One of the major changes in recent years was during Coronavirus times where we had to abandon the whole match aspect because we couldn’t play with two teams at the same time. We introduced aspects of the game as challenges. Now we have a challenge track as well, where teams can just focus on certain aspects of the league and do not need to play the full game.

The skills of the teams are not developing in such a way that we really need to add new challenges because it is still unsolved and hard for all the teams to get the robots running, because we have so many different aspects.

I understand that you are thinking about some changes to the league. Could you say more about this?

Alexander: Yes, we have some ideas for a new Industrial League. As Festo are pulling out we don’t see the need to stick with this particular machine type. We had a workshop earlier this year with all the teams and organising committees and we’re proposing a future challenge, or league. We will discuss this at RoboCup2025.

Till: We actually already had discussions with @work, and agreed that the long-term goal is to merge the two competitions into one big Industrial League. Next year, we will start converging by doing some kind of collaboration challenge or crossover challenge where teams from @work will collaborate with what’s now the Logistics League, but will be the Smart Manufacturing League by that time. Then hopefully, in two or three years, this will be one big league rather than separate industrial competitions. And the idea of the league that we’re currently planning to migrate to is really a broader smart manufacturing scenario where we have different aspects of smart manufacturing. So currently it’s really only the production logistics part. But in the future, we also want to include the assembly itself as part of the competition and also extend this to humanoid robots and also focus on human-robot collaboration in this manufacturing setting.

So this will be very different to the Logistics League as it is right now. How we will do the migration from what we have to that new league without losing all the teams is something that’s still in the making.

Alexander: I also don’t think we shouldn’t restrict ourselves to just one type of robot. As we see, there is something going on with respect to humanoid robots, and the Rescue League is proposing a quadruped robot. In the @Home League there are ideas to introduce the staircases where you need more agility in these settings. So for me, I wouldn’t restrict the type of robots that we’re using. We are just thinking about proposing challenges that are looking towards the future. So far we have been doing things that we thought might be relevant to industry, but industry is not very interested in what we are doing here. At least, they are not knocking at our doors and asking what our solutions are. That’s also another aspect that we will possibly discuss at Robocup 2025 – how we could increase our impact as a league for the outside world.

So that’s one of the aims, I guess, to evolve in a way such that industry will be more interested?

Alexander: Yes, be relevant, right? I mean, you see so much going on, in particular in China with the robot manufacturing OEMs [original equipment manufacturers] that build these robots in a few years that have quite impressive capabilities. And well, we stand aside and just watch. So maybe we should use those robots, and integrate them into our process. It’s very important that we are opening our minds to envision a future that’s different from today.

About the interviewees

Alexander Ferrein received his MSc in Computer Science (Dipl.-Inform.) and his PhD (Dr. rer. nat) from Aachen University in 2001 and 2007, respectively. Between 2009-2011 he joined the Robotics and Agents Research Lab, University of Cape Town, as a postdoctoral research fellow with Feodor-Lynen scholarship granted by the Alexander-von-Humboldt Foundation. He then re-joined the Knowledge-Based Systems Group at Aachen University before he became a professor for Robotics and Computer Science at FH Aachen University of Applied Sciences. He is a heading the Mobile Autonomous Systems & Cognitive Robotics Institute at Aachen Applied Science University. His research focusses on the field of Artificial Intelligence and Cognitive Robotics. Since 2015 he is member of the Advisory Committee of the African-German Network of Excellence in Science whose Vice-president he was between 2019-2023. His research concentrates on the field of cognitive robotics. In particular, he is interested in high-level control and decision making of robots and agents acting under real-time constraints.

Till Hofmann is a Postdoc at RWTH Aachen University. His research focuses on planning, plan execution, generalized planning as well as reactive synthesis, with a particular focus on planning for robotics. He was a participant in the RoboCup Logistics League from 2016 until 2019 and member of the technical committee from 2017 until 2020. Since 2024, he is on the executive committee of the RCLL.

Wataru Uemura was born in 1977, and received B.E, M.E. and D.E. degrees from Osaka City University, in 2000, 2002, and 2005. He is an associate professor in Electronics, Information and Communication Engineering Course, Faculty of Advanced Science and Technology, Ryukoku University in Shiga, Japan. He is a member of IEEE, RoboCup and others. He is a chairperson of the RoboCup Japanese National Committee. He is an executive committee member of RoboCup Logistics League. He was a member of the Industrial Robotics Competition Committee, the World Robot Summit. He was TPC Vice Chairs of GCCE 2012, Conference Chair of GCCE 2016, and Publication Chairs of GCCE (Global Conference on Consumer Electronics). He is a member of the World Skills in Japan organizing committee of Autonomous Mobile Robots.
]]> Self-supervised learning for soccer ball detection and beyond: interview with winners of the RoboCup 2025 best paper award https://robohub.org/self-supervised-learning-for-soccer-ball-detection-and-beyond-interview-with-winners-of-the-robocup-2025-best-paper-award/ Fri, 19 Sep 2025 08:56:35 +0000 https://aihub.org/?p=18466 Presentation of the best paper award at the RoboCup 2025 symposium.

An important aspect of autonomous soccer-playing robots concerns accurate detection of the ball. This is the focus of work by Can Lin, Daniele Affinita, Marco Zimmatore, Daniele Nardi, Domenico Bloisi, and Vincenzo Suriani, which won the best paper award at the recent RoboCup symposium. The symposium takes place alongside the annual RoboCup competition, which this year was held in Salvador, Brazil. We caught up with some of the authors to find out more about the work, how their method can be transferred to applications beyond RoboCup, and their future plans for the competition.

Could you start by giving us a brief description of the problem that you were trying to solve in your paper “Self-supervised Feature Extraction for Enhanced Ball Detection on Soccer Robots”?

Daniele Affinita: The main challenge we faced was that deep learning generally requires a large amount of labeled data. This is not a major problem for common tasks that have already been studied, because you can usually find labeled datasets online. But when the task is highly specific, like in RoboCup, you need to collect and label the data yourself. That means gathering the data and manually annotating it before you can even start applying deep learning. This process is not scalable and demands a significant human effort.

The idea behind our paper was to reduce this human effort. We approached the problem through self-supervised learning, which aims to learn useful representations of the data. After all, deep learning is essentially about learning latent representations from the available data.

Could you tell us a bit more about your self-supervised learning framework and how you went about developing it?

Daniele: First of all, let me introduce what self-supervised learning is. It is a way of learning the structure of the data without having access to labels. This is usually done through what we call pretext tasks. These are tasks that don’t require explicit labels, but instead exploit the structure of the data. For example, in our case we worked with images. You can randomly mask some patches and train the model to predict the missing parts. By doing so, the model is forced to learn meaningful features from the data.

In our paper, we enriched the data by using not only raw images but also external guidance. This came from a larger model which we refer to as the teacher. This model was trained on a different task which is more general than the target task we aimed for. This way the larger model can provide guidance (an external signal) that helps the self-supervision to focus more on the specific task we care about.

In our case, we wanted to predict a tight circle around the ball. To guide this, we used an external pretrained model (YOLO) for object detection, which instead predicts a loose bounding box around the ball. We can arguably say that the bounding box, a rectangle, is more general than a circle. So in this sense, we were trying to use external guidance that doesn’t solve exactly the underlying task.

Overview of the data preparation pipeline.

Were you able to test this model out at RoboCup 2025?

Daniele: Yes, we deployed it at RoboCup 2025 and showed great improvements over our previous benchmark, which was the model we used in 2024. In particular, we noticed that the final training requires much less data. The model was also more robust under different lighting conditions. The issue we had with previous models was that they were tailored for specific situations. But of course, all the venues are different, the lighting and the brightness are different, there might be shadows on the field. So it’s really important to have a reliable model and we really noticed a great improvement this year.

What’s your team name, and could you talk a bit about the competition and how it went?

Daniele: So our team is SPQR. We are from Rome, and we have been competing in RoboCup for a long time.

Domenico Blois: We started in 1998, so we are one of the oldest teams in RoboCup.

Daniele: Yeah, I wasn’t even born then! Our team started with the four-legged robots. And then the league shifted more towards biped robots because they are more challenging, they require balance and, overall it’s harder to walk on just two legs.

Our team has grown a lot during recent years. We have been following a very positive trend, going from 9th place in 2019 to third place at the German Open in 2025, and we got 4th place at RoboCup 2025. Our recent success has attracted more students to the team. So it’s kind of a loop – you win more, you attract more students, and you can work more on the challenges proposed by RoboCup.

SPQR team.

Domenico: I want to add that also, from a research point of view, we have won three best paper awards in the last five years, and we have been proposing some new trends towards, for example, the use of LLMs for coding (as a robot’s behaviour generator under the supervision of a human coach). So we are trying to keep the open research field active in our team. We want to win the matches but we also want to solve the research problems that are bound together with the competition.

One of the important contributions of our paper is towards the use of our algorithms outside RoboCup. For example, we are trying to apply the ball detector in precision farming. We want to use the same approach to detect rounded fruits. This is something that is really important for us; to exit the context of Robocup and to use Robocup tools for new approaches in other fields. So if we lose a match, it’s not a big deal for us. We want our students, our team members, to be open minded towards the use of RoboCup as a starting point for understanding teamwork and for understanding how to deal with strict deadlines. This is something that RoboCup can give us. We try to have a team that is ready for every type of challenge, not only within RoboCup, but also other types of AI applications. Winning is not everything for us. We’d prefer to use our own code and not win, than win using code developed by others. This is not optimal for achieving first place, but we want to teach our students to be prepared for the research that is outside of RoboCup.

You said that you’ve previously won two other best paper awards. What did those papers cover?

Domenico: So the last two best papers were kind of visionary papers. In one paper, we wanted to give an insight in how to use the spectators to help the robots score. For example, if you cheer louder, the robots tend to kick the ball. So this is something that is not actually used in the competition now, but is something more towards the 2050 challenge. So we want to imagine how it will be 10 years from now.

The other paper was called “play everywhere”, so you can, for example, play with different types of ball, you can play outside, you can even play without a specific goal, you can play using Coca-Cola cans as goalposts. So the robot has to have a general approach that is not related to the specific field used in RoboCup. This is in contrast to other teams that are very specific. We have a different approach and this is something that makes it harder for us to win the competition. However, we don’t want to win the competition, we want to achieve this goal of having, in 2050, this match between the RoboCup winners and the FIFA World Cup winners.

I’m interested in what you said about transferring the method for ball detection to farming and other applications. Could you say more about that research?

Vincenzo Suriani: Our lab has been involved in some different projects relating to farming applications. The Flourish project ran from 2015 – 2018. More recently, the CANOPIES project has focussed on precision agriculture for permanent crops where farmworkers can efficiently work together with teams of robots to perform agronomic interventions, like harvesting or pruning.

We have another project that is about detecting and harvesting grapes. There is a huge effort in bringing knowledge back from RoboCup to other projects, and vice versa.

Domenico: Our vision now is to focus on the new generation of humanoid robots. We participated in a new event, the World Humanoid Robot Games, held in Beijing in August 2025, because we want to use the platform of RoboCup for other kinds of applications. The idea is to have a single platform with software that is derived from RoboCup code that can be used for other applications. If you have a humanoid robot that needs to move, you can reuse the same code from RoboCup because you can use the same stabilization, the same vision core, the same framework (more or less), and you can just change some modules and you can have a completely different type of application with the same robot with more or less the same code. We want to go towards this idea of reusing code and having RoboCup as a test bed. It is a very tough test bed, but you can use the results in other fields and in other applications.

Looking specifically at RoboCup, what are your future plans for the team? There are some big changes planned for the RoboCup Leagues, so could you also say how this might affect your plans?

Domenico: We have a very strong team and some of the team members will do a PhD in the coming years. One of our targets was to keep the students inside the university and the research ward, and we were successful in this, because now they are very passionate about the RoboCup competition and about AI in general.

In terms of the changes, there will be a new league within RoboCup that is a merger of the standard platform league (SPL) and the humanoid kid-size league. The humanoid adult-size league will remain, so we need to decide whether to join the new merged league, or move to adult-sized robots. At the moment we don’t have too many details, but what we know is that we will go towards a new era of robots. We acquired robots from Booster and we are now acquiring another G1 robot from Unitree. So we are trying to have a complete family of new robots. And then I think we will go towards the league that is chosen by the other teams in the SPL league. But for now we are trying to organize an event in October in Rome with two other teams to exchange ideas and to understand where we want to go. There will also be a workshop to discuss the research side.

Vincenzo: We are also in discussion about the best size of robot for the competition. We are going to have two different positions, because robots are becoming cheaper and there are teams that are pushing to move more quickly to a bigger platform. On the other hand, there are teams that want to stick with a smaller platform in order to do research on multi agents. We have seen a lot of applications for a single robot but not many applications with a set of robots that are cooperating. And this has been historically one of the core parts of research we did in RoboCup, and also outside of RoboCup.

There are plenty of points of view on which robot size to use, because there are several factors, and we don’t know how fast the world will change in two or three years. We are trying to shape the rules and the conditions to play for next year, but, because of how quickly things are changing, we don’t know what the best decision will be. And also the research we are going to do will be affected by the decision we make on this.

There will be some changes to other leagues in the near future too; the small and middle sizes will close in two years probably, and the simulation league also. A lot will happen in the next five years, probably more than during the last 10-15 years. This is a critical year because the decisions are based on what we can see, what we can spot in the future, but we don’t have all the information we need, so it will be challenging.

For example, the SPL has a big, probably the biggest, community among the RoboCup leagues. We have a lot of teams that are grouping by interest and so there are teams that are sticking to working on this specific problem with a specific platform and teams that are trying to move to another platform and another problem. So even inside the same community we are going to have more than one point of view and hopes for the future. At a certain point we will try to figure out what is the best for all of them.

Daniele: I just want to add that in order to achieve the 2050 challenge, in my opinion, it is necessary to have just one league encompassing everything. So up to this point, different leagues have been focusing on different research problems. There were leagues focusing only on strategy, others focusing only on the hardware, our league focusing mainly on the coordination and dynamic handling of the gameplay. But at the end of the day, in order to compete with humans, there must be only one league bringing all these single aspects together. From my point of view, it totally makes sense to keep merging leagues together.

About the authors

Daniele Affinita is a PhD student in Machine Learning at EPFL, specializing in the intersection of Machine Learning and Robotics. He has over four years of experience competing in RoboCup with the SPQR team. In 2024, he worked at Sony on domain adaptation techniques. He holds a Bachelor’s degree in Computer Engineering and a Master’s degree in Artificial Intelligence and Robotics from Sapienza University of Rome.

Vincenzo Suriani earned his Ph.D. in Computer Engineering in 2024 from Sapienza University of Rome, with a specialization in artificial intelligence, robotic vision, and multi-agent coordination. Since 2016, he has served as Software Development Leader of the Sapienza Soccer Robot Team, contributing to major robotic competitions and international initiatives such as EUROBENCH, SciRoc, and Tech4YOU. He is currently a Research Fellow at the University of Basilicata, where he focuses on developing intelligent environments for software testing automation. His research, recognized with award-winning papers at the RoboCup International Symposium (2021, 2023, 2025), centers on robotic semantic mapping, object recognition, and human–robot interaction.

Domenico Daniele Bloisi is an associate professor of Artificial Intelligence at the International University of Rome UNINT. Previously, he was associate professor at the University of Basilicata, assistant professor at the University of Verona, and assistant professor at Sapienza University of Rome. He received his PhD, master’s and bachelor’s degrees in Computer Engineering from Sapienza University of Rome in 2010, 2006 and 2004, respectively. He is the author of more than 80 peer-reviewed papers published in international journals and conferences in the field of artificial intelligence and robotics, with a focus on image analysis, multi-robot coordination, visual perception and information fusion. Dr. Bloisi conducts research in the field of melanoma and oral carcinoma prevention through automatic medical image analysis in collaboration with specialized medical teams in Italy. In addition, Dr. Bloisi is WP3 leader of the EU H2020 SOLARIS project, unit leader for the PRIN PNRR RETINA project, unit leader for the PRIN 2022 AIDA project. Since 2015, he is the team manager of the SPQR robot soccer team participating in the RoboCup world competitions

Can Lin is a master student in Data Science at Sapienza university of Rome. He holds a bachelor degree in Computer science and Artificial intelligence from the same university. He joined the SPQR team in September of 2024, focusing on tasks related to computer vision.

]]> RoboCup@Work League: Interview with Christoph Steup https://robohub.org/robocupwork-league-interview-with-christoph-steup/ Fri, 22 Aug 2025 12:15:27 +0000 https://aihub.org/?p=18234 RoboCup@Work League teams at the event in Brazil.

RoboCup is an international scientific initiative with the goal of advancing the state of the art of intelligent robots, AI and automation. The annual RoboCup event, where teams gather from across the globe to take part in competitions across a number of leagues, this year took place in Salvador, Brazil from 15-21 July. In a series of interviews, we’ve been meeting some of the RoboCup trustees, committee members, and participants, to find out more about their respective leagues. Christoph Steup is an Executive Committee member and oversees the @Work League. Ahead of the event in Brazil, we spoke to Christoph to find out more about the @Work League, the tasks that teams need to complete, and future plans for the League.

Could you start by giving us an introduction to the @Work league?

The @Work League, along with the Logistics League, forms the Industrial League. Our goal is to mimic some of the aspects of industrial production systems. An important aspect of this is factory automization and trying to mimic the factory of the future, where you have autonomous robots building products according to customer design. In these factories of the future, a single piece would be produced individually for each customer. Factories nowadays have big conveyor belts and a lot of automization, with the tasks mostly done in the same way, and you can only build stuff efficiently if you build millions of items. We are working on building individual pieces, where automization is still possible, and even a single piece can be built effectively. But obviously, in our RoboCup competitions, we are not interested in building on a factory scale – we are doing it on a very small scale. That means our robots are typically 80 centimeters long, the largest are around 70 centimeters wide, and some of them are also 80 centimeters high. So let’s say they fit in a one metre cubed box. Also, all our operations are done on the ground. This is just for simplification because building big tables to make it more realistic would also increase the cost for RoboCup and wouldn’t give much additional value.

What our robots need to do is transport objects from different workstations. So we have a default configuration where the arena starts, and there are workstations with objects lying on them, and some of these objects need to be transported to other workstations. The robot needs to do that completely autonomously. So this is one of the special things about the @Work League, that it’s completely autonomous and there is only a single restart allowed per team. So that means the robot really needs to be reliable. One of the big differences between medium teams and very good teams is that the very good teams perform well all the time whereas the medium teams have some good runs and some bad runs.

As well as the object transportation that you mentioned, and there other tasks that the teams need to carry out?

There are some special tasks in our league, like the precision placement task where the robot needs to fit an object into a cavity that is essentially the same shape and size as the object. It’s a little bit like the game that babies do to train their dexterity.

We also have a task that is inspired by a conveyor belt, but we are using a table that is constantly turning. The robots need to grasp stuff while the table is turning. This looks a little bit silly because no one would actually put a rotating table in a factory, however this is our way of actually mimicking a conveyor belt. The conveyor belt itself would be really, really difficult to integrate into the competition, so we just abstracted that and use this rotating table to actually have the same challenge but in a more manageable way. And it’s still a very, very hard challenge.

Then there are some special challenges that we integrate. For example, that robots need to report their state back so that we can observe what the robot is doing. We also have a challenge where humans are in the loop. For example, the robot brings pieces to a certain workstation where a human is present, the human assembles the pieces, and then the human needs to give a sign to the robot, and then the robot will take the piece away and put it somewhere else. This is designed to really mimic the automated factory flow.

In the past we also had a challenge where the robots needed to open a drawer, take something out and then close the drawer again. We’ve also had tasks where the robot has to handle fragile objects, like sweets, where the robot really needed to be careful in manipulating them. So in general, what differentiates us most from the Logistics League is that we are focusing a lot on manipulation and all the difficulties that come with manipulation and unknown objects, whereas Logistics is more tailored towards large-scale logistics processes with all their optimization and planning.

I was lucky enough to attend RoboCup last year in Eindhoven and what the teams were doing was really impressive. It was also interesting to see how varied the robots were, and how teams were approaching the tasks in distinct ways, with different grabbers and so on.

Yes, this difference in approaches is related to the history of our League, which is a little bit similar to the Logistics League. The Logistics League was originally a sponsored demonstration by Festo, which is a large company from Germany that creates tools, but they also have a didactics area where they provide tools to help people understand factory optimization. The @Work League was sponsored by Kuka, the robotics company, and, in the beginning, they required every team to compete with the Kuka youBot. So this was pretty much the default platform for our league, but at some point Kuka dropped from a sponsor to just an advisor to the league, and nowadays they are not part of the league at all. So when the Kuka youBot was going out of commission, the teams searched for alternatives and now we are presented with a wide variety of robots that are competing in the league, which I personally find really cool. Now we have all these different robots, all these different approaches, and some work better in some scenarios and worse in others. So we really have a scientific approach to the problem and we are really getting some insights into how you can tackle this problem on multiple levels.

Have you noticed that some of the challenges specifically are more difficult in general for all the teams?

In 2018 we introduced a challenge of so-called arbitrary surfaces which are surfaces unknown to the teams that are put on top of the workstations. The teams need to be able to deal with these surfaces. There are two surfaces that are really, really awful for the teams – one is grass that we pretty much stole from the soccer competitions! We just thought it would be funny to try it, and it was a really interesting problem, especially for some of the grippers of the different teams. For example, the current world champion, they have a rigid gripper so they can have force feedback when they grasp. However, the grass is really difficult for them – because of their rigidity, they always grasp the grass itself and then they pull up the grass with the object. And this led to some interesting problems, like they are transporting the actual surface around and not only the object. This isn’t a problem for teams using a flexible gripper. However, on the other hand, the flexible gripper makes it really difficult to assess if you have grasped the object because you have very bad force feedback. So there are two different approaches that have their pros and cons in different scenarios.

Three robots from the @Work competition in Brazil.

Are you introducing any new tasks for this year?

Yes and no. So actually we are introducing a completely new challenge which is different from what we’ve done before. The new challenge is the so-called smart farming challenge, which is opening our league to a whole new field of applications, because we are now looking at agriculture. We are working on this with Studica, a robotics company from Canada, whose hardware we are using. We’ve already given it a try at the German Open. This challenge comes with some new specifics and one these is that the teams only get the robot shortly before the competition. So they don’t really know the robot largely in advance, and they need to assemble, program and design the robot in a very short time. To compensate for this, we reduce the amount of optimization and robustness that is necessary. Because it’s an agriculture setting, we have different objects, like fruits, that the teams need to handle. This makes it a little bit more complicated because fruits have a more arbitrary shape and different levels of ripeness that need to be detected. We also have some grapes that are hanging on a wall, which is a completely different kind of manipulation task than before, because before the teams just needed to grasp things from surfaces, but now they really need to pluck stuff from a wall in a reliable way. So this is the new challenge. It also comes with a lot of software challenges because the computational power of this robot is very limited because it only has a Raspberry Pi. For example, there is not a lot of image processing possible, especially compared to the current robots, some of which even have GPUs embedded.

Is there a particular part of the hardware or software that you’ve seen some of the biggest developments in over the last year or so?

Yeah, I think one big change I observed over the years was a switch from custom neural networks for object detection to off-the-shelf components. So pretty much all teams nowadays use YOLO networks, which you can get pre-trained and they just deploy them on GPUs that they embedded into their robots. This is also one of the reasons why the robots really grew in size over the last few years because they needed space for the larger computational power. This actually made it possible for a lot of teams to reliably detect the objects. Object detection was a big problem in the beginning of the league and nowadays it’s not really a big issue – most teams are really good at that. Sometimes they are a little bit startled with decoy objects – these are objects in the arena that are not really part of the task, and they are unknown to the teams beforehand. Sometimes they are, let’s say, evil decoys that look like an object and there’s some mismatches that the teams do, but this is becoming very rare.

I think the second big change is a switch to larger manipulators with more degrees of freedom. So in the beginning, everyone had a very small manipulator with only five degrees of freedom, which limited the operating range, and nowadays pretty much all teams have a six degree of freedom manipulator with a large range. This means that they don’t need to move their robot when they are in front of a workstation, which makes them much faster and also much more precise.

Could you talk about the future plans for the League?

There are a few things we are thinking about.

With regards to the competition itself, we had a discussion with the teams about what they are interested in doing in the future. Two things came up that they really want to have. One is mobile obstacles, so they want other objects to move autonomously through the arena. We’re in the process of creating that, in cooperation with EduArt, which is a company from Germany that also provides small educational robots. And the second thing we want to introduce is a kind of humanoid robot that the teams can use to actually handle special manipulation tasks that cannot be done simply with a robot manipulator.

In terms of creating an entry-level League, we have been working on this and one potential idea is to use the smart-farming challenge as the entry point. Through the collaboration with Studica, we can provide teams with the robot and they get to keep it after the competition. At the German Open I spoke to the Rapidly-Manufactured Rescue League about this crossover between the Rescue and the Junior Leagues, and they are very keen to collaborate.

We’re also talking with the Logistics League. Their sponsor, Festo, has dropped out of their league and now they need to reorganize. We are wondering if it would be worthwhile to bring our leagues closer together, or even fuse them together to a single RoboCup Industrial league. The Logistics League wants to do more manipulation, and the @Work League wants to do more planning, so we are closing the gap naturally between the two. However, this is just a thought at the moment – we need to see how the teams react to that.

About Christoph

Christoph Steup is an active researcher specializing in various fields of robotics, including swarm robotics, precision farming, and weather-resilient autonomous driving. He currently works at the Fraunhofer Institute for Transportation and Infrastructure Systems (IVI), where he leads the Swarm Technology Group. Prior to this role, he headed the Computational Intelligence in Robotics group at the Otto von Guericke University Magdeburg. Christoph’s involvement with RoboCup began in 2015 when he joined the robOTTO team of Otto von Guericke University as team leader. His contributions to the RoboCup community expanded as he became a member of the Technical Committee for the @Work League in 2017. In 2019, he further advanced his participation by joining the Executive Committee of the league.
]]> #RoboCup2025: social media round-up part 2 https://robohub.org/robocup2025-social-media-round-up-part-2/ Thu, 24 Jul 2025 09:13:41 +0000 https://aihub.org/?p=18043

RoboCup2025 took place from 15-21 July in Salvador, Brazil. The event saw around 3000 participants competing in the various leagues. In our first social media round-up post we saw what the teams got up to during the first couple of days of the event. In this second post, we take a look at the action from the final days when the competitions reached their climax.

In the #RoboCup2025 @Home OPL Final, our robot performed very well. It opened two doors, removed trash, and closed a cabinet door. Overall, NimbRo came in second, next to team Tidyboy (Korea).
www.ais.uni-bonn.de/nimbro/@Home

[image or embed]

— Sven Behnke (@sven-behnke.bsky.social) 20 July 2025 at 18:04

]]> #RoboCup2025: social media round-up 1 https://robohub.org/robocup2025-social-media-round-up-1/ Mon, 21 Jul 2025 12:56:55 +0000 https://aihub.org/?p=18022

RoboCup2025 took place in Salvador, Brazil. The event saw around 3000 participants competing in the various leagues. Find out what the teams got up to during the first couple of days:

]]> Livestream of RoboCup2025 https://robohub.org/livestream-of-robocup2025/ Fri, 18 Jul 2025 10:55:03 +0000 https://aihub.org/?p=18030

RoboCup2025 is currently taking place in Salvador, Brazil. With day one of the main competition complete, things are hotting up across the many different leagues. From soccer to rescue, from industrial to home scenarios, teams are putting their robots through their paces across a variety of tasks and matches.

If you would like to catch up on the action from the first day, you can watch the recording of the livestream below. This includes coverage of the teams competing, interviews with participants and organisers, and insights into RoboCup and the various leagues.

The livestreams for days two and three, which will feature the knockout stages of the competitions, can be found below:

The livestream for the award ceremony, will be here:

You can also find the livestream on the RoboCup Twitch channel

Read our series of interviews with RoboCup organisers, trustees, and committee members:

]]> Tackling the 3D Simulation League: an interview with Klaus Dorer and Stefan Glaser https://robohub.org/tackling-the-3d-simulation-league-an-interview-with-klaus-dorer-and-stefan-glaser/ Tue, 15 Jul 2025 14:26:05 +0000 https://aihub.org/?p=17971 A screenshot from the new simulator that will be trialled for a special challenge at RoboCup2025.

The annual RoboCup event, where teams gather from across the globe to take part in competitions across a number of leagues, will this year take place in Brazil, from 15-21 July. In advance of kick-off, we spoke to two members of the RoboCup Soccer 3D Simulation League: Executive Committee Member Klaus Dorer, and Stefan Glaser, who is on the Maintenance Committee and who has been recently developing a new simulator for the League.

Could start by just giving us a quick introduction to the Simulation League?
Klaus Dorer: There are two Simulation Leagues in Soccer: the 2D Simulation League and the 3D Simulation League. The 2D Simulation League, as the name suggests, is a flat league where the players and ball are simulated with simplified physics and the main focus is on team strategy. The 3D Simulation League is much closer to real robots; it simulates 11 versus 11 Nao robots. The level of control is like with real robots, where you move each motor of the legs and the arms and so on to achieve movement.

I understand that you have been working on a new simulator for the 3D League. What was the idea behind this new simulator?
Klaus: The aim is to bring us closer to the hardware leagues so that the simulator can be more useful. The current simulator that we use in the 3D Simulation League is called SimSpark. It was created in the early 2000s with the aim of making it possible to play 11 vs 11 players. With the hardware constraints of that time, there had to be some compromises on the physics to be able to simulate 22 players at the same time. So the simulation is physically somewhat realistic, but not in the sense that it’s easy to transpose it to a real Nao robot.

Stefan Glaser: The idea for developing a new simulator has been around for a few years. SimSpark is a very powerful simulation framework. The base framework is domain independent (not soccer specific) and specific simulations are realized via plugins. It supports multiple physics engines in the backend and provides a flexible scripting interface for configuration and adaptations of the simulation. However, all this flexibility comes with the price of complexity. In addition to that, SimSpark uses custom robot model specifications and communication protocols, limiting the amount of available robot models and requiring teams to develop custom communication layers only for communicating with SimSpark. As a result of this, SimSpark has not been widely adopted in the RoboCup community.

With the new simulator, I would like to address these two major issues: complexity and standardization. In the ML community, the MuJoCo physics engine has become a very popular choice for learning environments after Google DeepMind acquired it and released it open source. Its standards for world and robot model specifications are widely adopted in the community and there exist a lot of ready-to-use robot model specifications for a wide variety of virtual as well as real-world robots. In the middle of last year, they (MuJoCo) added a feature which allows you to manipulate the world representation during simulation (adding and removing objects to / from the simulation while preserving the simulation state). This is one essential requirement we have in the simulation league, where we start with an empty field and then the agents connect on demand and form the teams. When this feature has been added, I decided to make a step forward and try to implement a new simulator for the 3D Simulation League based on MuJoCo. Initially, I wanted to start development in C/C++ to achieve maximum performance, but then decided to start in Python to reduce complexity and make it more accessible for other developers. I started development on Easter Monday so it’s not even three months old!

I think it might be useful to explain a little bit more about the setup of our league and the requirements of the simulator. If we take the FIFA game (on your favorite gaming device) as an example, there is one simulation happening which simulates 22 players and the decision making is part of the simulation having full access to the state of the world. In the 3D Simulation League we have two teams with 11 robots on the field, but we also have 22 individual agent softwares which are connected to the simulation server, each controlling one single robot. Each connected agent only receives sensor information related to their robot in the simulation. They are also only allowed to communicate via the server – there is no direct communication between the agents allowed in Simulation League. So we have a general setup where the simulation server has to be able to accept up to 22 connections and manage the situation there. This functionality has been the major focus for me for the last couple of months and this part is already working well. Teams can connect their agents, which will receive sensor information and can actuate joints of the robot in the simulation and so on. They are also able to select different robot models if they like.

An illustration of the simulator set-up.

Presumably the new simulator has a better representation of the physics of a real robot.
Klaus: Exactly. For example, how the motors are controlled is now a bit different and much closer to real robots. So when I did my first experiments, I saw the robot collapse and I thought it was exactly how a real robot would collapse! In SimSpark we also had falling robots but the motor control in the new simulator is different. Now you can control the motors by speed, by force, by position, which is much more flexible – it’s closer to what we know from real robots.

I think that, at least initially, it will be more difficult for the Simulation League teams to get the robots to do what they want them to do, because it’s more realistic. For example, in SimSpark the ground contact was much more forgiving. So if you step hard on the ground, you don’t fall immediately with a SimSpark robot but with a MuJoCo robot this will be much more realistic. Indeed, in real robots ground contact is somewhat less forgiving.

I had a question about the vision aspect – how do the individual agents “see” the position of the other agents on the field?
Stefan: We simulate a virtual vision pipeline on the server side. You have a restricted field of view of ±60° horizontally and vertically. Within that field of view you will detect the head, the arms, the feet of other players, or the ball, for example, or different features of the field. Similar to common real-world vision pipelines, each detection consists of a label, a direction vector and the distance information. The information has some noise on it like real robots have, too, but teams don’t need to process camera images. They get the detections directly from the simulation server.

We’ve previously had a discussion about moving towards getting camera images of the simulation to integrate into the vision pipeline on the agent side. This was never really realistic in SimSpark with the implementation we had there. However, it should be possible with MuJoCo. However, for the first version, I used the same way the traditional simulator treated the vision. This means that teams don’t need to train a vision model, and don’t need to handle camera images to get started. This reduces the load significantly and also shifts the focus of the problem towards motion and decision making.

Will the simulator be used at RoboCup 2025?
Stefan: We plan to have a challenge with a new simulator and I will try to provide some demo games. At the moment it’s not really in a state where you can play a whole competition.

Klaus: That’s usually how we proceed with new simulators. We would not move from one to the other without any intermediate step. We will have a challenge this year at RoboCup 2025 with the new MuJoCo simulator where each participating team will try to teach the robot to kick as far as possible. So, we will not be playing a whole game, we won’t have multiple robots, just a single robot stepping in front of the ball and kicking the ball. That’s the technical challenge for this year. Teams will get an idea of how the simulator works, and we’ll get an idea of what has to be changed in the simulator to proceed.

This new challenge will be voluntary, so we are not sure how many teams will participate. Our team (MagmaOffenburg) will certainly take part. It will be interesting to see how well the teams perform because no one knows how far a good kick is in this simulator. It’s a bit like in Formula One when the rules change and no one knows which team will be the leading team.

Do you have an idea of how much adaptation teams will have to make if and when you move to the new simulator for the full matches?
Stefan: As a long-term member of 3D Simulation League, I know the old simulator SimSpark pretty well, and know the protocols involved and how the processes work. So the first version of the new simulator is designed to use the same basic protocol, the same sensor information, and so on. The idea is that the teams can use the new simulator with minimal effort in adapting their current agent software. So they should be able to get started pretty fast.

Although, when designing a new platform, I would like to take the opportunity to make a step forward in terms of protocols, because I also want to integrate other Leagues in the long-term. They usually have other control mechanisms, and they don’t use the same protocol that is prominent in 3D Simulation. Therefore there has to be some flexibility in the future. But for the first version, the idea was to get the Simulation League ready with minimal effort.

Klaus: The big idea is that this is not just used in the 3D Simulation league, but also as a useful simulator for the Humanoid League and also for the Standard Platform League (SPL). So if that turns out to be true, then it will be completely successful. For the Kick Challenge this year, for example, we use a T1 robot that is a Humanoid League robot.

Could you say something about this simulation to real world (Sim2Real) aspect?
Stefan: We’d like it to be possible for the motions and behaviors in the simulator to be ported to real robots. From my point of view, it would be useful the other way round too.

We, as a Simulation League, usually develop for the Simulation League and therefore would like to get the behaviors running on a real robot. But the hardware teams usually have a similar issue when they want to test high-level decision making. They might have two to five robots on the field, and if they want to play a high-level decision-making match and train in that regard, they always have to deploy a lot of robots. If they also want to have an opponent, they have to double the amount of robots in order to play a game to see how the strategy would turn out. The Sim2Real aspect is also interesting for these teams, because they should be able to take what they deployed on the real robot and it should also work in the simulation. They can then use the simulation to train high-level skills like team play, player positioning and so on, which is a challenging aspect for the real robot leagues like SPL or the Humanoid Leagues.

Klaus: And the reason we know this is because we have a team in the Simulation League and we have a team in the Humanoid League. So that’s another reason why we are keen to bring these things closer together.

How does the refereeing work in the Simulation League?
Klaus: A nice thing about Simulation Leagues is that there is a program which knows the real state of the world so we can build in the referee inside the simulator and it will not fail. For things like offside, whether the ball passed the goal line, that’s fail safe. All the referee decisions are taken by the system itself. We have a human referee but they never need to intervene. However, there are situations where we would like artificial intelligence to play a role. This is not currently the case in SimSpark because the rules are all hard coded. We have a lot of fouls that are debatable. For example, there are many fouls that teams agree should not have been a foul, and other fouls that are not called that should have been. It would be a nice AI learning task to get some situations judged by human referees and then train an AI model to better determine the rules for what is a foul and what isn’t a foul. But this is currently not the case.

Stefan: On the new simulator I am not that far into the development that I have implemented the automatic referee yet. I have some basic set of rules which progress the game as such, but judging fouls and deciding on special situations is not yet implemented in the new simulator.

What are the next steps for developing the simulator?
Stefan: One of the next major steps will be to refine the physics simulation. For instance, even though there exists a ball in the simulation, it is not yet really well refined. There are a lot of physics parameters which we have to decide on to reflect the real world as good as possible. This will likely require a series of experiments in order to get to the correct values for various aspects. In this aspect I’m hoping for some engagement of the community, as it is a great research opportunity and I personally would prefer the community to decide on a commonly accepted parameter set based on a level of evidence that I can’t easily provide all by myself. So in case someone is interested in refining the physics of the simulation such that it best reflects the real world, you are welcome to join!

Another major next step will be the development of the automatic referee of the soccer simulation, deciding on fouls, handling misbehaving agents and so on. In the first version, foul conditions will likely be judged by an expert system specifically designed for this purpose. The simulation league has developed a set of foul condition specifications which I plan to adapt. In a second step, I would like to integrate and support the development of AI based foul detection models. But yeah, one step after the other.

What are you particularly looking forward to at RoboCup2025?
Klaus: Well, with our team we have been vice world champion seven times in a row. This year we are really hoping to make it to world champion. We are very experienced in getting losses in finals and this year we are looking forward to changing that, from a team perspective.

Stefan: I’m going to Brazil in order to promote the simulator, not just for the Simulation League, but also across the boundaries for the Humanoid Leagues and the SPL Leagues. I think that this simulator is a great chance to bring people from all the leagues together. I’m particularly interested in the specific requirements of all the teams of the different leagues. This understanding will help me tailor the new simulator towards their needs. This is one of my major highlights for this year, I would say.

You can find out more about the new simulator at the project webpage, and from the documentation.

Klaus Dorer is professor for artificial intelligence, autonomous systems and software engineering at Offenburg University, Germany. He is also a member of the Institute for Machine Learning and Analytics IMLA. He has been team leader of the RoboCup simulation league teams magmaFreiburg (since 1999), living systems, magmaFurtwangen and is now team leader of magmaOffenburg since 2009. Since 2014, he has also been part of the humanoid adult size league team Sweaty.

Stefan Glaser is teaching assistant for artificial intelligence and intelligent autonomous systems at the Offenburg University, Germany. He has been part of the RoboCup simulation league team magmaOffenburg since 2009 and the RoboCup humanoid adult size league team Sweaty since 2014.
]]> An interview with Nicolai Ommer: the RoboCup Soccer Small Size League https://robohub.org/an-interview-with-nicolai-ommer-the-robocupsoccer-small-size-league/ Tue, 01 Jul 2025 10:26:04 +0000 https://aihub.org/?p=17947 Kick-off in a Small Size League match. Image credit: Nicolai Ommer.

RoboCup is an international scientific initiative with the goal of advancing the state of the art of intelligent robots, AI and automation. The annual RoboCup event is due to take place from 15-21 July in Salvador, Brazil. The Soccer component of RoboCup comprises a number of Leagues, with one of these being the Small Size League (SSL). We caught up with Executive Committee member Nicolai Ommer to find out more about the SSL, how the auto referees work, and how teams use AI.

Could start by giving us a quick introduction to the Small Size League?

In the Small Size League (SSL) we have 11 robots per team – the only physical RoboCup soccer league to have the full number of players. The robots are small, cylindrical robots on wheels and they can move in any direction. They are self-built by the teams, so teams have to do both the hardware and the programming, and a lot of things have to work together to make a team work. The AI is central. We don’t have agents, so teams have a central computer at the field where they can do all the computation and then they send the commands to the robots in different abstractions. Some teams will just send velocity commands, other teams send a target.

We have a central vision system – this is maintained by the League, and has been since 2010. There are cameras above the field to track all the robots and the ball, so everyone knows where the robots are.

The robots can move up to 4 meters per second (m/s), after this point it gets quite unstable for the robots. They can change direction very quickly, and the ball can be kicked at 6.5 m/s. It’s quite fast and we’ve already had to limit the kick speed. Previously we had a limit of 8 m/s and before that 10m/s. However, no robot can catch a ball with this speed, so we decided to reduce it and put more focus on passing. This gives the keeper and the defenders a chance to actually intercept a kick.

It’s so fast that for humans it’s quite difficult to understand all the things that are going on. And that’s why, some years ago, we introduced auto refs, which help a lot in tracking, especially things like collisions and so on, where the human referee can’t watch everything at the same time.

How do the auto refs work then, and is there more than one operating at the same time?

When we developed the current system, to keep things fair, we decided to have multiple implementations of an auto ref system. These independent systems implement the same rules and then we do a majority vote on the decisions.

To do this we needed a middle component, so some years ago I started this project to have a new game controller. This is the user interface (UI) for the human referee who sits at a computer. In the UI you see the current game state, you can manipulate the game state, and this component coordinates the auto refs. The auto refs can connect and report fouls. If only one auto ref detects the foul, it won’t count it. But, if both auto refs report the foul within the time window, then it is counted. Part of the challenge was to make this all visual for the operator to understand. The human referee has the last word and makes the final decision.

We managed to establish two implementations. The aim was to have three implementations, which makes it easier to form a majority. However, it still works with just two implementations and we’ve had this for multiple years now. The implementations are from two different teams who are still active.

How do the auto refs deal with collisions?

We can detect collisions from the data. However, even for human referees it’s quite hard to determine who was at fault when two robots collide. So we had to just define a rule, and all the implementations of the auto ref implement the same rule. We wrote in the rulebook really specifically how you calculate if a collision happened and who was at fault. The first consideration is based on the velocity – below 1.5m/s it’s not a collision, above 1.5m/s it is. There is also another factor, relating to the angle calculation, that we also take into account to determine which robot was at fault.

What else do the auto refs detect?

Other fouls include the kick speed, and then there’s fouls relating to the adherence to normal game procedure. For example, when the other team has a free kick, then the opposing robots should maintain a certain distance from the ball.

The auto refs also observe non-fouls, in other words game events. For example, when the ball leaves the field. That’s the most common event. This one is actually not so easy to detect, particularly if there is a chip kick (where the ball leaves the playing surface). With the camera lens, the parabola of the ball can make it look like it’s outside the field of play when it isn’t. You need a robust filter to deal with this.

Also, when the auto refs detect a goal, we don’t trust them completely. When a goal is detected, we call it a “possible goal”. The match is halted immediately, all the robots stop, and the human referee can check all the available data before awarding the goal.

You’ve been involved in the League for a number of years. How has the League and the performance of the robots evolved over that time?

My first RoboCup was in 2012. The introduction of the auto refs has made the play a lot more fluent. Before this, we also introduced the concept of ball placement, so the robots would place the ball themselves for a free kick, or kick off, for example.

From the hardware side, the main improvement in recent years has been dribbling the ball in one-on-one situations. There has also been an improvement in the specialized skills performed by robots with a ball. For example, some years ago, one team (ZJUNlict) developed robots that could pull the ball backwards with them, move around defenders and then shoot at the goal. This was an unexpected movement, which we hadn’t seen before. Before this you had to do a pass to trick the defenders. Our team, TIGERs Mannheim, has also improved in this area now. But it’s really difficult to do this and requires a lot of tuning. It really depends on the field, the carpet, which is not standardized. So there’s a little bit of luck that your specifically built hardware is actually performing well on the competition carpet.

The Small Size League Grand Final at RoboCup 2024 in Eindhoven, Netherlands. TIGERs Mannheim vs. ZJUNlict. Video credit: TIGERs Mannheim. You can find the TIGERs’ YouTube channel here.

What are some of the challenges in the League?

One big challenge, and also maybe it’s a good thing for the League, is that we have a lot of undergraduate students in the teams. These students tend to leave the teams after their Bachelor’s or Master’s degree, the team members all change quite regularly, and that means that it’s difficult to retain knowledge in the teams. It’s a challenge to keep the performance of the team; it’s even hard to reproduce what previous members achieved. That’s why we don’t have large steps forward, because teams have to repeat the same things when new members join. However, it’s good for the students because they really learn a lot from the experience.

We are continuously working on identifying things which we can make available for everyone. In 2010 the vision system was established. It was a huge factor, meaning that teams didn’t have to do computer vision. And we are currently looking at establishing standards for wireless communication – this is currently done by everyone on their own. We want to advance the League, but at the same time, we also want to have this nature of being able to learn, being able to do all the things themselves if they want to.

You really need to have a team of people from different areas – mechanical engineering, electronics, project management. You also have to get sponsors, and you have to promote your project, get interested students in your team.

Could you talk about some of the AI elements to the League?

Most of our software is script-based, but we apply machine learning for small, subtle problems.

In my team, for example, we do model calibration with quite simple algorithms. We have a specific model for the chip kick, and another for the robot. The wheel friction is quite complicated, so we come up with a model and then we collect the data and use machine learning to detect the parameters.

For the actual match strategy, one nice example is from the team CMDragons. One year you could really observe that they had trained their model so that, once they scored goal, they upvoted the strategy that they applied before that. You could really see that the opponent reacted the same way all the time. They were able to score multiple goals, using the same strategy again and again, because they learned that if one strategy worked, they could use it again.

For our team, the TIGERs, our software is very much based on calculating scores for how good a pass is, how well can a pass be intercepted, and how we can improve the situation with a particular pass. This is hard-coded sometimes, with some geometry-based calculations, but there is also some fine-tuning. If we score a goal then we track back and see where the pass came from and we give bonuses on some of the score calculations. It’s more complicated than this, of course, but in general it’s what we try to do by learning during the game.

People often ask why we don’t do more with AI, and I think the main challenge is that, compared to other use cases, we don’t have that much data. It’s hard to get the data. In our case we have real hardware and we cannot just do matches all day long for days on end – the robots would break, and they need to be supervised. During a competition, we only have about five to seven matches in total. In 2016, we started to record all the games with a machine-readable format. All the positions are encoded, along with the referee decisions, and everything is in a log file which we publish centrally. I hope that with this growing amount of data we can actually apply some machine learning algorithms to see what previous matches and previous strategies did, and maybe get some insights.

What plans do you have for your team, the TIGERs?

We have actually won the competition for the last four years. We hope that there will be some other teams who can challenge us. Our defence has not really been challenged so we have a hard time finding weaknesses. We actually play against ourselves in simulation.

One thing that we want to improve on is precision because there is still some manual work to get everything calibrated and working as precisely as we want it. If some small detail is not working, for example the dribbling, then it risks the whole tournament. So we are working on making all these calibration processes easier, and to do more automatic data processing to determine the best parameters. In recent years we’ve worked a lot on dribbling in the 1 vs 1 situations. This has been a really big improvement for us and we are still working on that.

About Nicolai

Nicolai Ommer is a Software Engineer and Architect at QAware in Munich, specializing in designing and building robust software systems. He holds a B.Sc. in Applied Computer Science and an M.Sc. in Autonomous Systems. Nicolai began his journey in robotics with Team TIGERs Mannheim, participating in his first RoboCup in 2012. His dedication led him to join the RoboCup Small Size League Technical Committee and, in 2023, the Executive Committee. Passionate about innovation and collaboration, Nicolai combines academic insight with practical experience to push the boundaries of intelligent systems and contribute to the global robotics and software engineering communities.
]]> RoboCupRescue: an interview with Adam Jacoff https://robohub.org/robocuprescue-an-interview-with-adam-jacoff/ Wed, 25 Jun 2025 13:28:20 +0000 https://aihub.org/?p=17908 The RoboCupRescue arena at RoboCup2024, Eindhoven.

RoboCup is an international scientific initiative with the goal of advancing the state of the science of intelligent robots, AI and automation. The annual RoboCup event will take place from 15-21 July in Salvador, Brazil. The RoboCupRescue League is an important element of the competition and focuses on the challenges involved in search and rescue applications. We caught up with Adam Jacoff, co-founder of the RoboCupRescue league, former RoboCup Trustee, and chair of the organising committee, to find out more.

Could you start by giving us an overview of the Rescue League?

The RoboCupRescue League is now in its 25th year hosting competitions and workshops all around the world. We’re focused on developing autonomous robots that can enable emergency responders to perform extremely hazardous tasks from safer stand-off distances. That includes search and rescue scenarios in compromised or collapsed structures, for example, but there are many tasks that firefighters and other public safety organizations do every day that would be safer with robots doing the dangerous part. We are really the only League at RoboCup that’s focused on emergency responders, so our arenas appear a little more chaotic than the other leagues, but in a controlled sort of way. We have twenty standard test methods that we use as challenge tasks that emergency responders and our league of teams around the world have helped develop and validate over many years. These competitions do three things at once.

First, they guide the research with tangible terrains, obstacles, and tasks that are representative of emergency response operations. The researchers likely do not know any emergency responders, but we do, and we have distilled their requirements into twenty or so standard test methods. So the teams know that if they can solve the challenges presented to them in the test lanes, maybe not all twenty, but ten of them, or five of them, that combination of capabilities are applicable to some public safety mission. And the more autonomy the researchers can implement into their robots, the more likely the robots will be effective and easy to use in the field. The main objective of our league is to quicken the development of effective and reliable autonomous behaviors for these complex environments.

RoboCup is an excellent incubator for developing and evaluating cutting edge research and at the same time for developing, validating, and disseminating new standard test methods. Our standard test methods are reproducible and approach the reality of very complex and hazardous environments in an incremental way. We have increasingly difficult settings for each test. So we start easy in the Preliminaries with all the terrain lanes flat so teams can optimize their approaches for each particular challenge. By Semi-Finals the terrain lanes are inclined into crossover 15° slopes with a complex terrain obstacle in the middle. The teams start performing sequences of multiple lanes, so they need to intelligently switch between different behaviors to succeed. Then for the Finals we inject more complexity with slippery features for the robots to lose traction, pinch points to force more steering, and higher step-over obstacles to make the sequences even harder. This is how we guide and challenge both autonomous and remotely operated robots beyond their comfort zone.

Second, the intensity of the experience and education for engineers and computer scientists leads to recruiting opportunities and careers. Everybody understands that teams who do well at RoboCupRescue have done something special. They can go on to more intense research or can help implement that technology in the commercial market. Our League has inspired and validated new robotic approaches to very complex environments and helped make the jump from research to commercial implementation.

The third piece of the puzzle is our connection to various communities of emergency responders. Our sequence of test lanes is exactly what emergency responders use to support their procurement and training. They use trial data captured with the robot manufacturers to help decide what robot to buy. They also use the test lanes to train remote operators and measure their proficiency. So RoboCupRescue helps emergency responders understand what the next wave of emerging technologies can do. For example, the nuclear waste cleanup industry wants to credential their remote robot operators to ensure robots don’t get stranded within extremely harsh radiation-filled environments. The robots need to keep working for 1,000 hours or more to be cost effective. They also want to ensure remote operators are proficient enough to safely perform the intended tasks without crashing the robot on the stairs and blocking the way for all other robots. Or worse yet, force humans in protective suits to go in and clear the way. Different organizations come to RoboCupRescue competitions and see various aspects of the league in action that are useful to them. In this case, as an obstacle course to get a robot driver’s license.

At Eindhoven last year (for RoboCup2024) we had the local bomb squads provide what we call a “reverse demonstration.” All these researchers give demonstrations of their robots at their schools to sponsors, family, friends, and maybe even to emergency responders. But they never get to see what the emergency responders do for a living – how dangerous their jobs are. So the RoboCupRescue league typically pauses for a while during the competition and comes together to watch a bomb technician get into their padded suit and go through the sequence of test lanes doing all the tasks that we’re asking the robots to perform. The researchers see how much of a burden it is just to wear the padded suits, which are very heavy and require forced air into the helmet. It’s not only physically difficult, they’re walking to and interacting with known explosives to render them safe – which is unconscionable given the capabilities of robots these days. They need to be working remotely from a safe stand-off distance. These “reverse demonstrations” are likely the first time the researchers ever get to meet people in bomb suits, and understand that it is these emergency responders we are working to help. Whether it’s firefighters or police, a natural or manmade disaster, we’re trying to help emergency responders stay out of harm’s way. They need capable, reliable, and easy to use robots to deal with extreme hazards more safely and effectively.

The RoboCupRescue arena at ICRA2025 (one of the qualifying events for RoboCup2025). Credit: Adam Jacoff.

So you mentioned trying to promote autonomy over teleoperation. How do you go about trying to encourage people to use more autonomy in the league?

Competitions need to have simple and clear rules. They need to challenge robots, lead them toward practical solutions, and highlight teams that are successful in a variety of challenges. So RoboCupRescue has developed scoring tasks for mobility, dexterity, and mapping within complex terrains. To score mobility points, for example, the robot needs to drive from one end of a terrain lane to the other end, or ascend and descend obstacles like stairs. They earn 1 point for each successful traverse up to 10 total repetitions to add some statistical significance. The operator is always out of sight of the robot, so working only through their interface as if the robot is inside a building. The terrain lanes and obstacles start easy and get harder, but all teams are being evaluated in the same difficulty setting as the competition progresses, so the points stay the same.

If the robot can successfully traverse the lane or perform the dexterity tasks without the operator touching the interface, we call that autonomy. At the beginning of each traverse the operator can manually set a goal point at the other end of the lane. Then they hit “go” and we all watch and learn together. If the robot gets to the other end without the operator touching the controls, the team gets 4 points instead of 1 point for that autonomous traverse. That 4:1 one ratio appears to be enough incentive to make sure teams develop autonomous behaviors and try them at each start point. However, they can always repair back to remote operation anytime the robot stalls to score at the lesser rate, so the rules encourage trying autonomy first. Also, we give Best-In-Class Autonomy awards to recognize teams that earned the most points autonomously throughout the competition.

Why do we need autonomy in emergency response operations? First, because the onboard autonomy typically makes the robot easier to use for a remote operator, which can also mean it is more likely to succeed down range or maybe be more reliable. Also, inside buildings there are often radio communications drop-out zones. Think about your cell phone reception inside concrete buildings or in basements under buildings, these places still need to be searched even when radio communication from a remote stand-off location outside the structure doesn’t support real-time video or control. So each RoboCupRescue lane, and the entire Labyrinth or Maze used for mapping, is considered a “radio drop-out zone” with tasks that score 4 to 1 if you can perform them autonomously. That applies to mobility, dexterity, and mapping tasks.

An example of one of the lanes (challenges) that teams tackle during competition. Credit: Adam Jacoff.

Will there be any new innovations to look out for this year?

What you’re going to see new this year are four-legged robots, called quadrupeds, that now have wheels as feet. There was a commercial robot that came to RoboCup2024 in Eindhoven. I invited them to demonstrate in the test lanes while the teams were at lunch, and this new robot design literally dominated our Semi-Finals sequence. Now that was a demonstration, conducted with the operator in direct line-of-sight of the robot, so much easier than remotely controlling the robot in the same terrains. But it didn’t have a remote interface. That’s exactly what our league of teams do best by adding interfaces, sensors, mapping, manipulators, and the autonomy to make it all effective and easier to use remotely.

These legged robots with wheels are a literal step function improvement in mobility, the likes of which I’ve only seen twice in thirty plus years. They have a level of mobility relative to their size that is astonishing. So we’re turning that new robot into our league’s first “standard platform.”

One of the four-legged robots with wheels as feet. Image from video taken at ICRA 2025, of the robot tackling one of the test lanes. Credit: Adam Jacoff.

Yes, I wanted to ask about the League’s plans for introducing a standard platform. Could you talk more about that?

This robot will provide a common development platform across teams with inherently good mobility within our terrains and obstacles. The league is negotiating with the manufacturer so that teams can purchase it at a relatively low price, which will bring down the cost of entry for new teams into the league. That level of mobility within our complex terrains and obstacles makes the autonomy a lot easier to develop – teams can focus on higher-level planning because the lower-level autonomous gaits and behaviors are already relatively effective. Similarly equipped teams can collaborate more closely, compare performance more directly, and share software solutions.

As a league, we can also start introducing teaming tasks for multiple robots. What kind of teaming might we explore? Maybe a certain object of interest is too heavy to drag for one robot, but could three of them work together to get it done like a team of dogs pulling a sled? Or maybe to carry payloads and supplies down-range, or even a non-ambulatory victim in a so-called litter back up-range to the base of operations. There will be lots of possibilities.

This new class of robots will operate in all the same test lanes along with all the other wheeled, tracked, and other robots developed by teams. We score every robot similarly of course, but then can separately compare the quantitative trial results for different classes of robots. It might turn out that it’s unfair to compare a new legged robot with wheels with a slower but stronger tracked robot that’s been winning RoboCupRescue for years. It’s not like tracked robots don’t have their use cases in emergency response operations. As soon as you get to tasks that involve exerting forces on the environment, like turning valves, the robots benefit from being heavier and stronger, which typically means tracks with independent front and rear flippers to negotiate the complex terrains and climb stairs. Everyone doesn’t need to adopt the new robot to compete, teams should work on implementations that push the state of the science in whatever direction they can. Then our Best-In-Class Awards can recognize the most effective robots in a variety of like-kind implementations, wheeled, tracked, legged, and maybe now legged with wheels.

When humanoids come into the league, and they will at some point, they’ll be welcomed and will be compared directly against other humanoids at least initially. We can also aggregate performance across a class of robots to identify advantages and apparent weaknesses. Emergency responders can then look at all the trial results to see which systems might work in their environments.

Do you know how many teams will be using this new robot?

I think we have almost twenty teams involved this year, but I’m not sure how many will be using the new legged robot with wheels. I expect maybe three or four initially, which would make a reasonable bracket of teams that can collaborate and compare more closely than others. There is a team from Austria that was in the German Open that is planning to come to Brazil. I’m sure when more people see the robot in action they’ll want one. Especially any new team coming into the league starting from scratch. Meanwhile, the RoboCupRescue league is poised to evaluate that robot design fully, add essential operational capabilities, and push the state of the science a bit further.

An example of the legged, wheeled robot in action. Credit: Adam Jacoff.

Will there be any different challenges this year?

Yes, this year we’re adding a new version of our original Stepfield terrain. They were our first rather complex terrain that inspired drastic changes in robot designs twenty years ago. The Stepfield terrain ensures the robots are always in contact with multiple different terrain elevations, and there is no respite throughout, even for legged robots with wheels. For the past couple of years, we got away from the Stepfield terrain because it was the hardest terrain to replicate at a temporary venue. But the year off for COVID was a growth time for us in terms of making the test methods as inexpensive and stowable as possible, so that small research organizations can replicate the tests to practice, refine approaches, and evaluate themselves during their development process. We first found a way to fabricate new Stepfields terrains from purchased, stackable crates. Now we have a fabricated version that accomplishes the same objectives less expensively. So RoboCupRescue teams will validate that the test apparatus as fabricated can stand up to the constant rigor of a very wide variety of robots across hundreds of trials in less than a week. That’s why RoboCupRescue is an excellent incubator for standard test methods too.

Is there anything else you’d like to highlight about the League?

One of the key features of RoboCupRescue is that the test lanes fabricated for the competition typically become a robot test facility for a local organization, like a nearby emergency responder facility or university. It can be set up perpetually, or it can move periodically to support regional competitions within the country. We usually try to connect the local organizers with their nearest emergency responders who already have robots or are seeking to purchase robots. The Olympic Village is the best analogy because it stays behind after the competition is over to be useful to the local region. So as we host our 25th year of RoboCupRescue in Brazil, we add to our world map of standard test facilities and new friends that we can continue to collaborate with professionally, and hopefully help their regional emergency responders use robots to stay out of harm’s way.

About Adam

Adam Jacoff is a robotics research engineer at the National Institute of Standards and Technology (NIST) which is part of the U.S. Department of Commerce. Over the past thirty years he has developed a variety of innovative robots and directed evaluations of more than a hundred others in a range of sizes, including the first technology readiness level assessment of autonomous mobility for the U.S. Army’s Experimental Unmanned Vehicle (XUV) (2002-2003).
 
His current efforts are focused toward developing 50 standard test methods to objectively evaluate ground robots, aerial drones, and aquatic vehicles for public safety operations. He has conducted 50 international robot competitions using the test methods as challenge tasks to guide innovation and measure progress (2000-present), and more than a hundred robot exercises to refine and validate the test methods with emergency responders and robot manufacturers (2005-present). These include dozens of comprehensive robot evaluations using the test methods to quantify key capabilities guiding more than $200M of purchasing decisions for civilian and military organizations (2010-present). He is now validating use of the test methods as repeatable practice tasks to focus remote operator/pilot training and measure proficiency for credentialing (2015-present).  He received a B.S. degree in Mechanical Engineering from the University of Maryland and a M.S. degree in Computer Science from Johns Hopkins University.
]]> Gearing up for RoboCupJunior: Interview with Ana Patrícia Magalhães https://robohub.org/gearing-up-for-robocupjunior-interview-with-ana-patricia-magalhaes/ Wed, 18 Jun 2025 09:20:05 +0000 https://aihub.org/?p=17845 Action from RoboCupJunior Rescue at RoboCup 2024. Photo: RoboCup/Bart van Overbeeke.

The annual RoboCup event, where teams gather from across the globe to take part in competitions across a number of leagues, will this year take place in Brazil, from 15-21 July. An important part of the week is RoboCupJunior, which is designed to introduce RoboCup to school children, and sees hundreds of kids taking part in a variety of challenges across different leagues. This year, the lead organizer for RoboCupJunior is Ana Patrícia Magalhães. We caught up with her to find out how the preparations are going, what to expect at this year’s competition, and how RoboCup inspires communities.

Could you tell us about RoboCupJunior and the plans you have for the competition this year?

RoboCup will take place from 15-21 July, in Salvador, Brazil. We expect to receive people from more than 40 countries, across the Junior and Major Leagues. We are preparing everything to accommodate all the students taking part in RoboCupJunior, who will participate in the Junior Leagues of Soccer, Rescue and OnStage. They are children and teenagers, so we have organized shuttles to take them from the hotels to the convention center. We’ve also prepared a handbook with recommendations about security, places they can visit, places to eat. The idea is to provide all the necessary support for them, because they are so young. We’re also organizing a welcome party for the Juniors so that they can experience a little bit of our culture. It will hopefully be a good experience for them.

The Juniors will be located on the first level of the mezzanine at the convention center. They will be separate from the Major Leagues, who will be on the ground floor. Of course, they’ll be able to visit the Major Leagues, and talk to the students and other competitors there, but it will be nice for them to have their own space. There will also be some parents and teachers with them, so we decided to use this special, dedicated space.

RoboCupJunior On Stage at RoboCup 2024. Photo: RoboCup/Bart van Overbeeke.

Do you have any idea of roughly how many teams will be taking part?

Yes, so we’ll have about 48 teams in the Soccer Leagues, 86 teams in the Rescue Leagues, and 27 in OnStage. That’s a lot of teams. Each team has about three or four students, and many of the parents, teachers and professors travel with them too. In total, we expect about 600 people to be associated with RoboCupJunior.

RoboCupJunior Soccer at RoboCup 2024. Photo: RoboCup/Bart van Overbeeke.

Have you got more RoboCupJunior participants from Brazil this year due to the location?

Yes, we have many teams from Brazil competing. I don’t know the exact number, but there are definitely more Brazilian teams this year, because it’s a lot cheaper and easier for them to travel here. When we have competitions in other countries, it’s expensive for them. For example, I have a team here in Salvador that qualified for the super regional event in the US and our team couldn’t go. They had qualified, but they couldn’t go because they didn’t have money to pay for the ticket. Now, it will be possible for all the Brazilian teams qualified to participate because it’s cheaper for them to come here. So it’s a big opportunity for development and to live the RoboCup experience. It’s very important for children and teenagers to share their research, meet people from other countries, and see what they are doing, and what research path they are following. They are very grateful for the opportunity to have their work tested against others. In a competition, it is possible to compare your research with others. So it’s different from conferences where you present a paper and show your work, but it’s not possible to compare and evaluate the results with other similar work. In a competition you have this opportunity. It’s a good way to get insights and improve your research.

RoboCupJunior Rescue at RoboCup 2024. Photo: RoboCup/Bart van Overbeeke.

Your role at this RoboCup will be organizing RoboCupJunior. Are you also involved in the Major Leagues?

Yes, so my main role is organizing RoboCupJunior and I am also one of the chairs of the RoboCup Symposium. Besides, some teams from my laboratory are competing in the Major leagues. My team participates in the @Home league, but I haven’t had much time to help them recently, with all the preparations for RoboCup2025. Our laboratory also has teams from the 3d Simulation Soccer League, and the Flying Robots Demo. This will be the first time we’ll see a flying robot demo league at a RoboCup.

We’ll also have two junior teams from the Rescue Simulation League. They are very excited about taking part.

RoboCupJunior Rescue at RoboCup 2024. Photo: RoboCup/Bart van Overbeeke.

RoboCup was last held in Brazil in 2014, and I understand that there were quite a lot of new people that were inspired to join a team after that. Do you think the 2025 RoboCup will have the same effect and will inspire more people in Brazil to take part?

Yes, I hope so. The last one inspired many, many students. We could perceive the difference before and after RoboCup at that time, related to projects in schools. In 2014, RoboCup was held in João Pessoa, a city in the north east that is not as developed or populated as many other states in Brazil. It really improved the research in that place and the interest in robotics especially. After the 2014 RoboCup, we’ve had many projects submitted to the Brazilian RoboCup competition from that state every year. We believe that it was because of RoboCup being held there.

We hope that RoboCup2025 next month will have the same effect. We think it might have an even bigger impact, because there is more social media now and the news can spread a lot further. We are expecting many visitors. We will have a form where schools that want to visit can enroll on a guided visit of RoboCup. This will go live on the website next week, but we are already receiving many messages from schools asking how they can participate with their group. They are interested in the events, so we have high expectations.

We have been working on organizing RoboCup2025 for over a year, and there is still much to do. We are excited to receive everybody here, both for the competition and to see the city. We have a beautiful city on the coast, and some beautiful places to visit, so I recommend that people come and stay for some days after the competition to get to know our city.

About Ana Patrícia

Ana Patrícia F. Magalhães Mascarenhas received her PhD in Computer Science from the Federal University of Bahia (2016) and Master in Mechatronics from the Federal University of Bahia (2007). She is currently an adjunct professor at the State University of Bahia (UNEB) at the Information Systems course. She is a researcher and vice coordinator of the Center for Research in Computer Architecture, Intelligent Systems and Robotics (ACSO). Her current research focuses on service robotics and software engineering, especially related to the use of Artificial Intelligence (AI) in the software development process and in Model-Driven Development (DDM).
]]> Preparing for kick-off at RoboCup2025: an interview with General Chair Marco Simões https://robohub.org/preparing-for-kick-off-at-robocup2025-an-interview-with-general-chair-marco-simoes/ Thu, 12 Jun 2025 09:30:56 +0000 https://aihub.org/?p=17798 The Salvador Convention Center, where RoboCup 2025 will take place.

RoboCup is an international scientific initiative with the goal of advancing the state of the art of intelligent robots, AI and automation. The annual RoboCup event, where teams gather from across the globe to take part in competitions across a number of leagues, will this year take place in Brazil, from 15-21 July. We spoke to Marco Simões, one of the General Chairs of RoboCup 2025 and President of RoboCup Brazil, to find out what plans they have for the event, some new initiatives, and how RoboCup has grown in Brazil over the past ten years.

Marco Simões

Could you give us a quick introduction to RoboCup 2025?

RoboCup will be held in Salvador, Brazil. When RoboCup was held in Brazil 11 years ago, in 2014, we had a total of 100,000 visitors, so that was a great success. This year, we expect even more, around 150,000, during all the events. Nowadays, AI and robotics are attracting more attention. We are also in a town, Salvador, with a bigger population than the previous location (João Pessoa). For these reasons, we estimate the attendance to be about 150,000 people.

Regarding the number of teams, registration has not closed yet, so we’re unsure about the final numbers. However, we expect to have about 300-400 teams and around 3000 competitors. We have been helping with visas, so we hope to see higher participation from teams who couldn’t attend in the previous two years due to visa issues. We are doing our best to ensure people can come and have fun at RoboCup!

This is also a great year for the RoboCup community: We have just agreed on new global league partners, including the Chinese companies Unitree, Fourier, and Booster Robotics. They will bring their humanoids and four-legged robots to RoboCup. These will not only be exhibited to the public but also used by some teams. They are amazing robots with very good skills. So, I think this will be an amazing edition of RoboCup this year.

Did the 2014 event in Brazil inspire more teams to participate in RoboCup?

Yes, we have seen a significant increase in our RoboCup community. In the last two years, Brazil has had the fourth-largest number of teams and participants at RoboCup in Bordeaux (2023) and Eindhoven (2024). This was a very big increase because ten years ago, we were not even in the top eight or nine.

We’ve made a significant effort with RoboCupJunior in the last ten years. Most people who’ve taken part in RoboCupJunior have carried on and joined the RoboCup Major League. So, the number of teams in Brazil has been increasing year by year over the last ten years. This year, we have a great number of participants because of the lower travel costs. We are expecting to be in the top three this year in terms of the highest number of participants.

Photo of participants at RoboCup 2024, which took place in Eindhoven. Photo credit: RoboCup/Bart van Overbeeke

It’s impressive that so many RoboCupJunior participants go on to join a Major League team.

Yes, we have an initiative here in Brazil called the Brazilian Robotics Olympiad. In this event, we chose two Junior Leagues – OnStage and the Rescue Line League – and we organized a competition based on these two Leagues. We run it in regional competitions all over Brazil – so 27 states. We organize at least one competition in each state during the year, and the best teams from each state come to the national competition together with the Major Leagues. We organize the Brazilian Olympiad to get RoboCupJunior to more students. This is how we’ve managed to increase participation in RoboCupJunior. Then, when students go to university, many of them continue to participate, but in the Major Leagues. So that’s a very successful strategy we’ve used in Brazil in the last 10 years.

Could you tell us about some more of the highlights from the Brazilian RoboCup community in recent years?

Two or three years ago, one of the Brazilian teams was the champion of RoboCup @Home. We have seen a big increase in the number of teams in the @Home League. In the national competition in Brazil, we have more than 12 teams participating. Back in 2014, we only had one team participating. So we’ve had a great increase—this League is one of the highlights in Brazil.

More teams are also participating in the Small Size League (part of the soccer League). Two years ago, one of the Brazilian teams was the champion of the division B of the Small Size League. So, over the last five years, we’ve seen some Brazilian teams in the top three positions in Major Leagues in the RoboCup world competition. This is a result of the increase in the number of teams and the quality of what the teams are developing. So at this time, we have an increased number of publications and teams participating in the competition with good results, so that’s very important.

Another excellent contribution for this year is a league we created five years ago – a flying robot league, where autonomous drones perform some missions and tasks. We’ve proposed this League as a demo for RoboCup2025, and we will have a Flying Robot Demo at the competition this year. This will be the first time we’ll have autonomous drones at the RoboCup competition, and the Brazilian community proposed it.

RoboCup @Home with Toyota HSR robots in the Domestic Standard Platform League, RoboCup 2024. Photo: RoboCup/Bart van Overbeeke.

Will you be taking part in the competition this year, or will you be focusing entirely on your role as General Chair?

This year, my laboratory (ACSO/Uneb) has qualified for the 3d Simulation League (soccer), the Flying Robot Demo, and RoboCup @Home, so we are participating in three Leagues. We also supervise RoboCupJunior Teams in the Rescue Simulation League. This year, my students have had only a little supervision from me because I’ve been very engaged with the organization.

In our 3D simulation team, we have lots of developments with deep reinforcement learning and some new novel strategies that allow our teams to gain new skills, and we are combining the new skills with our former multi-agent coordination strategy. For this reason, I think we will have a robust team in the competition because we are not only working on skills, we are also working on multi-agency strategies. When both aspects are joined, you can have a really good soccer team that plays very well. We have a good team and expect to achieve a greater position this year. In the latter years, we were in the top four or five, but we hope to get into the top three this year.

In 3D, you not only work on multi-agent aspects but also need to work on skills such as walking, kicking, and running. Teams are now trying to develop new skills. For example, in recent years, our team has developed the sprint running movement, which was a result of deep reinforcement learning. It is not a natural running motion but a running movement that works according to the League’s rules. It makes the robots go very fast from one point to another, making the team very competitive.

Most teams are learning skills but don’t know how to exploit them strategically in the game. Our focus is not only on creating new skills but also on using them strategically. We are currently working on a very innovative approach.

This year, the simulation league will run a challenge using a new simulator based on MuJoCo. If the challenge goes well, we may move to this new simulator in the following years, which can more realistically simulate real humanoid robots.

Action from the semi-finals of RoboCup Soccer Humanoid League at RoboCup 2024. Photo: RoboCup/Bart van Overbeeke.

Finally, is there anything else you’d like to highlight about RoboCup2025?

We are working on partnerships with local companies. For example, we have sponsorship from Petrobras, one of the biggest oil companies in the world. They will discuss how they are using robotics and AI in their industry. They were also one of the first sponsors of the Flying Robots League. It’s important to have these links between industry and the RoboCup community.

We also have excellent support from local companies and the government. They will be showing the community their latest developments. In the Rescue League, for example, we’ll have a demonstration from the local force showing what they do to support people in disaster situations.

This event is also an excellent opportunity for RoboCuppers, especially those who have never been to Brazil, to spend some days after the event in Salvador, visiting some tourist spots. Salvador was the first Brazilian capital, so we have a rich history. There are a lot of historical sites to see and some great entertainment options, such as beaches or parties. People can have fun and enjoy the country!

About Marco

Marco Simões is an Associate Professor at Bahia State University, Salvador, Brazil. He is the General Chair of RoboCup2025, and President of RoboCup Brazil.
]]> Interview with Jerry Tan: Service robot development for education https://robohub.org/interview-with-jerry-tan-service-robot-development-for-education/ Wed, 18 Sep 2024 08:51:06 +0000 https://robohub.org/?p=210914
At the International Joint Conference on Artificial Intelligence (IJCAI) 2023, I had the opportunity to interview Jerry Tan from Lattel Robotics, a company dedicated to promoting AI-focused robotics education and training. They work closely with the RoboCup@Home Education initiative, supporting schools and institutions in introducing AI and service robot development to students. Their goal is to equip learners with practical AI application skills in computer vision, autonomous navigation, object manipulation and speech interactions.

Through their AI robotics and AI applications workshops, Lattel Robotics offers an introduction to robot operating system (ROS)-based AI applications development in service robotics. As a hardware partner for the RoboCup@Home Education initiative, they assist schools and institutions in competing in AI robotic challenges by developing applications that address real-world problems. Their AI learning solutions include setting up AI laboratories, designing curriculums and developing courseware.

Andrea: Thank you for taking the time to speak with me. It’s a pleasure to meet you. Could you please tell me more about who can benefit from the Jupiter2 platform, and what exactly it is designed to do?

Jerry: Just about anyone can use it with a programming background to get started to develop their own AI applications via service robot development, using open source software.

Andrea: What kind of sensors do your robots use? Just microphones and cameras, or is there more?

Jerry: Yes, so we use a range of sensors, including RGB-D cameras and LIDAR sensors for depth perception. Besides, we have microphones and speakers for speech interaction, along with robotic arms for object manipulation and mobile platforms for navigation.

Andrea: So, I have a little question about this. Do you have an API (Application Programming Interface) that students and researchers can work through?

Jerry: It’s not always necessary, as the robot is equipped with its own laptop, acting as a central processing unit. Getting started is easier, as we have integrated the hardware platform with open-source software like OpenCV and YOLO, using the Robot Operating System (ROS1 and ROS2). These are popular tools among AI researchers and developers.

Andrea: It sounds intriguing, but the setup process seems complicated. Integration often takes time.

Jerry: The robotic platform is already integrated and ready to be deployed. For our AI learning device, Juno2, all you need to do is to connect it to a laptop via a USB port and you can straight away boot up the Linux (Ubuntu) environment with ROS, without any software installation.

AI learning device, Juno2.

Andrea: How do you work with schools?

Jerry: We’re an education solution provider, supporting schools and institutions that want to introduce practical AI applications learning in a classroom. Programming experience in Python or C++ is a prerequisite in doing AI applications development. So we provide Python introduction workshop for complete beginners. Then using a train-the-trainers approach, we offer 3-day AI robotics workshops using Jupiter2 or 2-day AI applications workshops using Juno2. Through these workshops, educators are given an overview of the AI applications modules which would enable them to customise the material accordingly for education, training or even research purposes.

Andrea: What if someone doesn’t have direct access to the robot? Can they work remotely?

Jerry: Absolutely. You can connect via a remote desktop software from your computer and continue programming. As mentioned just now, we also have a smaller device called Juno2, designed for classroom use. You don’t need the full robot setup in this case; you can use this device to do computer vision and speech interactions applications. It’s a plug-and-play solution that works with any laptop or operating system, automatically loading Ubuntu, ROS and our Jupiter software development frameworks. It’s perfect for conducting online classes, as students can continue learning from home or wherever they are.

Andrea: If you have a robot like this, can additional components, like speakers, be added to your robots?

Jerry: Yes, definitely. That’s a good question. Jupiter2 is an open platform that is
customisable and reconfigurable. If you have an engineering background, you can modify the hardware based on the requirements. This flexibility allows both beginners and advanced users to focus on their specific areas of interest, whether it is software programming or hardware customisation.

Andrea: Have you exported these gadgets before?

Jerry: Yes, we have exported to Europe, Africa, South and North America before. So far there are more than 30 over schools, universities and institutions globally that have adopted our learning platforms for education, training, research and/or robotic competitions.

Andrea: Fantastic, thank you for your time and insights. I wish you continued success in the future!

You can find more information on the Lattel Robotics website.

Jerry Tan is the Managing Director of Lattel Robotics. A German-trained engineer turned entrepreneur, Jerry is currently running the AI robotics education and training companies in Malaysia and Singapore. Striving to empower anyone in getting started to learn AI applications practically, Jerry works closely with schools and academic institutions in setting up laboratories and developing courseware.
]]> #RoboCup2024 – daily digest: 21 July https://robohub.org/robocup2024-daily-digest-21-july/ Sun, 21 Jul 2024 19:48:45 +0000 https://aihub.org/?p=15404 A break in play during a Small Size League match.

Today, 21 July, saw the competitions draw to a close in a thrilling finale. In the third and final of our round-up articles, we provide a flavour of the action from this last day. If you missed them, you can find our first two digests here: 19 July | 20 July.

My first port of call this morning was the Standard Platform League, where Dr Timothy Wiley and Tom Ellis from Team RedbackBots, RMIT University, Melbourne, Australia, demonstrated an exciting advancement that is unique to their team. They have developed an augmented reality (AR) system with the aim of enhancing the understanding and explainability of the on-field action.

The RedbackBots travelling team for 2024 (L-to-R: Murray Owens, Sam Griffiths, Tom Ellis, Dr Timothy Wiley, Mark Field, Jasper Avice Demay). Photo credit: Dr Timothy Wiley.

Timothy, the academic leader of the team explained: “What our students proposed at the end of last year’s competition, to make a contribution to the league, was to develop an augmented reality (AR) visualization of what the league calls the team communication monitor. This is a piece of software that gets displayed on the TV screens to the audience and the referee, and it shows you where the robots think they are, information about the game, and where the ball is. We set out to make an AR system of this because we think it’s so much better to view it overlaid on the field. What the AR lets us do is project all of this information live on the field as the robots are moving.”

The team has been demonstrating the system to the league at the event, with very positive feedback. In fact, one of the teams found an error in their software during a game whilst trying out the AR system. Tom said that they’ve received a lot of ideas and suggestions from the other teams for further developments. This is one of the first (if not, the first) AR system to be trialled across the competition, and first time it has been used in the Standard Platform League. I was lucky enough to get a demo from Tom and it definitely added a new level to the viewing experience. It will be very interesting to see how the system evolves.

Mark Field setting up the MetaQuest3 to use the augmented reality system. Photo credit: Dr Timothy Wiley.

From the main soccer area I headed to the RoboCupJunior zone, where Rui Baptista, an Executive Committee member, gave me a tour of the arenas and introduced me to some of the teams that have been using machine learning models to assist their robots. RoboCupJunior is a competition for school children, and is split into three leagues: Soccer, Rescue and OnStage.

I first caught up with four teams from the Rescue league. Robots identify “victims” within re-created disaster scenarios, varying in complexity from line-following on a flat surface to negotiating paths through obstacles on uneven terrain. There are three different strands to the league: 1) Rescue Line, where robots follow a black line which leads them to a victim, 2) Rescue Maze, where robots need to investigate a maze and identify victims, 3) Rescue Simulation, which is a simulated version of the maze competition.

Team Skollska Knijgia, taking part in the Rescue Line, used a YOLO v8 neural network to detect victims in the evacuation zone. They trained the network themselves with about 5000 images. Also competing in the Rescue Line event were Team Overengeniering2. They also used YOLO v8 neural networks, in this case for two elements of their system. They used the first model to detect victims in the evacuation zone and to detect the walls. Their second model is utilized during line following, and allows the robot to detect when the black line (used for the majority of the task) changes to a silver line, which indicates the entrance of the evacuation zone.

Left: Team Skollska Knijgia. Right: Team Overengeniering2.

Team Tanorobo! were taking part in the maze competition. They also used a machine learning model for victim detection, training on 3000 photos for each type of victim (these are denoted by different letters in the maze). They also took photos of walls and obstacles, to avoid mis-classification. Team New Aje were taking part in the simulation contest. They used a graphical user interface to train their machine learning model, and to debug their navigation algorithms. They have three different algorithms for navigation, with varying computational cost, which they can switch between depending on the place (and complexity) in the maze in which they are located.

Left: Team Tanorobo! Right: Team New Aje.

I met two of the teams who had recently presented in the OnStage event. Team Medic’s performance was based on a medical scenario, with the team including two machine learning elements. The first being voice recognition, for communication with the “patient” robots, and the second being image recognition to classify x-rays. Team Jam Session’s robot reads in American sign language symbols and uses them to play a piano. They used the MediaPipe detection algorithm to find different points on the hand, and random forest classifiers to determine which symbol was being displayed.

Left: Team Medic Bot Right: Team Jam Session.

Next stop was the humanoid league where the final match was in progress. The arena was packed to the rafters with crowds eager to see the action.
Standing room only to see the Adult Size Humanoids.

The finals continued with the Middle Size League, with the home team Tech United Eindhoven beating BigHeroX by a convincing 6-1 scoreline. You can watch the livestream of the final day’s action here.

The grand finale featured the winners of the Middle Size League (Tech United Eindhoven) against five RoboCup trustees. The humans ran out 5-2 winners, their superior passing and movement too much for Tech United.

]]> #RoboCup2024 – daily digest: 20 July https://robohub.org/robocup2024-daily-digest-20-july/ Sat, 20 Jul 2024 18:34:07 +0000 https://aihub.org/?p=15402 The Standard Platform Soccer League in action.

This is the second of our daily digests from RoboCup2024 in Eindhoven, The Netherlands. If you missed the first digest, which gives some background to RoboCup, you can find it here.

Competitions continued across all the leagues today, with participants vying for a place in Sunday’s finals.

The RoboCup@Work league focusses on robots in work-related scenarios, utilizing ideas and concepts from other RoboCup competitions to tackle open research challenges in industrial and service robotics.

I arrived at the arena in time to catch the advanced navigation test. Robots have to autonomously navigate, picking up and placing objects at different work stations. In this advanced test, caution tape is added to the arena floor, which the robots should avoid travelling over. There is also a complex placing element where teams have to put an object that they’ve collected into a slot – get the orientation or placement of the object slightly wrong and the it won’t fall into the slot.

The RoboCup@Work arena just before competition start.

Eight teams are taking part in the league this year. Executive Committee member Asad Norouzi said that there are plans to introduce a sub-league which would provide an entry point for new teams or juniors to get into the league proper.

I caught up with Harrison Burns, Mitchell Torok and Jasper Arnold from Team MiRobot. They are based at the University of New South Wales and are attending RoboCup for the first time.

Team MiRobot from UNSW.

The team actually only started six months ago, so final preparations have been a bit stressful. However, the experience has been great fun, and the competition has gone well so far. Like most teams, they’ve had to make many refinements as the competition has progressed, leading to some late nights.

One notable feature of the team’s robot is the bespoke, in-house-designed grasping mechanism on the end of the arm. The team note that “it has good flexible jaws, so when it grabs round objects it actually pulls the object directly into it. Because it uses a linear motion, compared to a lot of other rotating jaws, it has a lot better reliability for picking up objects”.

Here is some footage from the task, featuring Team bi-t-bots and Team Singapore.

In the Middle Size Soccer league (MSL), teams of five fully autonomous robots play with a regular size FIFA ball. Teams are free to design their own hardware but all sensors have to be on-board and there is a maximum size and weight limit of 40kg for the robots. The research focus is on mechatronics design, control and multi-agent cooperation at plan and perception levels. Nine teams are competing this year.

I spoke to António Ribeiro, who is a member of the technical committee and part of Team LAR@MSL from the University of Minho, Portugal. The team started in 1998, but António and most of his colleagues on the current team have only been involved in the MSL since September 2022. The robots have evolved as the competition has progressed, and further improvements are in progress. Refinements so far have included communication, the detection system, and the control system. They are pleased with the improvements from the previous RoboCup. “Last year we had a lot of hardware issues, but this year the hardware seems pretty stable. We also changed our coding architecture and it is now much easier and faster for us to develop code because we can all work on the code at the same time on different modules”.

António cited versatility and cost-effective solutions as strengths of the team. “Our robot is actually very cheap compared to other teams. We use a lot of old chassis, and our solutions always go to the lowest cost possible. Some teams have multiple thousand dollar robots, but, for example, our vision system is around $70-80. It works pretty well – we need to improve the way we handle it, but it seems stable”.

Team LAR@MSL

The RoboCup@Home league aims to develop service and assistive robot technology with high relevance for future personal domestic applications. A set of benchmark tests is used to evaluate the robots’ abilities and performance in a realistic non-standardized home environment setting. These tests include helping to prepare breakfast, clearing the table, and storing groceries.

I arrived in time to watch the “stickler for the rules” challenge, where robots have to navigate different rooms and make sure that the people inside (“guests” at a party) are sticking to four rules: 1) there is one forbidden room – if a guest is in there the robot must alert them and ask them to follow it into another room), 2) everyone must have a drink in their hand – if not, the robot directs them to a shelf with drinks, 3) no shoes to be worn in the house, 4) there should be no rubbish left on the floor.

After watching an attempt from the LAR@Home robot, Tiago from the team told me a bit about the robot. “The goal is to develop a robot capable of multi general-purpose tasks in home and healthcare environments.” With the exception of the robotic arm, all of the hardware was built by the team. The robot has two RGBD cameras, two LIDARs, a tray (where the robot can store items that it needs to carry), and two emergency stop buttons that deactivate all moving parts. Four omnidirectional wheels allow the robot to move in any direction at any time. The wheels have independent suspension systems which guarantees that they can all be on the ground at all times, even if there are bumps and cables on the venue floor. There is a tablet that acts as a visual interface, and a microphone and speakers to enable communication between humans and the robot, which is all done via speaking and listening.

Tiago told me that the team have talked to a lot healthcare practitioners to find out the main problems faced by elderly people, and this inspired one of their robot features. “They said that the two main injury sources are from when people are trying to sit down or stand up, and when they are trying to pick something up from the floor. We developed a torso that can pick objects from the floor one metre away from the robot”.

The LAR@Home team.

You can keep up with the latest news direct from RoboCup here.

Click here to see all of our content pertaining to RoboCup.

]]> #RoboCup2024 – daily digest: 19 July https://robohub.org/robocup2024-daily-digest-19-july/ Fri, 19 Jul 2024 16:10:00 +0000 https://aihub.org/?p=15398 The main soccer arena.

RoboCup is an international scientific initiative with the goal to advance the state of the art of intelligent robots. As part of this initiative, a series of competitions and events are held throughout the year. The main showcase event is an international affair with teams travelling from far and wide to put their machines through their paces.

This year, RoboCup is being held in three arenas in the Genneper Parken, Eindhoven, The Netherlands. The organisers are expecting over 2,000 participants, from 45 different countries, with around 300 teams signed up to take part in the various competitions.

Although RoboCup started out as a football (or soccer) playing competition, other leagues have since been introduced, focussing on robots in industrial, rescue, and home settings. There is even a dedicated league for young roboticists – RoboCupJunior – where participants can take part in either football, rescue, or artistic events.

I am lucky enough to be able to attend this year, and, for the next three days, I’ll be bringing you a daily digest of some of the exciting happenings from Eindhoven.

Today, 19 July, sees the competition in full swing. The main soccer arena, boasting multiple pitches, hosts a number of the different leagues which form RoboCupSoccer.

Some of the pitches in the main soccer arena.

My first port of call was the Standard Platform League, where the round 5 champions cup match between SPQR Team vs rUNSWift was taking place. SPQR ran out winners and advance to round 6. In this league, all teams compete with identical robots (currently the humanoid NAO by Aldebaran). The robots operate fully autonomously, meaning that there is no external control from neither humans nor computers.

Standard platform league. Round 5 champions cup match between SPQR Team vs rUNSWift.

The Humanoid AdultSize league is arguably the most challenging of the leagues, with many constraints placed on the robots to make them as human-like as possible. For example, they must have roughly human-like body proportions, they need to walk on two legs, and they are only allowed to use human-like sensors (up to two cameras to sense the environment). In this AdultSize competition, two robots from each team compete, and the team members walk behind the robots to catch them in case of a fall. Such a mishap could prove costly in terms of potential hardware damage.

Action from the Humanoid AdultSize League.

The RoboCup Rescue Robot League sees teams developing robotic systems with the goal of enabling emergency responders to perform extremely hazardous tasks from safer stand-off distances. During the competition, teams compete in a round-robin, putting their robots through their paces on a number of different challenges. The leading teams following this initial phase progress to the finals on Sunday. The tasks include navigating in complex environments, opening doors, and sensing. Teams may run the machines completely autonomously, or with some assistive control. More points are awarded for completely autonomous operation.

RoboCup Rescue arena from above.

You can keep up with more RoboCup2024 news here.

]]> Interview with Marek Šuppa: insights into RoboCupJunior https://robohub.org/interview-with-marek-suppa-insights-into-robocupjunior/ Mon, 16 Oct 2023 09:30:22 +0000 https://robohub.org/?p=208492 robocupjunior soccer match in actionA RoboCupJunior soccer match in action.

In July this year, 2500 participants congregated in Bordeaux for RoboCup2023. The competition comprises a number of leagues, and among them is RoboCupJunior, which is designed to introduce RoboCup to school children, with the focus being on education. There are three sub-leagues: Soccer, Rescue and OnStage.

Marek Šuppa serves on the Executive Committee for RoboCupJunior, and he told us about the competition this year and the latest developments in the Soccer league.

What is your role in RoboCupJunior and how long have you been involved with this league?

I started with RoboCupJunior quite a while ago: my first international competition was in 2009 in Graz, where I was lucky enough to compete in Soccer for the first time. Our team didn’t do all that well in that event but RoboCup made a deep impression and so I stayed around: first as a competitor and later to help organise the RoboCupJunior Soccer league. Right now I am serving as part of the RoboCupJunior Execs who are responsible for the organisation of RoboCupJunior as a whole.

How was the event this year? What were some of the highlights?

I guess this year’s theme or slogan, if we were to give it one, would be “back to normal”, or something like that. Although RoboCup 2022 already took place in-person in Thailand last year after two years of a pandemic pause, it was in a rather limited capacity, as COVID-19 still affected quite a few regions. It was great to see that the RoboCup community was able to persevere and even thrive throughout the pandemic, and that RoboCup 2023 was once again an event where thousands of robots and roboticists meet.

It would also be difficult to do this question justice without thanking the local French organisers. They were actually ready to organise the event in 2020 but it got cancelled due to COVID-19. But they did not give up on the idea and managed to put together an awesome event this year, for which we are very thankful.

robots used for robocupjuniorExamples of the robots used by the RoboCupJunior Soccer teams.

Turning to RoboCupJunior Soccer specifically, could you talk about the mission of the league and how you, as organisers, go about realising that mission?

The mission of RoboCupJunior consists of two competing objectives: on one hand, it needs to be a challenge that’s approachable, interesting and relevant for (mostly) high school students and at the same time it needs to be closely related to the RoboCup “Major” challenges, which are tackled by university students and their mentors. We are hence continuously trying to both make it more compelling and captivating for the students and at the same time ensure it is technical enough to help them grow towards the RoboCup “Major” challenges.

One of the ways we do that is by introducing what we call “SuperTeam” challenges, in which teams from respective countries form a so-called “SuperTeam” and compete against another “SuperTeam” as if these were distinct teams. In RoboCupJunior Soccer the “SuperTeams” are composed of four to five teams and they compete on a field that is six times larger than the “standard” fields that are used for the individual games. While in the individual matches each team can play with two robots at most (resulting in a 2v2 game) in a SuperTeam match each SuperTeam fields five robots, meaning there are 10 robots that play on the SuperTeam field during a SuperTeam match. The setup is very similar to the Division B of the Small Size League of RoboCup “Major”.

The SuperTeam games have existed in RoboCupJunior Soccer since 2013, so for quite a while, and the feedback we received on them was overwhelmingly positive: it was a lot of fun for both the participants as well as the spectators. But compared to the Small Size League games there were still two noticeable differences: the robots did not have a way of communicating with one another and additionally, the referees did not have a way of communicating with the robots. The result was that not only was there little coordination among robots of the same SuperTeam, whenever the game needed to be stopped, the referees had to physically run after the robots on the field to catch them and do a kickoff after a goal was scored. Although hilarious, it’s far from how we would imagine the SuperTeam games to look.

The RoboCupJunior Soccer Standard Communication Modules aim to do both. The module itself is a small device that is attached to each robot on the SuperTeam field. These devices are all connected via Bluetooth to a single smartphone, through which the referee can send commands to all robots on the field. The devices themselves also support direct message exchange between robots on a single SuperTeam, meaning the teams do not have to invest into figuring out how to communicate with the other robots but can make use of a common platform. The devices, as well as their firmware, are open source, meaning not only that everyone can build their own Standard Communication Module if they’d like but also that the community can participate in its development, which makes it an interesting addition to RoboCupJunior Soccer.

two teams setting up their robotsRoboCupJunior Soccer teams getting ready for the competition.

How did this new module work out in the competition? Did you see an improvement in experience for the teams and organisers?

In this first big public test we focused on exploring how (and whether) these modules can improve the gameplay – especially the “chasing robots at kickoff”. Although we’ve done “lab experiments” in the past and had some empirical evidence that it should work rather well, this was the first time we tried it in a real competition.

All in all, I would say that it was a very positive experiment. The modules themselves did work quite well and for some of us, who happened to have experience with “robot chasing” mentioned above, it was sort of a magical feeling to see the robots stop right on the main referee’s whistle.

We also found out potential areas for improvement in the future. The modules themselves do not have a power source of their own and were powered by the robots themselves. We didn’t think this would be a problem but in the “real world” test it transpired that the voltage levels the robots are capable of providing fluctuates significantly – for instance when the robot decides to aggressively accelerate – which in turn means some of the modules disconnect when the voltage is lowered significantly. However, it ended up being a nice lesson for everyone involved, one that we can certainly learn from when we design the next iterations.


The livestream from Day 4 of RoboCupJunior Soccer 2023. This stream includes the SuperTeam finals and the technical challenges. You can also view the livestream of the semifinals and finals from day three here.

Could you tell us about the emergence of deep-learning models in the RoboCupJunior leagues?

This is something we started to observe in recent years which surprised us organisers, to some extent. In our day-to-day jobs (that is, when we are not organising RoboCup), many of us, the organisers, work in areas related to robotics, computer science, and engineering in general – with some of us also doing research in artificial intelligence and machine learning. And while we always thought that it would be great to see more of the cutting-edge research being applied at RoboCupJunior, we always dismissed it as something too advanced and/or difficult to set up for the high school students that comprise the majority of RoboCupJunior students.

Well, to our great surprise, some of the more advanced teams have started to utilise methods and technologies that are very close to the current state-of-the-art in various areas, particularly computer vision and deep learning. A good example would be object detectors (usually based on the YOLO architecture), which are now used across all three Junior leagues: in OnStage to detect various props, robots and humans who perform on the stage together, in Rescue to detect the victims the robots are rescuing and in Soccer to detect the ball, the goals, and the opponents. And while the participants generally used an off-the-shelf implementations, they still needed to do all the steps necessary for a successful deployment of this technology: gather a dataset, finetune the deep-learning model and deploy it on their robots – all of which is far from trivial and is very close to how these technologies get used in both research and industry.

Although we have seen only the more advanced teams use deep-learning models at RoboCupJunior, we expect that in the future we will see it become much more prevalent, especially as the technology and the tooling around it becomes more mature and robust. It does show, however, that despite their age, the RoboCupJunior students are very close to cutting-edge research and state-of-the-art technologies.

Two teams ready to start - robots on the fieldAction from RoboCupJunior Soccer 2023.

How can people get involved in RCJ (either as a participant or an organiser?)

A very good question!

The best place to start would be the RoboCupJunior website where one can find many interesting details about RoboCupJunior, the respective leagues (such as Soccer, Rescue and OnStage), and the relevant regional representatives who organise regional events. Getting in touch with a regional representative is by far the easiest way of getting started with RoboCup Junior.

Additionally, I can certainly recommend the RoboCupJunior forum, where many RoboCupJunior participants, past and present, as well as the organisers, discuss many related topics in the open. The community is very beginner friendly, so if RoboCupJunior sounds interesting, do not hesitate to stop by and say hi!

About Marek Šuppa

Marek Suppa

Marek stumbled upon AI as a teenager when building soccer-playing robots and quickly realised he is not smart enough to do all the programming by himself. Since then, he’s been figuring out ways to make machines learn by themselves, particularly from text and images. He currently serves as the Principal Data Scientist at Slido (part of Cisco), improving the way meetings are run around the world. Staying true to his roots, he tries to provide others with a chance to have a similar experience by organising the RoboCupJunior competition as part of the Executive Committee.

]]> #IROS2023 awards finalists and winners + IROS on Demand free for one year https://robohub.org/iros2023-awards-finalists-and-winners-iros-on-demand-free-for-one-year/ Thu, 12 Oct 2023 11:09:17 +0000 https://robohub.org/?p=208437

Credits: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)

Did you have the chance to attend the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023) in Detroit? Here we bring you the papers that received an award this year in case you missed them. And good news: you can read all the papers because IROS on Demand is open to the public and freely available for one year from Oct 9th. Congratulations to all the winners and finalists!

IROS 2023 Best Overall and Best Student Paper

Winner of the IROS 2023 Best Paper

  • Autonomous Power Line Inspection with Drones via Perception-Aware MPC, by Jiaxu Xing, Giovanni Cioffi, Javier Hidalgo Carrio, Davide Scaramuzza.

Winner of the IROS 2023 Best Student Paper

  • Controlling Powered Prosthesis Kinematics over Continuous Transitions Between Walk and Stair Ascent, by Shihao Cheng, Curt A. Laubscher, Robert D. Gregg.

Finalists

  • Learning Contact-Based State Estimation for Assembly Tasks, by Johannes Pankert, Marco Hutter.
  • Swashplateless-elevon Actuation for a Dual-rotor Tail-sitter VTOL UAV, by Nan Chen, Fanze Kong, Haotian Li, Jiayuan Liu, Ziwei Ye, Wei Xu, Fangcheng Zhu, Ximin Lyu, Fu Zhang.
  • Towards Legged Locomotion on Steep Planetary Terrain, by Giorgio Valsecchi, Cedric Weibel, Hendrik Kolvenbach, Marco Hutter.
  • Decentralized Swarm Trajectory Generation for LiDAR-based Aerial Tracking in Cluttered Environments, by Longji Yin, Fangcheng Zhu, Yunfan Ren, Fanze Kong, Fu Zhang.
  • Open-Vocabulary Affordance Detection in 3D Point Clouds, by Toan Nguyen, Minh Nhat Vu, An Vuong, Dzung Nguyen, Thieu Vo, Ngan Le, Anh Nguyen.
  • Discovering Symbolic Adaptation Algorithms from Scratch, by Stephen Kelly, Daniel Park, Xingyou Song, Mitchell McIntire, Pranav Nashikkar, Ritam Guha, Wolfgang Banzhaf, Kalyanmoy Deb, Vishnu Boddeti, Jie Tan, Esteban Real.
  • Parallel cell array patterning and target cell lysis on an optoelectronic micro-well device, by Chunyuan Gan, Hongyi Xiong, Jiawei Zhao, Ao Wang, Chutian Wang, Shuzhang Liang, Jiaying Zhang, Lin Feng.
  • FATROP: A Fast Constrained Optimal Control Problem Solver for Robot Trajectory Optimization and Control, by Lander Vanroye, Ajay Suresha Sathya, Joris De Schutter, Wilm Decré.
  • GelSight Svelte: A Human Finger-Shaped Single-Camera Tactile Robot Finger with Large Sensing Coverage and Proprioceptive Sensing, by Jialiang Zhao, Edward Adelson.
  • Shape Servoing of a Soft Object Using Fourier Series and a Physics-based Model, by Fouad Makiyeh, Francois Chaumette, Maud Marchal, Alexandre Krupa.

IROS Best Paper Award on Agri-Robotics sponsored by YANMAR

Winner

  • Visual, Spatial, Geometric-Preserved Place Recognition for Cross-View and Cross-Modal Collaborative Perception, by Peng Gao, Jing Liang, Yu Shen, Sanghyun Son, Ming C. Lin.

Finalists

  • Online Self-Supervised Thermal Water Segmentation for Aerial Vehicles, by Connor Lee, Jonathan Gustafsson Frennert, Lu Gan, Matthew Anderson, Soon-Jo Chung.
  • Relative Roughness Measurement based Real-time Speed Planning for Autonomous Vehicles on Rugged Road, by Liang Wang, Tianwei Niu, Shuai Wang, Shoukun Wang, Junzheng Wang.

IROS Best Application Paper Award sponsored by ICROS

Winner

  • Autonomous Robotic Drilling System for Mice Cranial Window Creation: An Evaluation with an Egg Model, by Enduo Zhao, Murilo Marques Marinho, Kanako Harada.

Finalists

  • Visuo-Tactile Sensor Enabled Pneumatic Device Towards Compliant Oropharyngeal Swab Sampling, by Shoujie Li, MingShan He, Wenbo Ding, Linqi Ye, xueqian WANG, Junbo Tan, Jinqiu Yuan, Xiao-Ping Zhang.
  • Improving Amputee Endurance over Activities of Daily Living with a Robotic Knee-Ankle Prosthesis: A Case Study, by Kevin Best, Curt A. Laubscher, Ross Cortino, Shihao Cheng, Robert D. Gregg.
  • Dynamic hand proprioception via a wearable glove with fabric sensors, by Lily Behnke, Lina Sanchez-Botero, William Johnson, Anjali Agrawala, Rebecca Kramer-Bottiglio.
  • Active Capsule System for Multiple Therapeutic Patch Delivery: Preclinical Evaluation, by Jihun Lee, Manh Cuong Hoang, Jayoung Kim, Eunho Choe, Hyeonwoo Kee, Seungun Yang, Jongoh Park, Sukho Park.

IROS Best Entertainment and Amusement Paper Award sponsored by JTCF

Winner

  • DoubleBee: A Hybrid Aerial-Ground Robot with Two Active Wheels, by Muqing Cao, Xinhang Xu, Shenghai Yuan, Kun Cao, Kangcheng Liu, Lihua Xie.

Finalists

  • Polynomial-based Online Planning for Autonomous Drone Racing in Dynamic Environments, by Qianhao Wang, Dong Wang, Chao Xu, Alan Gao, Fei Gao.
  • Bistable Tensegrity Robot with Jumping Repeatability based on Rigid Plate-shaped Compressors, by Kento Shimura, Noriyasu Iwamoto, Takuya Umedachi.

IROS Best Industrial Robotics Research for Applications sponsored by Mujin Inc.

Winner

  • Toward Closed-loop Additive Manufacturing: Paradigm Shift in Fabrication, Inspection, and Repair, by Manpreet Singh, Fujun Ruan, Albert Xu, Yuchen Wu, Archit Rungta, Luyuan Wang, Kevin Song, Howie Choset, Lu Li.

Finalists

  • Learning Contact-Based State Estimation for Assembly Tasks, by Johannes Pankert, Marco Hutter.
  • Bagging by Learning to Singulate Layers Using Interactive Perception, by Lawrence Yunliang Chen, Baiyu Shi, Roy Lin, Daniel Seita, Ayah Ahmad, Richard Cheng, Thomas Kollar, David Held, Ken Goldberg.
  • Exploiting the Kinematic Redundancy of a Backdrivable Parallel Manipulator for Sensing During Physical Human-Robot Interaction, by Arda Yigit, Tan-Sy Nguyen, Clement Gosselin.

IROS Best Paper Award on Cognitive Robotics sponsored by KROS

Winner

  • Extracting Dynamic Navigation Goal from Natural Language Dialogue, by Lanjun Liang, Ganghui Bian, Huailin Zhao, Yanzhi Dong, Huaping Liu.

Finalists

  • EasyGaze3D: Towards Effective and Flexible 3D Gaze Estimation from a Single RGB Camera, by Jinkai Li, Jianxin Yang, Yuxuan Liu, ZHEN LI, Guang-Zhong Yang, Yao Guo.
  • Team Coordination on Graphs with State-Dependent Edge Cost, by Sara Oughourli, Manshi Limbu, Zechen Hu, Xuan Wang, Xuesu Xiao, Daigo Shishika.
  • Is Weakly-supervised Action Segmentation Ready For Human-Robot Interaction? No, Let’s Improve It With Action-union Learning, by Fan Yang, Shigeyuki Odashima, Shochi Masui, Shan Jiang.
  • Exploiting Spatio-temporal Human-object Relations using Graph Neural Networks for Human Action Recognition and 3D Motion Forecasting, by Dimitrios Lagamtzis, Fabian Schmidt, Jan Reinke Seyler, Thao Dang, Steffen Schober.

IROS Best Paper Award on Mobile Manipulation sponsored by OMRON Sinic X Corp.

Winner

  • A perching and tilting aerial robot for precise and versatile power tool work on vertical walls, by Roman Dautzenberg, Timo Küster, Timon Mathis, Yann Roth, Curdin Steinauer, Gabriel Käppeli, Julian Santen, Alina Arranhado, Friederike Biffar, Till Kötter, Christian Lanegger, Mike Allenspach, Roland Siegwart, Rik Bähnemann.

Finalists

  • Placing by Touching: An empirical study on the importance of tactile sensing for precise object placing, by Luca Lach, Niklas Wilhelm Funk, Robert Haschke, Séverin Lemaignan, Helge Joachim Ritter, Jan Peters, Georgia Chalvatzaki.
  • Efficient Object Manipulation Planning with Monte Carlo Tree Search, by Huaijiang Zhu, Avadesh Meduri, Ludovic Righetti.
  • Sequential Manipulation Planning for Over-actuated UAMs, by Yao Su, Jiarui Li, Ziyuan Jiao, Meng Wang, Chi Chu, Hang Li, Yixin Zhu, Hangxin Liu.
  • On the Design of Region-Avoiding Metrics for Collision-Safe Motion Generation on Riemannian Manifolds, by Holger Klein, Noémie Jaquier, Andre Meixner, Tamim Asfour.

IROS Best RoboCup Paper Award sponsored by RoboCup Federation

Winner

  • Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidance, by Hongzhan Yu, Chiaki Hirayama, Chenning Yu, Sylvia Herbert, Sicun Gao.

Finalists

  • Anytime, Anywhere: Human Arm Pose from Smartwatch Data for Ubiquitous Robot Control and Teleoperation, by Fabian Clemens Weigend, Shubham Sonawani, Drolet Michael, Heni Ben Amor.
  • Effectively Rearranging Heterogeneous Objects on Cluttered Tabletops, by Kai Gao, Justin Yu, Tanay Sandeep Punjabi, Jingjin Yu.
  • Prioritized Planning for Target-Oriented Manipulation via Hierarchical Stacking Relationship Prediction, by Zewen Wu, Jian Tang, Xingyu Chen, Chengzhong Ma, Xuguang Lan, Nanning Zheng.

IROS Best Paper Award on Robot Mechanisms and Design sponsored by ROBOTIS

Winner

  • Swashplateless-elevon Actuation for a Dual-rotor Tail-sitter VTOL UAV, by Nan Chen, Fanze Kong, Haotian Li, Jiayuan Liu, Ziwei Ye, Wei Xu, Fangcheng Zhu, Ximin Lyu, Fu Zhang.

Finalists

  • Hybrid Tendon and Ball Chain Continuum Robots for Enhanced Dexterity in Medical Interventions, by Giovanni Pittiglio, Margherita Mencattelli, Abdulhamit Donder, Yash Chitalia, Pierre Dupont.
  • c^2: Co-design of Robots via Concurrent-Network Coupling Online and Offline Reinforcement Learning, by Ci Chen, Pingyu Xiang, Haojian Lu, Yue Wang, Rong Xiong.
  • Collision-Free Reconfiguration Planning for Variable Topology Trusses using a Linking Invariant, by Alexander Spinos, Mark Yim.
  • eViper: A Scalable Platform for Untethered Modular Soft Robots, by Hsin Cheng, Zhiwu Zheng, Prakhar Kumar, Wali Afridi, Ben Kim, Sigurd Wagner, Naveen Verma, James Sturm, Minjie Chen.

IROS Best Paper Award on Safety, Security, and Rescue Robotics in memory of Motohiro Kisoi sponsored by IRSI

Winner

  • mCLARI: A Shape-Morphing Insect-Scale Robot Capable of Omnidirectional Terrain-Adaptive Locomotion, by Heiko Dieter Kabutz, Alexander Hedrick, William Parker McDonnell, Kaushik Jayaram.

Finalists

  • Towards Legged Locomotion on Steep, Planetary Terrain, by Giorgio Valsecchi, Cedric Weibel, Hendrik Kolvenbach, Marco Hutter.
  • Global Localization in Unstructured Environments using Semantic Object Maps Built from Various Viewpoints, by Jacqueline Ankenbauer, Parker C. Lusk, Jonathan How.
  • EELS: Towards Autonomous Mobility in Extreme Environments with a Novel Large-Scale Screw Driven Snake Robot, by Rohan Thakker, Michael Paton, Marlin Polo Strub, Michael Swan, Guglielmo Daddi, Rob Royce, Matthew Gildner, Tiago Vaquero, Phillipe Tosi, Marcel Veismann, Peter Gavrilov, Eloise Marteau, Joseph Bowkett, Daniel Loret de Mola Lemus, Yashwanth Kumar Nakka, Benjamin Hockman, Andrew Orekhov, Tristan Hasseler, Carl Leake, Benjamin Nuernberger, Pedro F. Proença, William Reid, William Talbot, Nikola Georgiev, Torkom Pailevanian, Avak Archanian, Eric Ambrose, Jay Jasper, Rachel Etheredge, Christiahn Roman, Daniel S Levine, Kyohei Otsu, Hovhannes Melikyan, Richard Rieber, Kalind Carpenter, Jeremy Nash, Abhinandan Jain, Lori Shiraishi, Ali-akbar Agha-mohammadi, Matthew Travers, Howie Choset, Joel Burdick, Masahiro Ono.
  • Multi-IMU Proprioceptive Odometry for Legged Robots, by Shuo Yang, Zixin Zhang, Benjamin Bokser, Zachary Manchester.
]]> Interview with Roberto Figueiredo: the RoboCup experience https://robohub.org/interview-with-roberto-figueiredo-the-robocup-experience/ Fri, 04 Aug 2023 10:54:26 +0000 https://robohub.org/?p=207916 Five people holding kid-sized robots
Roberto Figueiredo is a master’s student at the University of Aveiro. He is a member of the Bold Hearts RoboCup team which competes in the Humanoid KidSize soccer league. He is currently the local representative for the Junior Rescue Simulation. We spoke to Roberto about his RoboCup journey, from the junior to the major leagues, and his experience of RoboCup events.

When was your first RoboCup event and which competition did you take part in?

I started in 2016 in the Junior leagues with my high school and I took part in the rescue simulation competition (although I originally joined the on-stage competition). This first event actually happened in Portugal, and it was similar to a workshop. We qualified to go to the world cup in rescue simulation, in Leipzig, Germany, and we ended up in second place. That was really nice, and it was my first contact with RoboCup, and with robotics generally. I’d been working with electronics in the past, but simulation gave me a bit of an introduction to the more theoretical aspects of robotics, and to AI in general. Rescue simulation makes you think of ways to make the robots independent and not manually controlled by humans.

RoboCup team in front of the rescue setupRoberto’s first RoboCup in 2016, Leipzig, pictured with the Singapore team celebrating after the finals.

Could you tell us about the subsequent RoboCup events that you took part in?

In 2017 we qualified to go to Nagoya, Japan, which was not just an amazing RoboCup, but an amazing trip. That’s another good thing about robotics, you get to meet a lot of new people in new countries. We did quite well in this competition as well, I think we reached 5th place.

After that we went to European RoboCup Junior in Italy. The following year was my last RoboCup as a junior, which was in Sydney. That was also an interesting event and I got to chat a bit more with the majors and understand how their teams worked. By this point, I had gained more experience, and I felt ready to get involved with a major league RoboCup team.

There is a big gap between the junior and major leagues. When I joined my team (the Bold Hearts), most of the team were PhDs and I was just a second year bachelor’s student so it was quite hard to pick up all the knowledge. However, if you are persistent enough and you are interested in, and passionate about, robotics you’ll get the hang of it and you’ll learn by trial and error.

Seven people standing and one kneelingEuroRoboCup 2022 in Portugal. Roberto (kneeling in photo) was part of the organising committee.

When was your first competition with the team in the major league?

My first competition was actually last year, in Thailand. We didn’t perform as we would like to, however, there is much more to RoboCup than just the competition – it is now more of a scientific and knowledge-sharing event, it’s unique. Just this year, in Bordeaux, we had a problem with our robots. Every time we disconnected the ethernet cable, the robot just stopped playing, and we couldn’t figure out what was happening. I asked another team that was using the same software – they had figured out the problem before and they told us how to solve it. I don’t think you’ll see that in other competitions. Every team has a joint objective which is making science progress, making friendships, and making other teams better by sharing their knowledge. That’s really unique.

How did you join the Bold Hearts team?

I decided to do my master’s in the UK (at the University of Hertfordshire), to experience a different country and a different style of education. When I joined, I knew there was a team so I was already looking forward to joining. After a couple of years of work, we finally got to go to a competition as a team. It’s been an amazing time and a huge learning experience.

What is your role on the team?

In our team, everyone does a bit of everything. We still have a lot of problems to solve – on both the hardware and software side. All of us currently are computer scientists so it’s a bit more of a struggle to work on the hardware side. So, I do a bit of everything, both AI and non-AI related problems. For example, I’ve done some 3d modelling for the robots, and I’m currently working on the balancing problem. We all work together on the problems which is amazing because you get to see a bit of everything and learn from everyone. Robotics is a very multidisciplinary field. You get to learn about all kinds of topics: mechanical engineering, electrical engineering, machine learning, coding in general.

The Bold Hearts’ qualification video for this year’s RoboCup competition

Could you tell us about this year’s competition (which took place in Bordeaux)?

This year we were a lot more prepared than last year, when we’d just come back from COVID, and all of our experienced members had recently left the team, due to finishing their PhDs and starting work. Creating a successful robot team is a huge integration problem. There are so many pieces that need to go together and work perfectly for the robots to function, and if one fails it looks like your system isn’t doing anything. We got walking working perfectly this year, we had vision working well too, and we had a stable decision tree, and we were able to listen to the controller (which is like a referee and passes on information about fouls, game start and stops etc.). However, we had some bugs in the decision tree that made everything fall apart and we spent a lot of time trying to debug it. This happens to a lot of teams. However, you can still appreciate the work and progress of what they have done.

Five people holding kid-sized robotsRoboCup 2023 in Bordeaux. Roberto (left) with Bold Hearts teammates.

What are the immediate plans for the team?

We are now thinking about joining the simulation competition, which is part of our league. It takes place in the winter season and we’re planning on joining to work on our software. The transition between simulation and hardware is quite hard. You need a good simulation base to be able to transfer directly the knowledge to the robot. We’re working on having a very good simulation so we can transfer, at least more easily, the knowledge learnt in simulation to the robots.

RoboCup is moving more towards AI and learning, which we can see in the 3d simulation. The robots learn a lot of the motion through reinforcement learning, for example. In the physical leagues it’s not as easy as we have to transfer that to the real world, where there is play in the joints, there’s backlash, there’s play in the 3d parts – there are a lot of variables that are not taken into account in simulations.

How has being part of RoboCup inspired your studies and research?

Every time I go to RoboCup I come out thinking about what I’m going to do next. I couldn’t be more inspired. It’s a really intense field but I love it. It makes you want to work really hard and it makes you passionate about science. I did my bachelor’s project related to RoboCup, I joined a master’s course on robotics, I keep asking my Professors if they want to start a team back in Portugal. I’m going to do my master’s thesis on robotics, on humanoids. I think humanoids are a very complex and interesting challenge. There is no one single solution.

About Roberto

Roberto Figueiredo

Roberto Figueiredo is a Portuguese, AI-focused computer scientist with a bachelor’s degree from the University of Hertfordshire. He currently pursuing a master’s in Robotics and Intelligent Systems from the University of Aveiro, and is passionate about advancing his expertise in robotics. He has long been very enthusiastic about robots and AI, being a participant in RoboCup since 2016 in the Rescue Simulation league. He has since become local representative for the Rescue League in Portugal and joined a Major team, Bold Hearts, in the Kid Size league, one of the most challenging in RoboCup Humanoid Soccer.
]]> #RoboCup2023 in tweets – part 2 https://robohub.org/robocup2023-in-tweets-part-2/ Sun, 16 Jul 2023 09:31:52 +0000 https://robohub.org/?p=207790

As this year’s RoboCup draws to a close, we take a look back at some of the highlights from the second half of the conference. Over the course of the weekend, the event focussed on the latter stages of the competitions, with the winners in all the different leagues decided. If you missed our round-up of the first half of RoboCup, you can catch up here.

]]> #RoboCup2023 in tweets – part 1 https://robohub.org/robocup2023-in-tweets-part-1/ Sat, 15 Jul 2023 09:31:45 +0000 https://robohub.org/?p=207787

This year’s RoboCup kicked off on 4 July and will run until 10 July. Taking place in Bordeaux, the event will see around 2500 participants, from 45 different countries take part in competitions, training sessions, and a symposium. Find out what attendees have been up to in preparation for, and in the first half of, the event.

]]> What’s coming up at #RoboCup2023? https://robohub.org/whats-coming-up-at-robocup2023/ Mon, 03 Jul 2023 11:47:30 +0000 https://robohub.org/?p=207677 robocup2023 logo
This year, RoboCup will be held in Bordeaux, from 4-10 July. The event will see around 2500 participants, from 45 different countries take part in competitions, training sessions, and a symposium. You can see the schedule for the week here.

The leagues and their competitions

The league competitions will take place on 6-9 July. You can find out more about the different leagues at these links:

Symposium

The RoboCup symposium will take place on 10 July. The programme can be found here.

There will be three keynote talks:

  • Cynthia Breazeal, Social Robots: Reflections and Predictions of Our Future Relationship with Personal Robots
  • Ben Moran and Guy Lever, Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning
  • Laurence Devillers, Socio-affective robots: ethical issues

Find out more at the event website.

]]> A four-legged robotic system for playing soccer on various terrains https://robohub.org/a-four-legged-robotic-system-for-playing-soccer-on-various-terrains/ Wed, 05 Apr 2023 08:30:00 +0000 https://news.mit.edu/2023/legged-robotic-system-playing-soccer-various-terrains-0403

Researchers created DribbleBot, a system for in-the-wild dribbling on diverse natural terrains including sand, gravel, mud, and snow using onboard sensing and computing. In addition to these football feats, such robots may someday aid humans in search-and-rescue missions. Photo: Mike Grimmett/MIT CSAIL

By Rachel Gordon | MIT CSAIL

If you’ve ever played soccer with a robot, it’s a familiar feeling. Sun glistens down on your face as the smell of grass permeates the air. You look around. A four-legged robot is hustling toward you, dribbling with determination. 

While the bot doesn’t display a Lionel Messi-like level of ability, it’s an impressive in-the-wild dribbling system nonetheless. Researchers from MIT’s Improbable Artificial Intelligence Lab, part of the Computer Science and Artificial Intelligence Laboratory (CSAIL), have developed a legged robotic system that can dribble a soccer ball under the same conditions as humans. The bot used a mixture of onboard sensing and computing to traverse different natural terrains such as sand, gravel, mud, and snow, and adapt to their varied impact on the ball’s motion. Like every committed athlete, “DribbleBot” could get up and recover the ball after falling. 

Programming robots to play soccer has been an active research area for some time. However, the team wanted to automatically learn how to actuate the legs during dribbling, to enable the discovery of hard-to-script skills for responding to diverse terrains like snow, gravel, sand, grass, and pavement. Enter, simulation. 

A robot, ball, and terrain are inside the simulation — a digital twin of the natural world. You can load in the bot and other assets and set physics parameters, and then it handles the forward simulation of the dynamics from there. Four thousand versions of the robot are simulated in parallel in real time, enabling data collection 4,000 times faster than using just one robot. That’s a lot of data. 

Video: MIT CSAIL

The robot starts without knowing how to dribble the ball — it just receives a reward when it does, or negative reinforcement when it messes up. So, it’s essentially trying to figure out what sequence of forces it should apply with its legs. “One aspect of this reinforcement learning approach is that we must design a good reward to facilitate the robot learning a successful dribbling behavior,” says MIT PhD student Gabe Margolis, who co-led the work along with Yandong Ji, research assistant in the Improbable AI Lab. “Once we’ve designed that reward, then it’s practice time for the robot: In real time, it’s a couple of days, and in the simulator, hundreds of days. Over time it learns to get better and better at manipulating the soccer ball to match the desired velocity.” 

The bot could also navigate unfamiliar terrains and recover from falls due to a recovery controller the team built into its system. This controller lets the robot get back up after a fall and switch back to its dribbling controller to continue pursuing the ball, helping it handle out-of-distribution disruptions and terrains. 

“If you look around today, most robots are wheeled. But imagine that there’s a disaster scenario, flooding, or an earthquake, and we want robots to aid humans in the search-and-rescue process. We need the machines to go over terrains that aren’t flat, and wheeled robots can’t traverse those landscapes,” says Pulkit Agrawal, MIT professor, CSAIL principal investigator, and director of Improbable AI Lab.” The whole point of studying legged robots is to go terrains outside the reach of current robotic systems,” he adds. “Our goal in developing algorithms for legged robots is to provide autonomy in challenging and complex terrains that are currently beyond the reach of robotic systems.” 

The fascination with robot quadrupeds and soccer runs deep — Canadian professor Alan Mackworth first noted the idea in a paper entitled “On Seeing Robots,” presented at VI-92, 1992. Japanese researchers later organized a workshop on “Grand Challenges in Artificial Intelligence,” which led to discussions about using soccer to promote science and technology. The project was launched as the Robot J-League a year later, and global fervor quickly ensued. Shortly after that, “RoboCup” was born. 

Compared to walking alone, dribbling a soccer ball imposes more constraints on DribbleBot’s motion and what terrains it can traverse. The robot must adapt its locomotion to apply forces to the ball to  dribble. The interaction between the ball and the landscape could be different than the interaction between the robot and the landscape, such as thick grass or pavement. For example, a soccer ball will experience a drag force on grass that is not present on pavement, and an incline will apply an acceleration force, changing the ball’s typical path. However, the bot’s ability to traverse different terrains is often less affected by these differences in dynamics — as long as it doesn’t slip — so the soccer test can be sensitive to variations in terrain that locomotion alone isn’t. 

“Past approaches simplify the dribbling problem, making a modeling assumption of flat, hard ground. The motion is also designed to be more static; the robot isn’t trying to run and manipulate the ball simultaneously,” says Ji. “That’s where more difficult dynamics enter the control problem. We tackled this by extending recent advances that have enabled better outdoor locomotion into this compound task which combines aspects of locomotion and dexterous manipulation together.”

On the hardware side, the robot has a set of sensors that let it perceive the environment, allowing it to feel where it is, “understand” its position, and “see” some of its surroundings. It has a set of actuators that lets it apply forces and move itself and objects. In between the sensors and actuators sits the computer, or “brain,” tasked with converting sensor data into actions, which it will apply through the motors. When the robot is running on snow, it doesn’t see the snow but can feel it through its motor sensors. But soccer is a trickier feat than walking — so the team leveraged cameras on the robot’s head and body for a new sensory modality of vision, in addition to the new motor skill. And then — we dribble. 

“Our robot can go in the wild because it carries all its sensors, cameras, and compute on board. That required some innovations in terms of getting the whole controller to fit onto this onboard compute,” says Margolis. “That’s one area where learning helps because we can run a lightweight neural network and train it to process noisy sensor data observed by the moving robot. This is in stark contrast with most robots today: Typically a robot arm is mounted on a fixed base and sits on a workbench with a giant computer plugged right into it. Neither the computer nor the sensors are in the robotic arm! So, the whole thing is weighty, hard to move around.”

There’s still a long way to go in making these robots as agile as their counterparts in nature, and some terrains were challenging for DribbleBot. Currently, the controller is not trained in simulated environments that include slopes or stairs. The robot isn’t perceiving the geometry of the terrain; it’s only estimating its material contact properties, like friction. If there’s a step up, for example, the robot will get stuck — it won’t be able to lift the ball over the step, an area the team wants to explore in the future. The researchers are also excited to apply lessons learned during development of DribbleBot to other tasks that involve combined locomotion and object manipulation, quickly transporting diverse objects from place to place using the legs or arms.

The research is supported by the DARPA Machine Common Sense Program, the MIT-IBM Watson AI Lab, the National Science Foundation Institute of Artificial Intelligence and Fundamental Interactions, the U.S. Air Force Research Laboratory, and the U.S. Air Force Artificial Intelligence Accelerator. The paper will be presented at the 2023 IEEE International Conference on Robotics and Automation (ICRA).

]]> Robot Talk Episode 41 – Alessandra Rossi https://robohub.org/robot-talk-episode-41-alessandra-rossi/ Fri, 17 Mar 2023 14:21:06 +0000 https://robohub.org/?p=206848 Claire chatted to Alessandra Rossi from the University of Naples all about social robotics, theory of mind, and robots playing football.

Alessandra Rossi is Assistant Professor at the University of Naples Federico II in Italy. Her PhD thesis was part of the Marie Sklodowska-Curie ETN SECURE project at the University of Hertfordshire in the UK, and she is now a Visiting Lecturer and Researcher there. Her research interests include human-robot interaction, social robotics, explainable AI, multi-agent systems and user profiling. She is the team leader of RoboCup team Bold Hearts at the University of Hertfordshire, and Executive Committee member of the RoboCup Humanoid League.

]]> #IROS2022 best paper awards https://robohub.org/iros2022-best-paper-awards/ Mon, 14 Nov 2022 09:00:14 +0000 https://robohub.org/?p=205912

Did you have the chance to attend the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022) in Kyoto? Here we bring you the papers that received an award this year in case you missed them. Congratulations to all the winners and finalists!

Best Paper Award on Cognitive Robotics

Gesture2Vec: Clustering Gestures using Representation Learning Methods for Co-speech Gesture Generation. Payam Jome Yazdian, Mo Chen, and Angelica Lim.

Best RoboCup Paper Award

RCareWorld: A Human-centric Simulation World for Caregiving Robots. Ruolin Ye, Wenqiang Xu, Haoyuan Fu, Rajat Kumar, Jenamani, Vy Nguyen, Cewu Lu, Katherine Dimitropoulou, and Tapomayukh Bhattacharjee.

SpeedFolding: Learning Efficient Bimanual Folding of Garments. Yahav Avigal, Lars Burscheid, Tamim Asfour, Torsten Kroeger, and Ken Goldberg.

Best Paper Award on Robot Mechanisms and Design

Aerial Grasping and the Velocity Sufficiency Region. Tony G. Chen, Kenneth Hoffmann, JunEn Low, Keiko Nagami, David Lentink, and Mark Cutkosky.

Best Entertainment and Amusement Paper Award

Robot Learning to Paint from Demonstrations. Younghyo Park, Seunghun Jeon, and Taeyoon Lee.

Best Paper Award on Safety, Security, and Rescue Robotics

Power-based Safety Layer for Aerial Vehicles in Physical Interaction using Lyapunov Exponents. Eugenio Cuniato, Nicholas Lawrance, Marco Tognon, and Roland Siegwart.

Best Paper Award on Agri-Robotics

Explicitly Incorporating Spatial Information to Recurrent Networks for Agriculture. Claus Smitt, Michael Allan Halstead, Alireza Ahmadi, and Christopher Steven McCool.

Best Paper Award on Mobile Manipulation

Robot Learning of Mobile Manipulation with Reachability Behavior Priors. Snehal Jauhri, Jan Peters, and Georgia Chalvatzaki.

Best Application Paper Award

Soft Tissue Characterisation Using a Novel Robotic Medical Percussion Device with Acoustic Analysis and Neural Networks. Pilar Zhang Qiu, Yongxuan Tan, Oliver Thompson, Bennet Cobley, and Thrishantha Nanayakkara.

Best Paper Award for Industrial Robotics Research for Applications

Absolute Position Detection in 7-Phase Sensorless Electric Stepper Motor. Vincent Groenhuis, Gijs Rolff, Koen Bosman, Leon Abelmann, and Stefano Stramigioli.

ABB Best Student Paper Award

FAR Planner: Fast, Attemptable Route Planner using Dynamic Visibility Update. Fan Yang, Chao Cao, Hongbiao Zhu, Jean Oh, and Ji Zhang.

Best Paper Award

SpeedFolding: Learning Efficient Bimanual Folding of Garments. Yahav Avigal, Lars Berscheid, Tamim Asfour, Torsten Kroeger, and Ken Goldberg.

]]> RoboCup humanoid league: Interview with Jasper Güldenstein https://robohub.org/robocup-humanoid-league-interview-with-jasper-guldenstein/ Tue, 20 Sep 2022 07:22:53 +0000 https://robohub.org/?p=205558

RoboCup is an international scientific initiative with the goal of advancing the state of the art of intelligent robots, AI and automation. The annual RoboCup event returned to an in-person format for 2022, taking place from 13-17 July in Bangkok. RoboCup comprises a number of leagues, with perhaps the most well-known being the soccer leagues.

In the Humanoid League, autonomous robots with a human-inspired body plan and senses play soccer against each other. We spoke to Jasper Güldenstein, a member of the technical committee, about the competition at RoboCup 2022, and also about the Humanoid League Virtual Season. As a biweekly virtual competition taking place between the physical RoboCup events it provides teams with an opportunity to test their ideas and keep in touch throughout the year.

Could you give us an overview of the Humanoid League competition at RoboCup this year?

This year we had the first in-person event after a three year break. It was really good to have the majority of the teams coming back together and working on their robots again. Although a lot of teams came with fewer participants than they usually do, we still managed to have 12 teams in the kid size league and three teams in the adult size. Unfortunately, some teams could not participate due to travel restrictions, but we hope to see them back next year.

Humanoid teamsHumanoid league finalists with their robots

What was the format for the competition?

At the beginning we had a drop-in round, which is where we play games with one robot from each team participating in a joint team. So, we have eight robots playing from eight different teams. That is useful for finding out which teams are likely to perform the best in the competition. Next, the results from the drop-in games were used as seeding for the round robin phase of the regular competition, with the strongest teams separated into different groups. After the round robin, we have a knockout competition. The seeding method means that we can hopefully avoid the situation where very good teams get kicked out early. We saw that the most interesting games were towards the end of the competition when the teams performed really well.

Have you noticed improvements since the last physical competition?

I’d say definitely that one big thing that has improved for a lot of teams is the localisation. A lot of teams are more easily able to localise themselves on the field, and they don’t run off randomly. They are more certain that they are in the correct position.

Furthermore, I think the kicking has improved. The robots kick the ball much further than they used to. People have been tuning their kicking motions to increase the distance.

In terms of computer vision, this has definitely improved quite a bit. Something we did differently this time, which was inspired by what we did in the virtual season, is that we had a set of six different balls, all from previous FIFA competitions. For each game a ball was drawn randomly, so the teams couldn’t really prepare for all the balls. Although they were visually quite different, the teams didn’t really have any problems detecting the ball. We’ve seen, in general, that computer vision approaches have improved and these improvements have been transferred to the RoboCup competition. I think that almost all teams are using a neural network to detect the ball. This is a change from three, four, five years ago, where many teams used hand-tuned classical computer vision algorithms.

To talk a bit more about ball detection, it will be interesting to see what happens if we move to an environment with natural and/or varying light conditions. This year we were in a convention hall with uniform lighting. I believe next year, in Bordeaux, there is going to be some form of natural light coming in, and perhaps even fields that are outside. It’s still at the planning stage but we are looking forward to that. It will be a challenge and I strongly believe that the participants will find approaches to make their vision approach robust against these varying conditions.

Teams in actionThe setup and testing for the Humanoid League competition at RoboCup 2022, Bangkok.

Thinking about the transfer from the simulation to the real world, are there any specific elements that lend themselves well to being transferred?

In terms of computer vision, we had a bit of transfer. In the virtual season we concentrated a lot on changing the lighting conditions and having varied backgrounds, to be able to emulate the real world a bit better. I think a few teams used their vision approaches from the virtual season in the real world.

However, I think the strongest part is behaviour. Teams were able to test their strategies in the virtual competition and adapt every other week. For example, CIT Brains, which won the virtual season and the physical competition, made quite a few changes to their strategy and they had robust systems running. Their strategy worked really well, and in the final they managed to score several goals win against the previous world champions (Rhoban Football Club).

How did the competition go for your team (Hamburg Bit-Bots)?

We actually had quite a few hardware problems, especially on the mechanics side. The motors are wearing out and warped due to wear, and flexed more than we expected. This meant we had difficulties walking stably. And, if you can’t walk in a stable manner that defeats the purpose of everything else. It’s a really integrated system – if one component breaks, you are out of luck as you are very restricted in what you can change during the competition as you don’t have much spare equipment with you.

However, what was good for us was that we had a lot of software up and running, and a lot of it had been tested really well during the virtual league. We had to try and find a way round the walking problem algorithmically, to try to find walking parameters that were more stable. We also switched from [robot operating system] ROS 1 to ROS 2 which brought with it many challenges. We actually did a write up on the issues we faced, with some tricks and tips.

Will there be a new virtual league season this year?

Yes, we’ve discussed this in our technical committee and we plan on doing it again. The last event was successful and the teams enjoyed it. We plan on making some changes, such as logging the games to extract some interesting metrics and doing some analysis on those.

Another thing we want to do is domain randomisation, making the environment a bit more varied. This means that the approaches have to be more robust. The hope is that, when they are more robust, they can be transferred more easily to the real world. We were thinking about making the terrain slightly uneven. Another approach could be to modify the models of the robots such that the joints emulate a bit of wear, so they simulated actuators might be a bit weaker or stronger randomly, and teams have to find robust approaches to deal with that.

We won’t do everything at the beginning. We’ll move through the season and talk to the teams and form some organising groups to develop the simulation further, to run the games and to organise the competition itself. We are always happy to have input and we always talk to the teams to see what they think. It’s a competition but it’s something we build together.

Robots at the humanoid league BangkokHumanoid robots at RoboCup 2022, Bangkok.

Could you tell us about the satellite event that took place at RoboCup?

This was a discussion about how to get teams more interested in participating and how to bridge the junior leagues and the major leagues.

We know that some people who participated in RoboCup Junior specifically selected a University that has a RoboCup team so that they could join that team. It would be awesome for more people to do this, and for more juniors to know what the major league is about.

To bridge the gap between the junior and major leagues we don’t want to introduce another league, but we want some form of events where the two groups can meet, and where the juniors can show off how well they are doing. It would be good to have more interaction between the leagues, although we haven’t decided on the exact format yet.

About Jasper

Jasper

Jasper Güldenstein is a PhD student at University of Hamburg. His research focuses on humanoid navigation. He has been participating in RoboCup as a member of the Hamburg Bit-Bots since 2016 where his focus is developing not only the software of the team but also the electronics of the robot platform. In his recent master thesis he evaluated using reinforcement learning to perform path planning and execution for humanoid robots.

]]> Building a python toolbox for robot behavior https://robohub.org/building-a-python-toolbox-for-robot-behavior/ Sun, 07 Aug 2022 09:00:26 +0000 https://robohub.org/?p=205116

If you’ve been subject to my posts on Twitter or LinkedIn, you may have noticed that I’ve done no writing in the last 6 months. Besides the whole… full-time job thing … this is also because at the start of the year I decided to focus on a larger coding project.

At my previous job, I stood up a system for task and motion planning (TAMP) using the Toyota Human Support Robot (HSR). You can learn more in my 2020 recap post. While I’m certainly able to talk about that work, the code itself was closed in two different ways:

  1. Research collaborations with Toyota Research Institute (TRI) pertaining to the HSR are in a closed community, with the exception of some publicly available repositories built around the RoboCup@Home Domestic Standard Platform League (DSPL).
  2. The code not specific to the robot itself was contained in a private repository in my former group’s organization, and furthermore is embedded in a massive monorepo.

Rewind to 2020: The original simulation tool (left) and a generated Gazebo world with a Toyota HSR (right).

So I thought, there are some generic utilities here that could be useful to the community. What would it take to strip out the home service robotics simulation tools out of that setting and make it available as a standalone package? Also, how could I squeeze in improvements and learn interesting things along the way?

This post describes how these utilities became pyrobosim: A ROS2 enabled 2D mobile robot simulator for behavior prototyping.

What is pyrobosim?

At its core, pyrobosim is a simple robot behavior simulator tailored for household environments, but useful to other applications with similar assumptions: moving, picking, and placing objects in a 2.5D* world.

* For those unfamiliar, 2.5D typically describes a 2D environment with limited access to a third dimension. In the case of pyrobosim, this means all navigation happens in a 2D plane, but manipulation tasks occur at a specific height above the ground plane.

The intended workflow is:

  1. Use pyrobosim to build a world and prototype your behavior
  2. Generate a Gazebo world and run with a higher-fidelity robot model
  3. Run on the real robot!

Pyrobosim allows you to define worlds made up of entities. These are:

  • Robots: Programmable agents that can act on the world to change its state.
  • Rooms: Polygonal regions that the robot can navigate, connected by Hallways.
  • Locations: Polygonal regions that the robot cannot drive into, but may contain manipulable objects. Locations contain one of more Object Spawns. This allows having multiple object spawns in a single entity (for example, a left and right countertop).
  • Objects: The things that the robot can move to change the state of the world.

Main entity types shown in a pyrobosim world.

Given a static set of rooms, hallways, and locations, a robot in the world can then take actions to change the state of the world. The main 3 actions implemented are:

  • Pick: Remove an object from a location and hold it.
  • Place: Put a held object at a specific location and pose within that location.
  • Navigate: Plan and execute a path to move the robot from one pose to another.

As this is mainly a mobile robot simulator, there is more focus on navigation vs. manipulation features. While picking and placing are idealized, which is why we can get away with a 2.5D world representation, the idea is that the path planners and path followers can be swapped out to test different navigation capabilities.

Another long-term vision for this tool is that the set of actions itself can be expanded. Some random ideas include moving furniture, opening and closing doors, or gaining information in partially observable worlds (for example, an explicit “scan” action).

Independently of the list of possible actions and their parameters, these actions can then be sequenced into a plan. This plan can be manually specified (“go to A”, “pick up B”, etc.) or the output of a higher-level task planner which takes in a task specification and outputs a plan that satisfies the specification.

Execution of a sample action sequence in pyrobosim.

In summary: pyrobosim is a software tool where you can move an idealized point robot around a world, pick and place objects, and test task and motion planners before moving into higher-fidelity settings — whether it’s other simulators or a real robot.

What’s new?

Taking this code out of its original resting spot was far from a copy-paste exercise. While sifting through the code, I made a few improvements and design changes with modularity in mind: ROS vs. no ROS, GUI vs. no GUI, world vs. robot capabilities, and so forth. I also added new features with the selfish agenda of learning things I wanted to try… which is the point of a fun personal side project, right?

Let’s dive into a few key thrusts that made up this initial release of pyrobosim.

1. User experience

The original tool was closely tied to a single Matplotlib figure window that had to be open, and in general there were lots of shortcuts to just get the thing to work. In this redesign, I tried to more cleanly separate the modeling from the visualization, and properties of the world itself with properties of the robotic agent and the actions it can take in the world.

I also wanted to make the GUI itself a bit nicer. After some quick searching, I found this post that showed how to put a Matplotlib canvas in a PyQT5 GUI, that’s what I went for. For now, I started by adding a few buttons and edit boxes that allow interaction with the world. You can write down (or generate) a location name, see how the current path planner and follower work, and pick and place objects when arriving at specific locations.

In tinkering with this new GUI, I found a lot of bugs with the original code which resulted in good fundamental changes in the modeling framework. Or, to make it sound fancier, the GUI provided a great platform for interactive testing.

The last thing I did in terms of usability was provide users the option of creating worlds without even touching the Python API. Since the libraries of possible locations and objects were already defined in YAML, I threw in the ability to author the world itself in YAML as well. So, in theory, you could take one of the canned demo scripts and swap out the paths to 3 files (locations, objects, and world) to have a completely different example ready to go.

pyrobosim GUI with snippets of the world YAML file behind it.

2. Generalizing motion planning

In the original tool, navigation was as simple as possible as I was focused on real robot experiments. All I needed in the simulated world was a representative cost function for planning that would approximate how far a robot would have to travel from point A to point B.

This resulted in building up a roadmap of (known and manually specified) navigation poses around locations and at the center of rooms and hallways. Once you have this graph representation of the world, you can use a standard shortest-path search algorithm like A* to find a path between any two points in space.

This time around, I wanted a little more generality. The design has now evolved to include two popular categories of motion planners.

  • Single-query planners: Plans once from the current state of the robot to a specific goal pose. An example is the ubiquitous Rapidly-expanding Random Tree (RRT). Since each robot plans from its current state, single-query planners are considered to be properties of an individual robot in pyrobosim.
  • Multi-query planners: Builds a representation for planning which can be reused for different start/goal configurations given the world does not change. The original hard-coded roadmap fits this bill, as well as the sampling-based Probabilistic Roadmap (PRM). Since multiple robots could reuse these planners by connecting start and goal poses to an existing graph, multi-query planners are considered properties of the world itself in pyrobosim.

I also wanted to consider path following algorithms in the future. For now, the piping is there for robots to swap out different path followers, but the only implementation is a “straight line executor”. This assumes the robot is a point that can move in ideal straight-line trajectories. Later on, I would like to consider nonholonomic constraints and enable dynamically feasible planning, as well as true path following which sets the velocity of the robot within some limits rather than teleporting the robot to ideally follow a given path.

In general, there are lots of opportunities to add more of the low-level robot dynamics to pyrobosim, whereas right now the focus is largely on the higher-level behavior side. Something like the MATLAB based Mobile Robotics Simulation Toolbox, which I worked on in a former job, has more of this in place, so it’s certainly possible!

Sample path planners in pyrobosim.
Hard-coded roadmap (upper left), Probabilistic Roadmap (PRM) (upper right).
Rapidly-expanding Random Tree (RRT) (lower left), Bidirectional RRT* (lower right).

3. Plugging into the latest ecosystem

This was probably the most selfish and unnecessary update to the tools. I wanted to play with ROS2, so I made this into a ROS2 package. Simple as that. However, I throttled back on the selfishness enough to ensure that everything could also be run standalone. In other words, I don’t want to require anyone to use ROS if they don’t want to.

The ROS approach does provide a few benefits, though:

  • Distributed execution: Running the world model, GUI, motion planners, etc. in one process is not great, and in fact I ran into a lot of snags with multithreading before I introduced ROS into the mix and could split pieces into separate nodes.
  • Multi-language interaction: ROS in general is nice because you can have for example Python nodes interacting with C++ nodes “for free”. I am especially excited for this to lead to collaborations with interesting robotics tools out in the wild.

The other thing that came with this was the Gazebo world exporting, which was already available in the former code. However, there is now a newer Ignition Gazebo and I wanted to try that as well. After discovering that polyline geometries (a key feature I relied on) was not supported in Ignition, I complained just loudly enough on Twitter that the lead developer of Gazebo personally let me know when she merged that PR! I was so excited that I installed the latest version of Ignition from source shortly after and with a few tweaks to the model generation we now support both Gazebo classic and Ignition.

pyrobosim test world exported to Gazebo classic (top) and Ignition Gazebo (bottom).

4. Software quality

Some other things I’ve been wanting to try for a while relate to good software development practices. I’m happy that in bringing up pyrobosim, I’ve so far been able to set up a basic Continuous Integration / Continuous Development (CI/CD) pipeline and official documentation!

For CI/CD, I decided to try out GitHub Actions because they are tightly integrated with GitHub — and critically, compute is free for public repositories! I had past experience setting up Jenkins (see my previous post), and I have to say that GitHub Actions was much easier for this “hobbyist” workflow since I didn’t have to figure out where and how to host the CI server itself.

Documentation was another thing I was deliberate about in this redesign. I was always impressed when I went into some open-source package and found professional-looking documentation with examples, tutorials, and a full API reference. So I looked around and converged on Sphinx which generates the HTML documentation, and comes with an autodoc module that can automatically convert Python docstrings to an API reference. I then used ReadTheDocs which hosts the documentation online (again, for free) and automatically rebuilds it when you push to your GitHub repository. The final outcome was this pyrobosim documentation page.

The result is very satisfying, though I must admit that my unit tests are… lacking at the moment. However, it should be super easy to add new tests into the existing CI/CD pipeline now that all the infrastructure is in place! And so, the technical debt continues building up.

pyrobosim GitHub repo with pretty status badges (left) and automated checks in a pull request (right).

Conclusion / Next steps

This has been an introduction to pyrobosim — both its design philosophy, and the key feature sets I worked on to take the code out of its original form and into a standalone package (hopefully?) worthy of public usage. For more information, take a look at the GitHub repository and the official documentation.

Here is my short list of future ideas, which is in no way complete:

  1. Improving the existing tools: Adding more unit tests, examples, documentation, and generally anything that makes the pyrobosim a better experience for developers and users alike.
  2. Building up the navigation stack: I am particularly interested in dynamically feasible planners for nonholonomic vehicles. There are lots of great tools out there to pull from, such as Peter Corke’s Robotics Toolbox for Python and Atsushi Sakai’s PythonRobotics.
  3. Adding a behavior layer: Right now, a plan consists of a simple sequence of actions. It’s not very reactive or modular. This is where abstractions such as finite-state machines and behavior trees would be great to bring in.
  4. Expanding to multi-agent and/or partially-observable systems: Two interesting directions that would require major feature development.
  5. Collaborating with the community!

It would be fantastic to work with some of you on pyrobosim. Whether you have feedback on the design itself, specific bug reports, or the ability to develop new examples or features, I would appreciate any form of input. If you end up using pyrobosim for your work, I would be thrilled to add your project to the list of usage examples!

Finally: I am currently in the process of setting up task and motion planning with pyrobosim. Stay tuned for that follow-on post, which will have lots of cool examples.

You can read the original article at Roboticseabass.com.

]]> RoboCup2022 underway – where to find the livestream action https://robohub.org/robocup2022-underway-where-to-find-the-livestream-action/ Thu, 14 Jul 2022 13:59:14 +0000 https://robohub.org/?p=205042

RoboCup 2022 kicked off yesterday, and there have already been lots of competitions within the various leagues. Many of these are livestreamed to YouTube, and the recordings are available for anyone to watch.

Below are the links to the livestream (and recorded) channels for the leagues that have them.

In addition to these channels, there are also some stand-alone recordings.

Here are some highlights from the humanoid league drop-in tournament:

This video features a match between the HTWK-Robots and rUNSWift.

Find out more about RoboCup 2022 here.

]]> Happy Birthday Nao! https://robohub.org/happy-birthday-nao/ Sat, 05 Feb 2022 12:00:11 +0000 https://robohub.org/?p=203462

NAO robot. Image credits: Stephen Chin on Flickr [CC BY 2.0]

Created by the French company ‘Aldebaran Robotics’ in 2008, and acquired by ‘Softbank Robotics Japan’ in 2015, NAO is an autonomous and programmable humanoid robot that has been successfully applied to research and development applications for children and adults. More than 13,000 NAO robots are used in more than 70 countries around the world. Pretty much every lab working in human-robot interaction research owns a NAO making it the social robot that has been the most used in the history of the field.

In our paper ‘10 Years of Human-NAO Interaction Research: A Scoping Review’ published in Frontiers in Robotics and AI, we present an overview of the evolution of NAO’s technical capabilities. We also present the main results from a scoping review of the human-robot interaction research literature in which NAO was used.

Technical overview of Nao over the years

Appearance-wise, NAO hasn’t aged a bit. The childish anthropomorphic appearance of the robot has stayed pretty much the same over the last 10 years. However, a lot of improvement has been made over the last versions of the robots.

Evolution of the NAO robot’s technical characteristic since 2008

We also note that the software environment for the robot has also improved over the years integrating new technologies and features as years passed: face recognition and tracking, semantic engine. The NAOqi API also supports various programming languages. In 2013, Aldebaran also created a developer community. This community platform allowed to share resources, behaviours and featured a forum. This has probably participated a lot in the expansion and adoption of robot with developers and researcher sharing designs of behaviours, and other games they developed with the robot.

Quantitative analysis of the scoping review

Geographical distribution of the papers included in our scoping review

The geographical distribution of authors using NAO for their research in HRI is unprecedented for a social robot. NAO has been used in more than 50 countries all around the world. And we see from the above map that the distribution is well split between North-America, Europe and Asia. But still, just like in a lot of research field the global north, tends to publish more than southern countries.

In terms of the role of the robot, researchers have investigated different application domain with the robot being used as a peer or a demonstrator in public spaces, an educational and assistant for therapists (for children with ASD, learning disability or hearing disability) being the dominant ones.

Input data (left), output data (middle), equipment (right) for each application field

As mentioned above, NAO is equipped with various sensors and actuators allowing for multi-modal interaction. Often researchers have used the robot to speak and/or make gestures. On the perception side, the robot’s limited speech recognition and poor microphone quality refrained researcher to use NLP but instead made them rely on facial, and gesture recognition. We also noted in our analysis that researchers often used external devices such as RGBD cameras or tablets in order to capture other signals allowing to assess the situation.

Qualitative results

Multiple studies reported how fun and enjoyable was the appearance of the robot. NAO is certainly not just an eye-catcher robot as its portability is highly appreciated by the researchers. NAO can be regarded as a plug-and-play robot due to its robust and easy setup characteristics. It has also been found affordable compared to other humanoid robots.

On the other side, several technical issues were reported such as low battery life and overheating issues. The speech recognition limits the interaction distance and context (i.e. noisy environments). Finally, not much embedded autonomous functionality could be used in the initial versions.

Nao’s communities

Over the years, several types of communities have formed around the NAO robotic platform.

1) Educational communities

In 2010, the University of Tokyo piloted a new educational program developed by Aldebaran Robotics that aimed to train students to use NAO. Along with some discounts to buy NAO for educational purposes, the company released some ebooks. The books and educational program cover both technical aspects of using the robot as well as more creative projects that can be given as exercise to students. The educational market certainly helped in making NAO widely used in the 2010’s.

2) Engineering communities

In 2007, the Robocup decided to use the NAO robot as the official platform for its soccer league. As detailed in the review, this led to fruitful advancement on technical capabilities of the robot such as its locomotion and vision system.

More recently, the introduction of the Pepper robot as a new platform for the robocup@Home challenge certainly can let us think that it will boost the robot’s economical life and produce more advances in social and at home tasks with the robot.

3) Research communities

Aldebaran had a strong Research & Development strategy in its early days and naturally became partner of several large European projects: ALIZ-E, DREAM, SQUIRREL, L2Tor. These strategies had the advantage to form the future researchers in the use of their platform and made NAO a dominant robot in human-robot interaction research between 2015 and 2019.

4) Industrial translation

Image from RobotLab website visited on December 20th 2021

Recently a network of start-ups has woven around the NAO robot. Providing various services such as educational workshops, therapeutic sessions, or business and marketing services, the number of these start-ups have raised these past few years (e.g. Interactive Robotics, RobotLAB and Avatarion).

Conclusion

In our review, we present a comprehensive overview on the use of NAO, which is a remarkable social robot in many instances. So far, NAO has been exposed to a challenging yet rewarding journey. Its social roles have expanded thanks to its likeable appearance and multi-modal capabilities followed by its fitness to deliver socially important tasks. Still, there are gaps to be filled in view of sustainable and user-focused human-NAO interaction. We hope that our review can contribute to the field of HRI that needs more reflection and general evidence on the use of the social robots, such as NAO in a wide variety of contexts. An implication of the findings shows a greater need for increasing the value and practical application of NAO in user-centered studies. Future studies should consider the importance of real-world and unrestricted experiments with NAO and involve other humans that might facilitate human-robot interaction.

]]> 30 women in robotics you need to know about – 2020 https://robohub.org/30-women-in-robotics-you-need-to-know-about-2020/ Tue, 13 Oct 2020 07:00:25 +0000 https://robohub.org/30-women-in-robotics-you-need-to-know-about-2020/

It’s Ada Lovelace Day and once again we’re delighted to introduce you to “30 women in robotics you need to know about”! From 13 year old Avye Couloute to Bala Krishnamurthy who worked alongside the ‘Father of Robotics’ Joseph Engelberger in the 1970s & 1980s, these women showcase a wide range of roles in robotics. We hope these short bios will provide a world of inspiration, in our eighth Women in Robotics list! 

In 2020, we showcase women in robotics in China, Japan, Malaysia, Israel, Australia, Canada, United States, United Kingdom, Switzerland, Israel, Norway, Spain, The Netherlands, India and Iran. There are researchers, industry leaders, and artists. Some women are at the start of their careers, while others have literally written the book, the program or the standards.

CAPE CANAVERAL, Fla. – Members of the Kennedy Space Center government-industry team rise from their consoles within the Launch Control Center to watch the Apollo 11 liftoff through a window. Photo credit: NASA

 

We publish this list because the lack of visibility of women in robotics leads to the unconscious perception that women aren’t making newsworthy contributions. We encourage you to use our lists to help find women for keynotes, panels, interviews etc. Sadly, the daily experience of most women in robotics still looks like this famous NASA control room shot from the 1969 Apollo 11 moon landing, with one solitary woman in the team. It has taken sixty years for the trailblazers like Joann Morgan, Katherine G. Johnson, Dorothy Vaughan, Mary Jackson and Poppy Northcutt to become well known. And finally now we have a woman, Charlie Blackwell-Thompson, serving as Launch Director for the upcoming Artemis Mission, and another, Gwynne Shotwell, serving as President and COO of SpaceX.

In celebration of Women’s History Month, the “Women of Launch Control” working in Exploration Ground Systems take time out of their Artemis I launch planning to pose for a photo in Firing Room 1 of the Launch Control Center at NASA’s Kennedy Space Center in Florida on March 4, 2020. Artemis I will be the first integrated flight test of the Orion spacecraft and Space Launch System rocket, the system that will ultimately land the first woman and the next man on the Moon. Photo credit: NASA/Glenn Benson

In 2019, women still accounted for less than a quarter (23.6%) of those working in natural and applied sciences and related occupations. In these occupations, women earned, on average, \$0.76 to every \$1.00 earned by men in annual wages, salaries, and commissions in 2018. [ref Catalyst.org ]

This issue is even more pervasive and devastating if you are a person of color. We have always strived to showcase a wide range of origins and ethnicities in our annual list, and this year, as well as four African American roboticists, our list includes the first African American female CEO of a company valued over $1Billion USD. This is just a small step forward, but we’re pleased to announce the recent launch of the Black in Robotics organization, as well as greater recognition of the citation problem.

The citation problem is expected to significantly disadvantage women and people of color due to the historical lack of women followed by the recent growth of large scientific teams, multiplying exclusion. For example, Nature recently published a paper on the impact of NumPy, a significant scientific resource. NumPy was originally developed by many contributors. But the authoritative citation is likely to belong to this description paper, which has 26 authors, all male. [ref Space Australia]

On a positive note, many individuals and organizations intentionally try to reverse this bias. For example, Tulane University just published a guide to help you calculate how much of your reading list includes female authors and a citation guide, similar to the CiteHer campaign from BlackComputeher.org.  And as Bram Vanderborght, editor of IEEE Robotics and Automation magazine pointed out in the March 2020 issue, “Scientists are starting to consider how gender biases materialize in physical robots. The danger is that robot makers, consciously or not, may reinforce gender stereotypes and inadvertently create even greater deterrents for young, underrepresented people interested in joining our field.”

We hope you are inspired by these profiles, and if you want to work in robotics too, please join us at Women in Robotics. We are now a 501(c)(3) non-profit organization, but even so, this post wouldn’t be possible if not for the hard work of volunteers; Andra Keay, Hallie Siegel, Sabine Hauert, Sunita Pokharel, Ioannis Erripis, Ron Thalanki and Daniel Carrillo Zapata.

Fatemeh Pahlevan Aghababa

CTO and co-founder at Intelize | Technical Committee Head of International Robocup Federation | Technical Lead at Farakav co.

Fatemeh Pahlevan Aghababa is CTO and co-founder of Intelize startup which provides AI-powered solutions such as Ribona whose mission is to make a difference in Industry 4.0 via novel technologies. She is an active researcher in intelligent systems and cognitive robotic fields, and received the Silvia Coradeschi Robocup Award for “the young female scientists with distinguishing research in AI and robotics”. She is the Technical Committee Head of the International Robocup Federation, and has won the RoboCup World Championship for two consecutive years in Rescue Simulation League. Pahlevan was named among Rising Stars in VentureBeat’s Women in AI Awards.
Ariel Anders

Roboticist – Robust.AI

Ariel Anders is a black feminist roboticist who enjoys spending time with her family and artistic self-expression. Anders is the first roboticist hired at Robust.AI, an early stage robotics startup building the world’s first industrial grade cognitive engine. Anders received a BS in Computer Engineering from UC Santa Cruz and her Doctorate in Computer Science from MIT, where she taught project-based collaborative robotics courses, developed an iOS app for people with vision impairment, and received a grant to install therapy lamps across campus. Her research focused on reliable robotic manipulation with the vision of enabling household helpers.
Carlotta Berry

Professor of Electrical and Computer Engineering – Rose-Hulman Institute of Technology

Carlotta Berry, professor at Rose-Hulman Institute of Technology, helped found and co-directs the Rose Building Undergraduate Diversity (ROSE-BUD) program with a goal of recruitment, retention and professional development of historically marginalized and minoritized populations in computer science, computer, electrical and computer engineering. Berry also worked with faculty to create Rose-Hulman’s first multidisciplinary minor in robotics and has judged and organized FIRST Robotics competitions. Berry’s research is focused on educational mobile robotics, human-robot interaction and interfaces. Berry has also mentored and organized robotics programs for Girl Scout groups and visited elementary, middle, and high schools to motivate student’s interests in STEM.
Avye Couloute

Gen Arm 2Z Ambassador/ Founder of Girls into Coding

Avye Couloute is a multitalented 13-year-old, with a passion for coding from the age of 7. Avye founded Girls into Coding which is a volunteer event series empowering girls with the tools and skills to get in the tech industry. She has facilitated online and offline robotics workshops as well as raising money to provide textbooks and computer starter kits. Avye, a Gen Arm 2Z Ambassador, is also the recipient of The Diana Award’s Legacy Award and the winner of the 2020 FDM Everywoman in Technology Awards.
Yael Edan

Professor, Head of ABC Robotics Lab – Ben-Gurion University of the Negev (BGU)

Yael Edan, professor at Ben-Gurion University of the Negev is Head of the Agricultural, Biological, and Cognitive Robotics Initiative. Her research is specifically focused on agricultural robotics, human-robot cooperation, systems engineering of robotics systems and adaptive sensor selection and fusion. Edan has made major contributions in the introduction and application of intelligent automation and robotic systems to the field of agriculture with several patents. Edan has been involved in many international projects, is a member of IEEE RAS and a member of ASABE where she was the Chair of the Flexible Automation and Robotics/Mechatronics and BioRobotics committees.
Autumn Edwards

Professor – Western Michigan University

Autumn Edwards researches how ontological considerations, or people’s beliefs about the nature of communicators and of communication, both shape and are shaped by their interactions with social robots, as an emergent class of digital interlocutor. She is co-founder/co-director of the Communication and Social Robotics Labs (combotlab.org), Professor of Communication at Western Michigan University, and founding Editor-in-Chief of the journal Human-Machine Communication. She inspires passionate support from students and fellow staff, receiving the University Distinguished Teaching Award (2014), the Kim Giffin Research Award from the University of Kansas and was designated a Claude Kantner Research Fellow at Ohio University.
Marwa ElDiwiny

Researcher – University of Twente

Marwa ElDiwiny is a PhD researcher at Vrije Universiteit Brussel whose current research focus is on modelling and simulating self-healing soft materials for industrial applications. Her master thesis was UAV anti-stealth technology for safe operation. She has worked as a research engineer at Inria, Lille nord Europe, research scholar at Tartu Institute of Technology and lecturer in the Mechatronics and Industrial Robotics Program at Minia university, Egypt. Eldiwiny hosts the IEEE RAS Soft Robotics Podcast where researchers from both academia and industry are interviewed about the soft robotics field, with over 94 episodes available.
Aicha Evans

CEO – Zoox

Aicha Evans is the CEO of Zoox, the company developing purpose-built, zero-emission autonomous vehicles with end to end autonomy software stack. Evans is a former senior vice president and chief strategy officer at Intel Corporation where she drove Intel’s long-term strategy to transform from a PC-centric company to a data-centric company. Evans spent 10 years in various engineering management positions at Rockwell Semiconductors, Conexant, and Skyworks, before Intel. Evans is also on the Supervisory Board of SAP and is the first AfricanAmerican female CEO of a billion dollar company.
Elena Garcia Armada

Co-Founder – Marsi Bionics. Senior Researcher – Centre for Automation and Robotics

Elena Garcia Armada is a tenured researcher at the Spanish National Research Council and co-founder of Marsi Bionics. She is an expert in pediatric exoskeletons optimized for active gait rehabilitation of children suffering from neuromuscular diseases. She has won over 30 awards, is a member of the Innovation and Knowledge Transfer Working Group of the Ministry of Science, Innovation and Universities, a member of the Industrial Activities Board of IEEE Robotics and Automation Society, and on the jury of the Princess of Asturias Scientific and Technical Research Awards. She is passionate about helping children walk again.
Keiko Homma

Senior Researcher Service Robotics Research Team, Robot Innovation Research Center – AIST

Keiko Homma is a senior researcher at AIST in the Assistive Robotics Research Team, Human Augmentation Research Center. She received B.Sc and Ph.D degrees in Engineering from the University of Tokyo, and was a visiting researcher at Aalto University. Her current research interests include assistive and robot systems, particularly their safety aspects, and has developed a risk assessment tool for assistive robots and test dummies for exoskeleton typed physical assistant robots. She is also an IEEE RAS Distinguished Lecturer.
Michelle J. Johnson

Associate Professor & Director of Rehabilitation Robotics Lab – University of Pennsylvania

Michelle J. Johnson is an associate professor of the departments of physical medicine and rehabilitation, and of bioengineering at University of Pennsylvania, and directs the Rehabilitation Robotic Research and Design Laboratory in the Pennsylvania Institute of Rehabilitation Medicine which is affiliated with the General Robotics, Automation, Sensing & Perception (GRASP) Lab. Johnson’s PhD is in Mechanical Engineering from Stanford University. Her research specialization is in the design, development, and therapeutic use of novel, affordable, intelligent robotic assistants to quantify upper limb motor function in adults and children at risk of or with brain injury. She is also a US Fulbright Scholar.
Hadas Kress-Gazit

Associate Professor – Sibley School of Mechanical and Aerospace Engineering, and ICRA2022 Program Chair

Hadas Kress-Gazit is an Associate Professor at the Sibley School of Mechanical and Aerospace Engineering at Cornell University. Her research focuses on formal methods for robotics and automation and more specifically on synthesis for robotics – automatically creating verifiable robot controllers for complex high-level tasks. Her group explores different types of robotic systems including modular robots, soft robots and swarms, and synthesizes ideas from robotics, formal methods, control, hybrid systems and human-robot interaction. She has received multiple recognitions and awards for her research, her teaching and her advocacy for groups traditionally underrepresented in robotics.
Bala Krishnamurthy

CEO – Mobile Software Inc

Bala Krishnamurthy is a pioneer in robotics. From her early days at Unimation to her current role as CEO of Mobile Software. Krishnamurthy has over 40 years of experience in researching, designing and developing embedded real-time systems and software. She adapted the VAL language designed for PUMA robots to the hydraulic Unimate robots, then led the team at Engelberger’s HelpMate Robotics developing the software that allowed autonomous courier robots to navigate throughout hospitals. Krishnamurthy was also a member of NASA’s Office of Exploration Systems research proposal review panel for Human and Robotic Technology and authored dozens of technical articles.
Sam MacDonald

President – Deep Trekker

Sam MacDonald is President and cofounder of Deep Trekker, a Canadian made ROV (Remotely Operated Vehicle) for economical underwater robotic exploration. After working in automation and at Blackberry in marketing, Macdonald founded her own marketing leadership company. She then started Deep Trekker in 2009 taking it from garage to global company. Today, Deep Trekker has expanded into a host of verticals, including aquaculture, commercial diving, salvage, military, first responders, oil and gas, energy, marine surveying, wastewater, infrastructure and recreation, and sells products in 80 countries.
Karon Maclean

Professor – University of British Columbia, Director – SPIN The Sensory Perception and Interaction Research Group

Karon MacLean is a Professor in Computer Science at UBC, with degrees in Biology and Mechanical Engineering and time spent as a professional robotics engineer and haptics / interaction researcher at Interval Research, Palo Alto. MacLean’s research specializes in haptic interaction: cognitive, sensory and affective design for people interacting with the computation we touch, emote and move with and learn from, from robots to touchscreens and the situated environment. MacLean leads UBC’s Designing for People interdisciplinary research cluster and is Special Advisor, Innovation, and Knowledge Mobilization to UBC’s Faculty of Science.
Chris Macnab

Associate Professor – University of Calgary

Chris Macnab is an associate professor at the University of Calgary. She earned a BEng in Engineering Physics from the Royal Military College of Canada and a PhD from the University of Toronto Institute for Aerospace Studies, where she investigated control of flexible-joint space robots using neural networks. Macnab has worked as a control systems designer for Dynacon Enterprises, as part of a team that developed and tested a control system for an antenna on the space station. She has also worked for CRS Robotics, programming robot movements. Macnab’s specialization is in control systems, motion control, and robotics.
Shauna McIntyre

CEO – Sense Photonics

Shauna McIntyre is CEO at Sense Photonics, developer of high-performance, scalable 3D vision systems with offices in San Francisco, North Carolina and the UK. She is an automotive industry veteran who has led Google’s automotive services and Google Maps’ automotive programs. Mclntyre was also Chief of Staff for Google’s consumer electronics division and serves on the Board of Directors of Lithia Motors. Mclntyre brings a track record of driving innovation in traditional industries and a long-standing mission to deliver new, breakthrough experiences to industrial and automotive companies through intelligent hardware.
Elena Messina

Group Leader, Manipulation & Mobility – National Institute of Standards and Technology (NIST)

Elena Messina leads the Intelligent Systems Division’s Manipulation & Mobility Systems Group at the US National Institute of Standards and Technology. She also manages the Engineering Laboratory’s Robotic Systems for Smart Manufacturing Program. Messina founded key efforts to develop test methodologies for measuring performance of robots, which range from long-term use of robotic competitions to drive innovation to consensus standards for evaluating robotic components and systems. Messina has received three Department of Commerce Bronze Medals for Superior Performance and Technical Leadership, and the Edward Bennet Rosa Award for research and development leading to standardized test methods for emergency response robots.
Linda Nagata

SciFi Writer, Owner – Mythic Island Press

Linda Nagata is a science fiction and fantasy writer from Hawaii, best known for her high-tech science fiction, including the far-future adventure series, Inverted Frontier, and the near-future thriller, The Last Good Man. Nagata is most recognized for her Nanotech Succession series, which is considered exemplary of the Nanopunk genre. Nagata’s work has been nominated for the Hugo, Nebula, Locus, John W. Campbell Memorial, and Theodore Sturgeon Memorial awards. She has also won the Nebula award, and is a two-time Locus award winner.
Samia Nefti-Meziani

Founder and Head, Professor – Salford University,University of Salford’s Autonomous Systems and Advances Robotics Research Centre

Samia Nefti-Meziani is a Professor at Salford University UK, where she is founder of the ASAR Centre. She is a veteran and one of the pioneers in Soft Robotics and low cost cross-sectorial robotics systems and their application in many sectors including Manufacturing, Nuclear, Space, Autonomous cars and Healthcare. Nefti-Meziani runs several large UK and European research programs, and regularly appears on TV and radio. She is also a Cofounder and Executive Board Member of the UK’s National Robotics Network, former Vice Chairman of IEEE RAS UK and a member of the UK-Government Robotics Growth Partnership.
Nicci Rossouw

CEO – Exaptec

Nicci Rossouw is CEO and founder of Exaptec, a provider of social, service and telepresence robots. Rossouw specializes in robotics automation solution design and delivery using a Robotics-as-a-Service as a business model. Rossouw has provided robotic telepresence solutions to educational facilities and businesses to augment communication and movement for disabled and incapacitated people. Exaptec was one of Westpac’s top Businesses of Tomorrow winners.
Yulia Sandamirskaya

Applications Research Lead, Neuromorphic Computing Lab – Intel Labs

Yulia Sandamirskaya is a senior researcher at Intel Labs leading a group developing applications of Intel’s neuromorphic research chips. Sandamirskaya was also a group leader in the Institute of Neuroinformatics at the University of Zurich and ETH Zurich. Sandamirskaya specializes in perception, movement control, memory formation, and learning in embodied neuronal systems, and implemented neuronal architectures in neuromorphic devices, interfaced with robotic sensors and motors. Sandamirskaya has served as the chair of EUCOG, the European Society for Artificial Cognitive Systems, and as coordinator of the NEUROTECH project.
Katherine Scott

Developer Advocate – Open Robotics

Katherine Scott is a Developer Advocate at Open Robotics, the maintainers of ROS and Ignition Gazebo, and sits on the board of the Open Source Hardware Association. Scott was Cofounder and Software Lead at Tempo Automation, a manufacturer of low-volume electronics, served as Director of Research and Development at Sight Machine, a leader in manufacturing analytics, and worked as a computer vision engineer. Along the way, Scott developed a large part of the SimpleCV Python library, and several innovative software prototypes in the fields of computer vision, graphics, augmented reality, and robotics.
Hazlina Selamat

Associate Professor – Universiti Teknologi Malaysia, Director – Apt Touch Sdn Bhd, Research Fellow – Centre for Artificial Intelligence and Robotics (CAIRO)

Hazlina Selamat is an Associate Professor at Universiti Teknologi Malaysia, an active member of the university’s Centre for Artificial Intelligence & Robotics (CAIRO), Founding Member for the Malaysian Society for Automatic Control Engineers (MACE). She is the co-founder and Director of Apt Touch Sdn Bhd, a company that designs, develops, and manufactures fuel injection systems for small engines. Her projects include developing the optimization engine for an energy management system, and the pre-commercialization of a retrofit fuel injection kit for carburetor engines. Her interests are adaptive control, online system identification and application of control to the high-order and nonlinear systems.
Elizabeth Sklar

Research Director and Professor – Agrirobotics and University of Lincoln and Kings College of London

Elizabeth Sklar is a Professor at the University of Lincoln and King’s College London, UK, where she heads the Centre for Robotics Research. Sklar is a veteran of MIT/Lincoln Lab, a Founder Trustee of the RoboCup Federation, a Fulbright Scholar 2013-2014, former member of the Board of Directors for the IFAAMAS, and sits on the Editorial Board for the Journal of Autonomous Agents & MultiAgent Systems. Sklar specializes in interaction for multi-robot and human-robot systems, and shared decision making by applying computational argumentation-based dialogue to human-agent and human-robot systems.
Allison Thackston

Staff Software Engineer, Manager Shared Autonomy – Waymo

Allison Thackston has a passion for solving practical problems with robotics. She currently leads a team solving some of the toughest problems in self-driving technology at Waymo. Thackston was previously the Engineering Lead and Manager of the Shared Autonomy Robotics team at Toyota Research Institute and was the Lead Engineer for Robotic Perception on Robonaut 2, the first humanoid robot on the International Space Station. Thackston is experienced in robust task and motion planning, manipulation, and applied computer vision.
Liane Thompson

CEO – Aquaai Norway AS

Liane Thompson is the CEO of Aquaai, a marine robotics company that uses data to improve our understanding of ocean health by collecting and delivering real-time visual and environmental data to a web-based dashboard. Thompson’s company has grown from a daughter’s idea into a full-fledged production, with a global VC backed brand. Norwegian salmon producer Kvarøy Fiskeoppdrett is working with Aquaai’s bio-inspired finned robots to monitor fish behavior and conditions at their fish farms. Aquaai has been awarded Top Aquaculture Innovation Award 2019 Fish2.0 Global Forum, an Innovation Norway grant, as well as being selected to Australia’s TekFish Challenge.
Claire Tomlin

Charles A. Desoer Professor of Electrical Engineering and Computer Sciences – UC Berkeley

Claire Tomlin is a professor at the University of California Berkeley, where she holds the Charles A. Desoer Chair in Engineering. She specializes in control of safety-critical systems applied to air traffic control automation and unmanned air systems, and is known for her pioneering work developing methods for verifying the safe range of operation for hybrid systems. Tomlin is a MacArthur Foundation Fellow, a Fellow of the IEEE and AIMBE, a member of the US National Academy of Engineering and the American Academy of Arts and Sciences. She received the IEEE Transportation Technologies Award, and an honorary doctorate from KTH.
Janet Wiles

Professor – University of Queensland

Janet Wiles is Professor of Complex and Intelligent Systems at the University of Queensland, where she leads a multidisciplinary team studying social robotics and has pioneered the use of robots in Indigenous communities. She also leads the Future Technologies thread at the ARC Centre of Excellence for the Dynamics of Language developing Human Centred AI for language technologies and making AI tools available to a broad range of users. She is in the Human Centred Computing discipline in UQ’s School of Information Technology and Electrical Engineering. Wiles specializes in human-robot interaction, language technologies, bio-inspired computation, visualization and artificial intelligence, complex systems modelling in biology and neuroscience, human memory, language, and cognition.
Rong Xiong

Professor at Zhejiang University – Institute of Cyber Systems and Control, Zhejiang University

Rong Xiong is a professor and head of the Robotics Laboratory at Zhejiang University, co-director of the ZJU-UTS Joint Center on Robotics Research, international trustee of RoboCup, and expert member for a special project on intelligent robots. Ministry for Science and Technology, China. Her group is known for their work developing humanoid robots that can play table tennis with each other and also compete with human players. Xiong received first prizes at the Scientific and Technological Award, and Teaching Achievement of Zhejiang Province. Xiong’s research specialization includes visual recognition, simultaneous localization and mapping, motion planning, and control for robots.

Want to keep reading? There are 180 more stories on our 2013 to 2019 lists. Why not nominate someone for inclusion next year!

And we encourage #womeninrobotics and women who’d like to work in robotics to join our professional network at http://womeninrobotics.org

]]> COVID-19 robotics resources: ideas for roboticists, users, and educators https://robohub.org/covid-19-robotics-resources-ideas-for-roboticists-users-and-educators/ Mon, 13 Apr 2020 06:22:51 +0000 https://robohub.org/covid-19-robotics-resources-ideas-for-roboticists-users-and-educators/

Thessaloniki, Greece – April 6, 2020: Drone with recorded message informs citizens of Thessaloniki to stay home to be protected from the coronavirus.

Robots could have a role to play in COVID-19, whether it’s automating laboratory research, helping with logistics, disinfecting hospitals, education, or allowing carers, colleagues or loved ones to connect using telepresence. Yet many of these solutions are still in development or early deployment. The hope is that accelerating these translations could make a difference.

This page aims to compile some resources for roboticists who are able to help, users who need robots for COVID-19 applications, and people who want to learn about robotics while on lockdown.


This is not an exhaustive resource page, and we will regularly be updating the content. Please send pointers to sabine.hauert@robohub.org. Check AIhub.org for a similar resource page related to AI.

Many people are barely getting by, juggling work from home and caring for family, or other worries, please don’t feel like you should be doing any of the things listed below.

For people looking to use robots

Science Robotics has a useful commentary highlighting ways in which roboticists can help, written by some of the top brass in the field (Guang-Zhong Yang, Bradley J. Nelson, Robin R. Murphy, Howie Choset, Henrik Christensen, Steven H. Collins, Paolo Dario, Ken Goldberg, Koji Ikuta, Neil Jacobstein, Danica Kragic, Russell H. Taylor, Marcia McNutt). The four areas highlighted are clinical care (telemedicine and decontamination), logistics (delivery and handling of contaminated waste), reconnaissance (monitoring compliance with voluntary quarantines), and continuity of work and maintenance of socioeconomic functions (manufacturing).

Although not exhaustive, here are some ways robots are being used for COVID-19 efforts.

UVD Robots’ UV-Disinfection Robot.
https://youtu.be/6hs_sNGIUls

UBTECH AI robots in COVID-19 treating hospitals.
https://youtu.be/Z8QPcTUB9O4

Kuka robot doing automatic blood sampling.

And a story about a Stanford lab doing robotic Covid-19 testing.

Startship technologies delivery robots.

Drones to reinforce lockdown and disinfect.

Virtual museum tours using telepresence robots.

Social robots to connect to loved ones in nursing homes.

Robots powering the mass buying at Ocado and Amazon.

For people looking to learn robotics

You may be looking for activities for yourself, or for your kids. Here’s a short list of pointers, starting with resources for the younger generations.

iRobot has some nice educational robots like Root (that you would need to purchase) and associated curriculum. It’s worth checking out their other free STEM resources and newly launched education portal. I really like their activity book, which you can print and use.

If you can make purchases, check out the LEGO Mindstorm (~300 USD) or a Thymio (~200 USD), two great kits to get your kids programming robots. I’m currently playing with a sphero mini (~50 USD) with my 2-year-old.

Learn about some of the robotics competitions for students, including FIRST and RoboCup, some of these have a virtual league.

It’s National Robotics Week in the US, check out their planned activities and resources, although several events have been canceled due to social distancing.

The EPSRC Robotics & Autonomous Systems Network is running a school competition for kids to draw or write stories about robots.

Get an Arduino or Raspberry Pi kit and start playing around with sensors, motors, and simple robot designs.

Learn ROS and how to program robots on The Construct – Robot Ignite Academy. Simulations run in the cloud, so you don’t have to worry about hardware and software setup problems. More information on their efforts with online learning during COVID-19 here.

Watch an online robotics course on edX, coursera, or Udacity.

You can also learn about the latest happenings in robotics directly from the top conferences, as many have now gone online. The Human-Robot Interaction conference for example was last week and has been featuring videos on youtube.

For roboticists looking to help

Provide advice
Several governments have put out calls for expertise and ideas, including in robotics. The UK has set up a UK Government COVID-19 Outbreak Expert Database, the EU has the AI-ROBOTICS vs COVID-19 initiative of the European AI Alliance, and EU Digital Innovation Hubs such as DIH-Hero, focussed on healthcare, are gathering ideas and expertise.

Do research
Funding agencies are putting in place emergency grants for COVID-19 related work. This Science article outlines the US National Science Foundation’s efforts to use the NSF RAPID scheme to fund COVID-19 projects. UK Research and Innovation (UKRI) has an open call for research and innovation ideas to address COVID-19, as well as an Innovate UK call for business-led innovation in response to global disruption aimed at companies. National Research Canada has a COVID-19 programme. The Swiss National Science Foundation had a special call on Coronovirus. Germany offers grants for Multidisciplinary Research into Epidemics and Pandemics in Response to the Outbreak of SARS-CoV-2. Similar programs can be found in many countries, usually by looking at the governmental funding agency website, including in Austria, Belgium, Brazil, India. I’d welcome pointers to specific funding calls from elsewhere in the world that could be used for robotics developments – in particular from Asia – as language barriers have made this information difficult to find.

Join a hackathon
Hackathons are popping up around the world, with some focused on coding and tech, examples include the UNESCO CodeTheCurve Hackathon, the MIT COVID-19 Challenge: Beat the Pandemic, #CodeVSCovid19, and the Covid-19 Global Hackathon. The World Health Organization (WHO) Regional Office for Africa hosted its first virtual hackathon bringing together 100 leading innovators from across sub-Saharan Africa in a bid to pioneer creative local solutions to the COVID-19 pandemic and address critical gaps in the regional response. In the EU, EUvsVirus Hackathon aims to develop innovative solutions and overcome coronavirus-related challenges.

Repurpose robotics skills to produce PPE and ventilators
Many robotics labs are kitted with workshops that have 3D printers, laser cutters, electronics, and pumps (thanks to the soft robotics crowd), useful to build custom hardware that may come in handy to produce PPE or prototype medical supplies like ventilators.

You can check here for the NIHR COVID-19 PPE Challenge. If you have a 3D printer, you can help print shields at 3D Crowd UK, or things like hand-free door openers. Check the Open Source COVID19 Medical Supplies facebook group, or Helpful Engineering . There are also a number of open ventilator projects, including the e-vent, or OxVent, although there is a challenge in seeking approval for their use.

Read the literature
ArXiv launched new COVID-19 quick search. The search results are sorted by date, with the most recent papers at the top of the list. You can access the search here.

You can also read COVID-19 literature from IEEE for free here.

Hear from the robotics and AI experts
Check out the weekly Covid-19 Robots and Us organised by Silicon Valley Robotics and the CITRIS People and Robots Initiative.

Watch the Stanford HAI – COVID-19 and AI: A Virtual Conference.

Watch ELLIS against Covid-19 – I can’t embed the video because they have not made it public – but the link is here.

I hope this was helpful, please send me any pointers to add to this list at sabine.hauert@robohub.org or on twitter @sabinehauert.

]]> #SciRocChallenge tests robots in a realistic smart shopping environment https://robohub.org/scirocchallenge-tests-robots-in-a-realistic-smart-shopping-environment/ Wed, 18 Sep 2019 11:52:20 +0000 https://robohub.org/scirocchallenge-tests-robots-in-a-realistic-smart-shopping-environment/

OLYMPUS DIGITAL CAMERA

The city of Milton Keynes hosts from the 17th to the 21st of September the European Robotics League – Smart Cities Robotics Challenge (SciRoc Challenge). For the first time, international researchers in robotics and artificial intelligence meet in a shopping mall to demonstrate the state of the art in robotics within the context of smart cities and specifically smart shopping.

The European Robotics League, funded by the European Commission to advance research, development and innovation in robotics and artificial intelligence, is the umbrella for three robotics competitions: ERL Consumer, ERL Emergency and ERL Professional service robots. All three leagues meet every two years in the ERL Smart Cities Robotics Challenge, showcasing how real robots can make our lives better in urban environments.

The Challenge
The SciRoc challenge will be held in the smart shopping mall of the Centre:MK. The challenge focuses on smart shopping and is divided into a series of episodes, each consisting of a task to be performed addressing specific research challenges. In order to accomplish their tasks, robots will have to cooperate with the simulated digital infrastructure of a smart shopping mall. Although the competing robots will face mock scenarios, the environment and difficulties are intended to be as realistic as possible, including the interaction with people from the public.

The episodes are organised into three categories:

Human-Robot Interaction (HRI) and Mobility, focuses on robots able to show social behaviour tasks such as verbal interaction with humans.

Manipulation, focuses on robots able to achieve manipulation tasks.

Emergency, defines tasks addressed autonomously by small aerial robots

The SciRoc consortium has designed the episodes with the collaboration of external experts from research and industry in the different categories.

The five episodes that are part of the Smart Cities Robotics challenge, and have been chosen by the robotics community from a previous list of fourteen are:

Deliver coffee shop orders (E03)
In this episode the robot will assist customers in a coffee shop by taking orders and bringing objects to and from customers’ tables.
The main functionality evaluated in this episode is people perception. Additional side functionalities are navigation, speech synthesis and recognition.

Team SocRob and their robot MOnarCH getting ready to serve coffees. Photo Credits: European Robotics League

Take the elevator(E04)
The robot must take the elevator crowded with customers to reach a service located in another floor.
The robot should interact with the MK:DataHub to discover which floor it must reach to accomplish its task. The robot must be able to take the elevator together with regular customers of the shopping mall.

Shopping pick and pack (E07)
The robot is in one the booths of the mall. On the shelf of the booth are the goods displayed for sale to the customers.
The customers can place orders through a tablet. The robot must move behind the display and collect the requested packages for the customer, place them in a box, and place the box on a tray where the customer can pick it up.

Through the door (E10)
Doors are ubiquitous in human environments. There are many types of doors, some of which are easier to operate than others for a robot.
In this episode the robot will identify a door, approach it and open it completely within a specified tolerance from 90°.

Fast delivery of emergency pills (E12)
The aerial robot must attend an emergency situation in which a first-aid kit needs to be delivered to a customer.
The robot must be able to fly autonomously to the customer location as fast as possible.

Teams can participate in one or more episodes depending on to their research interests.

The teams
A total of 10 teams from 5 different countries classified to compete in the first edition of the ERL Smart Cities Robotics Challenge.
The teams participating in the SciRoc Challenge 2019 are:

1. SocRob@Home – The Soccer Robots or Society of Robots (SocRob) team is a long-term research project of the Instituto Superior Tecnico, Portugal. Funded in 1998, the team has a broad experience participating in robotics competitions such as RoboCup Soccer, RoboCup@Home, RocKin@Homeand and ERL Consumer Robots. The team has special interest in the topic of HRI and mobility and will participate in Episode 3 – Deliver coffee shop orders.

2. Robotics Lab UC3 – This multidisciplinary research group from the Universidad Carlos III de Madrid, Spain, has previously participated in the ERL Consumer Robots league with one of the TIAGO platforms sponsored by PAL Robotics. The team will demonstrate their robot abilities to interact with humans in the Episode 4 – Take the elevator.

3. Gentlebots – Gentlebots is a team of researchers in robotics from the Rey Juan Carlos University and the University of León, Spain. Their research focus is on software development that allows robots to exhibit intelligent behaviours and they have competed in RoboCup@Home. They will participate in Episode 3 and Episode 4.

4. b-it-bots – The team from the Hochschule Bonn-Rhein-Sieg, Germany, has a broad experience with industrial and domestic robots in RoboCup@Work and RoboCup@Home. Winners of the ERL Professional Robots Season 2018-2019, they will participate in Episode 7 – Shopping pick and pack and Episode 10- Open the door, both episodes involve manipulation tasks.

Team b-it-bots. Photo Credits: European Robotics League

“You learn a lot of things at University, but not always how to apply them in real life. Robotics competitions are the best place for students to use their knowledge in real scenarios and learn from mistakes. In this competition we are solving problems which do not have a solution yet, so the students cannot download a tutorial or watch a YouTube video, they need create their own engineered solutions. This is learning by doing, not learning by listening” says Deebul Nair, b-it-bots Team Manager.

5. Leeds Autonomous Service Robots – The team of the newly established AI group of the University of Leeds, UK, studies long-term decision making and adaptation. They will demonstrate how it is applied in robotics by participating in Episodes 3, 4 and 10.

6. HEARTS – The Healthcare Engineering and Assistive Robotics Technology and Services (HEARTS) team is based in the Bristol Robotics Laboratory, a collaboration between the University of the West of England (UWE) and the University of Bristol, UK. The team was formed to provide an opportunity for students to get hands-on experience of developing assistive robots that are robust and reliable to assist people in a range of situations. The HEARTS will participate in Episode 4 – Take the elevator.

“Participating in the SciRoc challenge gives me the opportunity to use a social robot platform like Pepper in applications different from the ones of my PhD. Pepper is designed for interaction with humans, so it is a good platform for the episode of the elevator in which we are competing” explains Beth Mackey, PhD student and member of the HEART team.

Team HEARTS. Photo Credits: European Robotics League

7. UWE Aero – This aerial team of the University of the West of England, UK, is made up of a group of students interested in aerospace projects, focusing in Unmanned aerial vehicles (UAVs). Their backgrounds in aerospace engineering, 3D printing and computer science are a perfect combination to participate in Episode 12 – Fast delivery of emergency pills.

8. CATIE Robotics – The technology transfer center CATIE, France, created in early 2018 this robotics team with the aim of exploring service robotics. CATIE robotics was created in early 2018, and has participated at RoboCup@Home. The team aims to explore service robotics from an application-driven perspective.

“Competitions bring together people from different technical backgrounds under a common goal. They give visibility and the opportunity to be part of a community of experts. Robotics competitions are always very motivating” says Remi Fabre, Team Leader of CATIE robotics.

Team CATIE robotics setting up their robot. Photo Credits: European Robotics League

They will apply their knowledge in control and grasping in Episode 7 – Shopping pick and pack.

9. eNTiTy – Everbots – eNTiTy is the team of the R&D department of NTT Disruption, Spain. The team focuses on developing social robotics applications for clients. They first participated in the ERL Consumer tournament in IROS 2018 conference in Madrid.

“Participating in robotics competitions such as the SciRoc challenge help us advance the state of the art in social robotics and put together a good team of researchers. It gives us the opportunity to test different algorithms, such as vision modules, that we can then apply to other products” says Julian Caro Linares, robotics engineer of NTT Disruption.

They will participate with a PAL Robotics TIAGO robot in Episode 3 – Deliver coffee shop orders and Episode 4 – Take the elevator.
Irene Diaz-Portales, Computer Vision Researcher adds “we chose TiAGo because it’s an excellent robotics platform for developing ROS modules.”

Team eNTiTy testing TIAGO robot. Photo Credits: European Robotics League

10. TeamBathDrones Research – The TeamBathDrones Research is the University of Bath, UK, competitive autonomous aircraft team. The team is formed of a mixture of lecturers, PhD and undergraduate students from the engineering faculty. Through entering in the ERL Smart cities challenge in the emergency category, they aim to demonstrate the application of collision avoidance by in-flight risk minimisation in Episode 12 – Fast delivery of emergency pills.

Which teams will successfully address the SciRoc Challenge Episodes? Don’t miss the updates starting this week.

]]> RoboCup 2017 results https://robohub.org/robocup-2017-results/ Fri, 04 Aug 2017 20:56:28 +0000 http://robohub.org/robocup-2017-results/

It was a return to the source for RoboCup 2017, which took place last week in Nagoya Japan, 20 years after its launch in the same city.

Bigger than ever, the competition brought together roboticists from around the world. Originally focussed on robot football matches, RoboCup has expanded to include leagues for rescue robots, industrial robots, and robots in the home. Kids are also part of the fun, competing in their own matches and creative shows. You can watch video introductions of all of the leagues here, or watch a quick summary below.

And here’s a scroll through 5 hours of football glory from this year’s competition.

Congratulations to all the winners and participants!

]]> RoboCup video series: Junior league https://robohub.org/robocup-video-series-junior-league/ Wed, 21 Jun 2017 09:30:38 +0000 http://robohub.org/robocup-video-series-junior-league/

RoboCup is an international scientific initiative with the goal to advance the state of the art of intelligent robots. Established in 1997, the original mission was to field a team of robots capable of winning against the human soccer World Cup champions by 2050.

To celebrate 20 years of RoboCup, the Federation is launching a video series featuring each of the leagues with one short video for those who just want a taster, and one long video for the full story. Robohub will be featuring one league every week leading up to RoboCup 2017 in Nagoya, Japan.

In our final set of videos, we are featuring the RoboCupJunior league! RoboCupJunior is a project-oriented educational initiative that sponsors local, regional and international robotic events for young students. It is designed to introduce RoboCup to primary and secondary school children, as well as undergraduates who do not have the resources to get involved in the senior leagues yet.

Short version:

Long version:

You can view all the videos on the RoboCup playlist below:

https://www.youtube.com/playlist?list=PLEfaZULTeP_-bqFvCLBWnOvFAgkHTWbWC

Please spread the word! If you would like to join a team, click here for more information.

If you liked reading this article, you may also want to read:

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

]]> RoboCup video series: Industrial league https://robohub.org/robocup-video-series-industrial-league/ Wed, 14 Jun 2017 10:00:46 +0000 http://robohub.org/robocup-video-series-industrial-league/

RoboCup is an international scientific initiative with the goal to advance the state of the art of intelligent robots. Established in 1997, the original mission was to field a team of robots capable of winning against the human soccer World Cup champions by 2050.

To celebrate 20 years of RoboCup, the Federation is launching a video series featuring each of the leagues with one short video for those who just want a taster, and one long video for the full story. Robohub will be featuring one league every week leading up to RoboCup 2017 in Nagoya, Japan.

This week, we consider being part of the RoboCupIndustrial league. RoboCupIndustrial is a competition between industrial mobile robots focusing on logistics and warehousing systems. In anticipation of Industry 4.0, participants compete in automation through robots, autonomous systems, and mobile robot technology. You’ll hear about the history and ambitions of RoboCup from the trustees, and inspiring teams from around the world.

Short version:

Long version:

Want to watch the rest? You can view all the videos on the RoboCup playlist below:

https://www.youtube.com/playlist?list=PLEfaZULTeP_-bqFvCLBWnOvFAgkHTWbWC

Please spread the word! If you would like to join a team, click here for more information.

 If you liked reading this article, you may also want to read:

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

]]> RoboCup video series: @Home league https://robohub.org/robocup-video-series-home-league/ Wed, 07 Jun 2017 10:18:19 +0000 http://robohub.org/robocup-video-series-home-league/

RoboCup is an international scientific initiative with the goal to advance the state of the art of intelligent robots. Established in 1997, the original mission was to field a team of robots capable of winning against the human soccer World Cup champions by 2050.

To celebrate 20 years of RoboCup, the Federation is launching a video series featuring each of the leagues with one short video for those who just want a taster, and one long video for the full story. Robohub will be featuring one league every week leading up to RoboCup 2017 in Nagoya, Japan.

This week, we consider being part of the RoboCup@Home league. Robots helping at home can certainly ‘feel’ like the future. One day, these robots might help with various tasks around the house. You’ll hear about the history and ambitions of RoboCup from the trustees, and inspiring teams from around the world.

Short version:

Long version:

Want to watch the rest? You can view all the videos on the RoboCup playlist below:

https://www.youtube.com/playlist?list=PLEfaZULTeP_-bqFvCLBWnOvFAgkHTWbWC

Please spread the word! and if you would like to join a team, check here for more information.

 

If you liked reading this article, you may also want to read:

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

]]> Robohub Digest 05/17: RoboCup turns 20, ICRA in Singapore, robot inspector helps check bridges https://robohub.org/robohub-digest-0517-robocup-turns-20-icra-in-singapore-robot-inspector-helps-check-bridges/ Mon, 05 Jun 2017 14:50:02 +0000 http://robohub.org/robohub-digest-0517-robocup-turns-20-icra-in-singapore-robot-inspector-helps-check-bridges/

A quick, hassle-free way to stay on top of robotics news, our robotics digest is released on the first Monday of every month. Sign up to get it in your inbox.

20 years of RoboCup

20 years in the books! RoboCup, which first started in 1997, was originally established to bring forth a team of robots that could beat the human soccer World Cup champions. Twenty years on, RoboCup is so more than just a soccer competition. In fact, the competition has grown into an international movement with a variety of leagues. Teams compete against each other in four different leagues and many sub-competitions, including home, work, and rescue missions. The complexity of missions in RoboCup requires intelligent, dynamic, sensing robots that can react to chaotic and changing environments. And in its 20-year history, the competition has brought forth numerous winners who have gone on to achieve great things.

Without robot competitions like RoboCup, the field of robotics wouldn’t be where it is today. So to celebrate 20 years of RoboCup, the Federation launched a video series featuring each of the leagues with one short video for those who just want a taster, and one long video for the full story. Robohub will be featuring one league every week leading up to RoboCup 2017 in Nagoya, Japan. You can watch all videos from the playlist here.

ICRA 2017 in Singapore

While we have RoboCup 2017 to look forward to, the IEEE 2017 International Conference on Robotics and Automation (ICRA) took place in Singapore. Under the conference theme “Innovation, Entrepreneurship, and Real-world Solutions”, the event brought together engineers, researchers, entrepreneurs and industry to address some of the major challenges of our times.

This year’s keynote speakers included Louis Phee, who described development and implementation of a surgical robot called EndoMaster, and Peter Luh, giving an overview of Industry 4.0. Alongside the speeches, the event included 25 workshops and tutorials, various exhibitions, as well as four Robot Challenges.

Self-driving cars: Competition heats up

May wasn’t just events and competitions. In the world of autonomous vehicles, French Groupe PSA, the second largest car manufacturer in Europe which owns brands such as Peugeot and Citroen, have teamed up with autonomous car maker nuTonomy. The collaboration will seek to build a self-driving Peugeot 3008, which they hope will hit the roads of Singapore in the not-too-distant future.

Groupe PSA and other well-known car manufacturers, including Ford Motors, who are just starting their bid to enter the autonomous car market, are lagging way behind Waymo (the self-driving car developed by Google’s parent Alphabet) when it comes to putting their cars on actual roads. And with Waymo about to team up with ride-hailing start-up Lyft, Google is getting ever closer to making their autonomous car part of mainstream traffic.

Another player in the self-driving car game we haven’t mentioned yet is cab service Uber. The company is locked in a dispute with Google over allegedly stolen design secrets and will likely be going to public trial later in the year. Linked to the lawsuit, Uber fired the engineer at the heart of the dispute, Anthony Levandowski, who is suspected of having stolen company secrets when he left Google and founded his own company, Otto, which was acquired by Uber last year. The information war continues.

Self-driving cars: Innovation

Meanwhile, a group of veterans previously linked with Alphabet founded their own company – DeepMap Inc. – which aims to develop systems that allow cars to navigate complex cityscapes.

It’s not just cityscapes that are complex to navigate. Considering other vehicles, cyclists, and pedestrians on busy roads form part of the difficult environment a self-driving car has to cope with due to its unpredictable nature. It will, therefore, become necessary for autonomous cars to understand and predict behaviour. Here, machine learning will be key, as Dr Nathan Griffiths explains in The Royal Society’s blog on machine learning in research.

And with so much interest and research into autonomous, intelligent cars, it’s not surprising that some, like Chris Urmson in a recent lecture at CMU, predict we will see a shift from the traditional transport model where people own their cars to a more dynamic, responsive model of “Transportation as a Service”, in which, companies own fleets of cars that can be used by anyone when and where they’re needed.

Robots in the fields

Much of the innovations that have enabled autonomous cars are transferable to industrial, commercial and agricultural vehicles. In the case of the latter, self-driving tractors and precision agribots have already increased productivity and made 24-hour autonomous, high-yield farming a possibility.

In Salinas Valley, California, robots are already used to pick lettuce, and to help vineyard owners decide when they need to water their plants. And GV (formerly Google Ventures) just invested $10 million into Abundant Robotics to build a picking robot, initially to pick apples but with potential for adaptation to support the harvest of other fruit.

It is believed the uptake of farming technologies is not progressing as quickly as it could be, due to farmers being slow to accept precision agriculture products, software, equipment and practices. But the farming (r)evolution is well underway, as agricultural robotics are already a 3 billion dollar industry set to grow to an impressive 12 billion by 2026.

Robots in the skies

While robots are still waiting to be fully accepted in agriculture, it’s no secret that the US military uses drones extensively. What came as a surprise was the strange video feed that surfaced in May from what appeared to be a drone flying over Florida’s panhandle, apparently sponsored by the National Reconnaissance Office – a body that doesn’t usually publicise its drone-related activities. The footage, now believed to have been shot in February, was likely uploaded as a demo video by a contractor.

While the Florida video caused quite a bit of confusion this month, another drone was making headlines for very different reasons. Previously used in military operations, the ArcticShark (a modified version of the military TigerShark) drone is now helping to fight climate change. And it’s now on a scientific mission to help scientists understand cloud formation and other atmospheric processes.

In other drone-related news, the Alaska Department of Transportation and Public Facilities has allowed some of its employees to receive licenses to operate drones to support projects involving roads, bridged and other structures. And a report has shown that drone funding fell by 64% in 2016 compared to 2015, with DJI dominating the market and issues such as battery life, connectivity problems and drone regulations stifling development efforts.

Robots in the water

From the skies to the sea: this May, NATO Nations agreed to use JANUS for their digital underwater communications. JANUS has the potential to make military and civilian, NATO and non-NATO, devices fully interoperable, doing away with the communication problems between systems and models by different manufacturers that have made underwater communication difficult up to this point.

Robots in the lab

Researchers at the Institute for Human and Machine Cognition in Pensacola, Florida, have developed a two-legged robot that can run without using sensors and a computer. The robot, called Planar Elliptical Runner, is stable through its physical design, which makes it different to other two or four-legged robots.

Meanwhile, a team at the University of California, Berkeley, has come up with a nimble-fingered robot that is able to pick up a wide range of objects using a 3D sensor and a deep learning neural network. It may not be perfect, but it’s the most nimble-fingered robot yet and the technology may find applications in picking and manufacturing in future.

Human-Machine Interaction

Most of the robots we interact with are practical helpers, offering support in the home, on the road or at work. Innovations some of us may have interacted with are autonomous lawn mowers, cars with self-driving features, or Amazon’s Alexa. With there plenty more robots in the pipeline.

A four-wheeled, waterproof, battery-powered robot inspector developed by a team in Nevada, may soon be supporting civil engineers and safety inspectors with bridge checks to reduce the chance of human error and omissions that could lead to a collapse such as the I-35W bridge disaster in Minnesota on 2007.

https://www.youtube.com/watch?v=kUXC7vMjEa8

And finally, engineers in Germany have built a robot priest called BlessU-2 that can beam light from its hands and deliver blessings in five languages. The robot is meant to spark discussions about faith, the church and the potential of AI.

Learning resources: Robot Academy

And to finish off our digest for May, we wanted to highlight a new open online resource for robotics education: the Robot Academy. Developed by Professor Peter Corke and the Queensland University of Technology (QUT), the Academy offers more than 200 lessons from robot joint control architecture to limits of electric motor performance. So if you find yourself with a bit of time on your hands, why not try a Robot Academy Masterclass?

Or if you’re after something a little bit different, there’s a new toolkit on “Computational Abstractions for Interactive Design of Robotic Devices” that allows you to drag and drop parts of a virtual robot on screen without the need to know exactly what needs to connect to what as a complex physics engine ensures your robot won’t fail or fall over.

Missed any previous Digests? Click here to check them out.

Upcoming events for June – July 2017

Intelligent Ground Vehicle Competition: June 2-5, Rochester, MI.

CES Asia: June 7-9, Shanghai, China.

Unmanned Cargo Ground Vehicle Conference: June 13-14, Maaspoort, Venlo, The Netherlands.

Autonomous machines world: June 26-27, Berlin, Germany.

RoboUniverse: June 28-30, Seoul.

CIROS: July 5, 2017 – July 8, 2017, Shanghai, China. 

ASCEND Conference and Expo: July 19, 2017 – July 21, 2017, Portland, OR 

RoboCup: July 25, 2017 – July 31, 2017, Nagoya, Japan 

Call for papers:

1st Annual Conference on Robot Learning (CoRL 2017): Call for papers deadline 28 June.

]]> RoboCup video series: Rescue league https://robohub.org/robocup-video-series-rescue-league/ Wed, 31 May 2017 14:34:17 +0000 http://robohub.org/robocup-video-series-rescue-league/

RoboCup is an international scientific initiative with the goal to advance the state of the art of intelligent robots. Established in 1997, the original mission was to field a team of robots capable of winning against the human soccer World Cup champions by 2050.

To celebrate 20 years of RoboCup, the Federation is launching a video series featuring each of the leagues with one short video for those who just want a taster, and one long video for the full story. Robohub will be featuring one league every week leading up to RoboCup 2017 in Nagoya, Japan.

Robotics isn’t only about playing soccer it’s also about helping people. This week, we take a look at what it takes to be part of RoboCupRescue. You’ll hear about the history and ambitions of RoboCup from the trustees, and inspiring teams from around the world.

Short version:

Long version:

Want to watch the rest? You can view all the videos on the RoboCup playlist below:

https://www.youtube.com/playlist?list=PLEfaZULTeP_-bqFvCLBWnOvFAgkHTWbWC

Please spread the word! and if you would like to join a team, check here for more information.

If you liked reading this article, you may also want to read:

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

]]> RoboCup video series: Soccer league https://robohub.org/robocup-video-series-robocup-soccer-league/ Wed, 24 May 2017 11:11:27 +0000 http://robohub.org/robocup-video-series-robocup-soccer-league/

RoboCup is an international scientific initiative with the goal to advance the state of the art of intelligent robots. Established in 1997, the original mission was to field a team of robots capable of winning against the human soccer World Cup champions by 2050.

To celebrate 20 years of RoboCup, the Federation is launching a video series featuring each of the leagues with one short video for those who just want a taster, and one long video for the full story. Robohub will be featuring one league every week leading up to RoboCup 2017 in Nagoya, Japan.

This week, we take a look at the heart-pumping excitement watching the popular RoboCup soccer leagues. You’ll hear about the history and ambitions of RoboCup from the trustees, and inspiring teams from around the world.

Short version:

Long video:

Can’t wait to watch the rest? You can view all the videos on the RoboCup playlist below:

https://www.youtube.com/playlist?list=PLEfaZULTeP_-bqFvCLBWnOvFAgkHTWbWC

Please spread the word! and if you would like to join a team, check here for more information.

If you liked reading this article, you may also want to read:

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

]]> What has twenty years of RoboCup taught us? https://robohub.org/what-has-twenty-years-of-robocup-taught-us/ Tue, 23 May 2017 09:54:53 +0000 http://robohub.org/what-has-twenty-years-of-robocup-taught-us/ Read More ›]]>

In 1985, a twenty-two year old Garry Kasparov became the youngest World Chess Champion. Twelve years later, he was defeated by the only player capable of challenging the grandmaster, IBM’s Deep Blue. That same year (1997), RoboCup was formed to take on the world’s most popular game, soccer, with robots. Twenty years later, we are on the threshold of the accomplishing the biggest feat in machine intelligence, a team of fully autonomous humanoids beating human players at FIFA World Cup soccer.

Many of the advances that have led to the influx of modern autonomous vehicles and machine intelligence are the result of decades of competitions. While Deep Blue and AlphaGo have beat the world’s best players at board games, soccer requires real-world complexities (see chart) in order to best humans on the field. This requires RoboCup teams to combine a number of mechatronic technologies within a humanoid device, such as real-time sensor fusion, reactive behavior, strategy acquisition, deep learning, real-time planning, multi-agent systems, context recognition, vision, strategic decision-making, motor control, and intelligent robot control.

 

Professor Daniel Lee of University of Pennsylvania’s GRASP Lab described the RoboCup challenges best, “Why is it that we have machines that can beat us in chess or Jeopardy but we can beat them in soccer? What makes it so difficult to embody intelligence into the physical world?” Lee explains, “It’s not just the soccer domain. It’s really thinking about artificial intelligence, robotics, and what they can do in a more general context.”

RoboCup has become so important that the challenge of soccer has now expanded into new leagues that focus on many commercial endeavors from social robotics, to search & rescue, to industrial applications. These leagues have a number of subcategories of competition with varying degrees of difficulty. In less than two months, international teams will convene in Nagoya, Japan for the twenty-first games. As a preview of what to expect, let’s review some of last year’s winners. And just maybe it could give us a peek at the future of automation.

RoboCup Soccer

While Iran’s human soccer team is 28th in the world, their robot counterparts (Baset Pazhuh Tehran) won 1st place in the AdultSize Humanoid competition. Baset’s secret sauce is its proprietary algorithms for motion control, perception, and path planning. According to Baset’s team description paper, the key was building a “fast and stable walk engine” based upon the success of past competitions. This walk engine was able to combine “all actuators’ data in each joint, and changing the inverse and forward kinematics” to “avoid external forces affecting robot’s stability, this feature plays an important role to keep the robot standing when colliding to the other robots or obstacles.” Another big factor was their goalkeeper that used a stereo vision sensor to detect incoming plays and win the competition by having “better percepts of goal poles, obstacles, and the opponent’s goalkeeper. To locate each object in real self-coordinating system.” The team is part of a larger Iranian corporation, Baset, that could deploy this perception in the field. Baset’s oil and gas clients could benefit from better localization techniques and object recognition for pipeline inspections and autonomous work vehicles. If by 2050 RoboCup’s humanoids will be capable of playing humans in soccer, one has to wonder if Baset’s mechanical players will spend their offseason working in the Arabian peninsula?

RoboCup Rescue 


In 2001 the RoboCup organization added simulated rescue to the course challenge, paving the way for many life-saving innovations already being embraced by first responders. The course starts with a simulated earthquake environment whereby the robot performs a search and rescue mission lasting 20 minutes. The skills are graded by overcoming a number of obstacles that are designed to assess the robot’s autonomous operation, mobility, and object manipulation. Points are given by the number of victims found by the robot, details about the victims, and the quality of the area mapped. In 2016, students from the King Mongkut’s University of Technology North Bangkok won first place with their Invigorating Robot Activity Project (or iRAP).

Similar to Baset, iRap’s success is largely based upon solving problems from previous contests where they placed consistently in the top tier. The team had a total of four robots: one autonomous robot, two tele-operative robots, and one aerial drone. Each of the robots had multiple sensors related to providing critical data, such as CO2 levels, temperature, positioning, 2D mapping, images, and two-way communications. iRap’s devices managed to navigate with remarkable ease the test environment’s rough surfaces, hard terrains, rolling floor, stairs, and inclined floor. The most impressive performer was the caged quadcopter using enhanced sensors to localize itself within an outdoor search perimeter. According to the team’s description paper, “we have developed the autonomously outdoor robot that is the aerial robot. It can fly and localize itself by GPS sensor. Besides, the essential sensors for searching the victim.” It is interesting to note that the Thai team’s design was remarkably similar to Flyability’s Gimball that won first place in the UAE’s 2015 Drones for Good Competition. Like the RoboCup winner, the Gimball was designed specifically for search & rescue missions using a lightweight carbon fiber cage.

As RoboCup contestants push the envelope of navigation mapping technologies, it is quite possible that the 2017 fleet could develop subterranean devices that could actively find victims within minutes of being buried by the earth.

RoboCup @Home

The home, like soccer, is one of the most chaotic conditions for robots to operate successfully. It is also one of the biggest areas of interest for consumers. Last year, RoboCup @Home celebrated its 10th anniversary by bestowing the top accolade to Team-Bielefeld (ToBI) of Germany. ToBi built a humanoid-like robot that was capable of learning new skills through natural language within unknown environments. According to the team’s paper, “the challenge is two-fold. On the one hand, we need to understand the communicative cues of humans and how they interpret robotic behavior. On the other hand, we need to provide technology that is able to perceive the environment, detect and recognize humans, navigate in changing environments, localize and manipulate objects, initiate and understand a spoken dialogue and analyse the different scenes to gain a better understanding of the surrounding.” In order to achieve these ambitious objectives the team created a Cognitive Interaction Toolkit (CITK) to support an “aggregation of required system artifacts, an automated software build and deployment, as well as an automated testing environment.”

Infused with its proprietary software the team’s primary robot, the Meka M1 Mobile Manipulator (left) demonstrated the latest developments in human-robot-interactions within the domestic setting. The team showcased how the Meka was able to open previously shut doors, navigate safely around a person blocking its way, and recognize and grasp many household objects. According to the team, “the robot skills proved to be very effective for designing determined tasks, including more script-like tasks, e.g. ’Follow-Me’ or ’Who-is-Who’, as well as more flexible tasks including planning and dialogue aspects, e.g. ’General-PurposeService-Robot’ or ’Open-Challenge’.”

RoboCup @Work

The @Work category debuted in 2016 with the German team from the Leibniz Universität Hannover (LUHbots) winning first place. While LUHbots’ hardware was mostly off the shelf parts (a mobile robot KUKA youBot), the software utilized a number of proprietary algorithms. According to the paper, “in the RoboCup we use this software e.g. to grab objects using inverse kinematics, to optimize trajectories and to create fast and smooth movements with the manipulator. Besides the usability the main improvements are the graph based planning approach and the higher control frequency of the base and the manipulator.” The key to using this approach within a factory setting is its robust object recognition. The paper explains, “the robot measures the speed and position of the object. It calculates the point and time where the object reaches the task place. The arm moves above the calculated point. Waits for the object and accelerates until the arm is directly above the moving-object with the same speed. Overlapping the down movement with the current speed until gripping the object. The advantage of this approach is that while the calculated position and speed are correct every orientation and much higher objects can be gripped.”

Similar to other finalists, LUHbots’ object recognition software became the determining factor to its success. RoboCup’s goal of playing WorldCup soccer with robots may seem trivial, but its practice is anything but meaningless. In each category, the advances developed on the field of competitive science are paying real dividends on a global scale across many industries.

In the words of the RoboCup mission statement: “The ultimate goal is to ‘develop a robot soccer team which beats the human world champion team.’ (A more modest goal is ‘to develop a robot soccer team which plays like human players.’) Needless to say, the accomplishment of the ultimate goal will take decades of effort. It is not feasible with current technologies to accomplish this in the near future. However, this goal can easily lead to a series of well-directed subgoals. Such an approach is common in any ambitious or overly ambitious project. In the case of the American space program, the Mercury project and the Gemini project, which manned an orbital mission, were two precursors to the Apollo mission. The first subgoal to be accomplished in RoboCup is ‘to build real and software robot soccer teams which play reasonably well with modified rules.’ Even to accomplish this goal will undoubtedly generate technologies, which will impact a broad range of industries.”

* Editor’s note: thank you to Robohub for providing a twenty-year history of RoboCup videos. 

]]> RoboCup video series: 20 years of history https://robohub.org/robocup-video-series-20-years-of-history/ Wed, 17 May 2017 21:53:40 +0000 http://robohub.org/robocup-video-series-20-years-of-history/

RoboCup is an international scientific initiative with the goal to advance the state of the art of intelligent robots. Established in 1997, the original mission was to field a team of robots capable of winning against the human soccer World Cup champions by 2050. 

The competition has now grown into an international movement with a variety of leagues that go beyond soccer. Teams compete to make robots for rescue missions, the home, and industry. And it’s not just researchers, kids also have their own league. Last year, almost 3,000 participants and 1,200 robots competed.

To celebrate 20 years of RoboCup, the Federation is launching a video series featuring each of the leagues with one short video for those who just want a taster, and one long video for the full story. Robohub will be featuring one league every week leading up to RoboCup 2017 in Nagoya, Japan.

This week, we take a whirlwind tour of the RoboCup competition, spanning all the leagues. You’ll hear about the history and ambitions of RoboCup from the trustees, and inspiring teams from around the world.

Short Version

Long Version

Can’t wait to watch the rest? You can view all the videos on the RoboCup playlist below:
https://www.youtube.com/playlist?list=PLEfaZULTeP_-bqFvCLBWnOvFAgkHTWbWC

Please spread the word! and if you would like to join a team, check here for more information.

]]> How we evolved a winning strategy for the RoboCup competition by imitating nature https://robohub.org/how-we-evolved-a-winning-strategy-for-the-robocup-competition-by-imitating-nature/ Thu, 01 Sep 2016 14:51:22 +0000 http://robohub.org/how-we-evolved-a-winning-strategy-for-the-robocup-competition-by-imitating-nature/ The winning goal scored by our RoboCup team, Gliders2016 (in yellow), in the 2D Simulation final of 2016. Credit: Mikhail Prokopenko

The winning goal scored by our RoboCup team, Gliders2016 (in yellow), in the 2D Simulation final of 2016. Credit: Mikhail Prokopenko

“In football everything is complicated by the presence of the opposite team.” This observation by the French philosopher Jean-Paul Sartre has been cited in multiple contexts, ranging from tactical planning in team sports, to game-theoretic modelling of microeconomics, to full-blown robotic warfare.

The common thread in all these studies is conflict: adversarial interactions bring about a disorganisation that can disrupt the most prepared of schemes.

This operates across many domains. Once a design – whether a tactical plan or engineering blueprint – comes into a contact with some unforeseen circumstances – such as a new opponent strategy or natural disaster – then it is likely to crumble, no matter how detailed and pre-optimised the preparations were.

One way for a complex system to withstand sudden stresses is to exploit self-organisation of its constituent parts and adapt to changing circumstances.

But this comes at a cost. It is extremely hard to predict how a complex system is going to behave if you can only partition and analyse the behaviour of each of the parts separately.

So is there a way to anticipate the unexpected? This is the central challenge faced by “complex systems” researchers who design and build systems with inherent robustness and resilience.

Artificial evolution

We can illustrate this challenge by returning to football, and robot soccer in particular.

RoboCup is the World Cup of robot soccer. Now approaching its 20th anniversary, recent events have attracted more than 40,000 visitors and participants from more than 45 countries.

The final game of RoboCup-2016 Soccer Simulation 2D League in Leipzig, Germany, on July 3, 2016, between Gliders2016 (in red, University of Sydney & CSIRO, Australia) and HELIOS2016 (in yellow, Fukuoka University & Osaka Prefecture University, Japan).

It is also a leading benchmark for artificial intelligence (AI), robotics and multi-agent complex systems.

While the physical robot leagues tend to draw the most attention, there is also a simulation league, which focuses on advancing virtual AI.

In the RoboCup 2D Simulation League, two teams of 11 fully autonomous software programs (called “agents”) play soccer in a two-dimensional virtual soccer stadium, with no remote control. The entire RoboCup community has developed the open-source simulator and visualisation software over 20 years. It contains nearly a million lines of code.

Each agent has virtual visual, acoustic and physical sensors to detect what is going on around them. And each can perform some basic actions like running, turning and kicking the ball.

The main challenge is to derive the best possible action to execute at any specific time while facing the unexpected and disruptive actions of the opposing agents.

Each simulation step takes merely a tenth of a second, during which the entire sensory-motor cycle takes place within an agent. This starts with it receiving new sensory inputs from the simulator. It then evaluates possible choices – up to 100,000 per cycle – and sends the chosen action back to the simulator.

Gliders2016 team, which won the 2016 simulation competition in July this year, is a collaborative effort of the University of Sydney (Professor Mikhail Prokopenko, Dr Victor Jauregui) and CSIRO (Peter Wang, Dr Oliver Obst).

The author (centre podium, right) and Dr Obst (centre podium, left) claim the championship trophy at the 2016 RoboCup award ceremony in Leipzig, Germany. Credit: Natalia Kishigami

The author (centre podium, right) and Dr Obst (centre podium, left) claim the championship trophy at the 2016 RoboCup award ceremony in Leipzig, Germany. Credit: Natalia Kishigami

In order to optimise our team, we used a form of human-based evolutionary computation. This is how we innovated and tested new behaviours, improving performance from one artificial “generation” to another.

We evaluated the software using several supercomputer clusters, executing tens of thousands of experimental runs per day. The number of such trials is approaching 10 million.

The bigger picture

During testing, we didn’t just look at how intelligently the software agents performed individually or as a team. We also considered the bigger picture.

We looked at the complexity of the opposing team’s behaviour. And whether our software was easy to adapt, scalable and robust to changes.

We analysed how our team could respond to surprising novelties that the opposition might unleash during the next tournament. And whether it could recover from sudden setbacks during a game.

The winning goal scored by our RoboCup team, Gliders2016 (in yellow), in the 2D Simulation final of 2016. Credit: Mikhail Prokopenko

The winning goal scored by our RoboCup team, Gliders2016 (in yellow), in the 2D Simulation final of 2016. Credit: Mikhail Prokopenko

Many of these questions resonate with generic issues encountered in complex systems. We had to consider how a small change in the initial conditions – for example, increasing the risk tolerance in making a pass – can affect the overall outcome. Another aspect was whether the dynamics of the system tend to repeat particular trajectories. We also studied the role of randomness in the simulation and how to model hidden variables.

In short, we explored how complex systems can help in developing the RoboCup-winning team in 2016.

Inspiration from nature

One of the insights that inspired the tactics in our team was the patterns observed in flocks of birds and schools of fish. These are classic cases of complexity in biology.

In response to a predator, many schools of fish display complex patterns of spatial aggregation, so that small disturbances can quickly cascade through an entire group, transferring the survival-critical information.

For our RoboCup team, similar behaviour allows the virtual players to achieve a high degree of coherent mobility. On the one hand, the players are constantly refining their positions in response to opponent players. On the other hand, the repositioning is not erratic and the players move in co-ordinated ways.

The team tactics have also been evolved to counteract different types of opponents, creating a rich variety of behaviours and increasing robustness and resilience. We evaluated this approach using information theory, quantifying and predicting the “information flows” within the system, rather than just “data flows”. The key difference is that information provides context for data, and the information flows highlight the most relevant and salient dynamics.

The complexity of RoboCup teams will likely only grow with time, meeting new challenges and solving new problems, as the whole initiative and the technology keep advancing.

An entirely new generation of scientists and engineers will drive the innovations in 2036, when RoboCup marks another 20 years on its journey.

By then, it is quite likely that complex systems, as a field of study, will have taken a central stage in educational programs worldwide, integrating elements of information technology, physics, biology and mathematics, and developing future leaders in engineering, science and business.

This article was originally published on The Conversation. Read the original article.

If you enjoyed this article, you may also want to read:

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

]]> Robohub Digest 07/16: Tesla’s Autopilot question, lethal robots, RoboCup, and a brave 10-year-old https://robohub.org/robohub-digest-072016-teslas-autopilot-question-lethal-robots-robocup-and-a-brave-10-year-old/ Mon, 01 Aug 2016 12:35:58 +0000 http://robohub.org/robohub-digest-072016-teslas-autopilot-question-lethal-robots-robocup-and-a-brave-10-year-old/ digest_16-07

A quick, hassle-free way to stay on top of robotics news, our robotics digest is released on the first Monday of every month. Sign up to get it in your inbox.

Tesla crash and the ‘AutoPilot’ question

Earlier this year, Tesla announced that Joshua Brown was in a fatal car crash driving his Model S while the Autopilot system was activated. This tragic news eventually sparked a debate in July on whether Tesla should continue to allow their customers to use the Autopilot system, or whether they should disable the feature if a driver is not being diligent enough, with some questioning whether calling it ‘Autopilot’ was misleading and luring drivers into a false sense of security by thinking the car can drive itself. Autopilot is meant as driver assistance (a more sophisticated form of cruise control), not driver replacement.

Tesla’s Autopilot driven cars are still a long way off truly autonomous robocars – and drivers need to be aware of this. Nevertheless, rumours are afoot that the company may be working on their next big step: Autopilot 2.0 with LIDAR – a more advanced and safer self-driving system, possibly for their new Model 3, Model S, and X cars. And it seems Tesla is not about to stop there, as Elon Musk laid out the company’s master plan (Part Deux) in a blog post.

Tesla isn’t the only company thinking about autonomous cars. Numerous companies are racing to build the first real and successful generation of driverless cars. But taking autonomous cars from research labs and testing grounds to roads isn’t a simple process, as Karl Iagnemma from nuTonomy explained in the recent Robots Podcast, and it may be a while yet until we see driverless vehicles on our roads.

Legal and ethical debate: Can and should police use lethal robots?

Autonomous cars aren’t quite here yet but people in Dallas were starkly reminded that other types of robots are in use on our streets — when police used a bomb-defusing robot to kill a suspect who had already shot several officers. As it turns out, law enforcement has over 1,000 robots in use already across the US.

What happened in Dallas was not illegal, as police are allowed to use lethal force and the way in which the robot was used was probably the best of several bad options given the situation. But, it sparked numerous debates about the militarization of the police force and the legality of using remote controlled robots to kill. These ethical questions are justified and important, as there has been little discussion of the use of robots by police. Now, people are asking questions about accountability, about regulation, and about the use of robots by police in less extreme circumstances, too. A discussion that’s essential if robots, including bomb-diffusion units and drones, become standard tools in law enforcement.

The discussions around police use of robots come at the same time as a report revealing the civilian death toll from US drone airstrikes outside war zones – another controversial use of robots.

Know your drone

In other drone news, the (not quite so controversial) use of commercial drones for deliveries is going ahead – at least in the UK. Amazon has announced that it’ll start making test deliveries soon, while in the US FAA regulation still hinders the company from introducing AmazonPrime drone service. But there was some exciting drone news in the US too, when DJI, the world’s leading commercial drone manufacturer, and Washington State University announced they are working to develop UAV systems for agriculture. Plus, the 3rd annual International Drone Photography Contest crowned its worthy winners this month, providing us some stunning aerial videos and photos to enjoy.

Lastly, we were once again reminded that drones aren’t necessarily just for the air as two young engineers from start-up OpenROV used their Trident submarine drone to explore Lake Tahoe – broadcasting their dives over social media. What a great idea!

Making your life and home smarter, with robots

While Amazon is just about to test its drone delivery systems in the UK, self-driving delivery bots are about to hit the streets. People in Germany and the city of London may soon have their parcels and take-away meals delivered by Starship robots. So if you live in Germany or London, next time your doorbell rings there may well be a delivery bot waiting outside!

And robots won’t just come to your doorstep. If Jibo Inc. has their way they’ll be in your home, too. The company just brought on veteran researcher Michael I Jordan as an advisory board member to help their home robot Jibo have better conversations with human owners through machine learning, so it can really ‘lend a helping hand’ in the household.

Advancements in AI

Machine learning and AI have developed in leaps and bounds since its early inception – and Google’s DeepMind proved this once again as it made Google’s data centers more efficient by effectively cutting electricity costs by 50 percent! There is still a long way to go of course and challenges to be met, not least when it comes to testing how clever our AIs really are.

A study published in the Journal of Experimental and Theoretical Artificial Intelligence exposed a major flaw in the standard test for AI – the Turing test. The researchers found that by remaining silent, robots could essentially outsmart the test and pass it without ever uttering a word, as judges wouldn’t be able to determine whether they were interacting with a robot or a human. While many had previously pointed out various issues with the Turing test, the findings from this study essentially call into question what passing the test really means and how limited its potential is to answer any deeper questions about AI.

So, do we need a new test? Probably. But for now, there aren’t many alternatives. However, challenges that maybe combine AI with the need to perform reactive physical tasks could be one answer, as Daniel Polani explored whether soccer may be the final frontier for robotic AI.

RoboCup and other competitions

Speaking of soccer, hundreds of teams from countries around the world entered their smart, autonomous bots into the soccer competition, hoping to take away this year’s RoboCup soccer crown. With eight different soccer categories, there were a lot of entries and of course eight final winners, but Robohub thinks with such an amazing show that everyone won!

Also at Robocup “Team Delft” from the Netherlands took home $50,000 after winning the Amazon Picking Challenge at RoboCup 2016 in Leipzig, Germany. Their robot used a combination of an off-the-shelf robotic arm and deep learning techniques to beat its competitors.

Winning the competition is of course not the same as getting the robot into Amazon warehouses (yet), as the speed of the robot is still four times slower (100 items per hour) compared to human workers (400 items per hour). But the improvement between last year’s and this year’s entries was astonishing. We can’t wait for next year’s event – where we’ll undoubtedly see an impressive range of new robots on the soccer fields. But RoboCup 2017 won’t just be about soccer of course, as we’ll see more picking robots as well as a new competition for domestic helpers, for which Toyota’s HSR was just selected as the standard robot.

But RoboCup was not the only big event – far away from the robot soccer fields, the 2016 China International Robot Show (CIROS) crowned Rethink Robotic’s Sawyer robot winner of Best Industrial Robot 2016 in the CIROS Golden Finger Awards. This high-performance collaborative robot is designed to work alongside humans in manufacturing environments – something the judges felt was highly relevant in China’s ambitious economy.

Are we there yet? Turning points and revolutions

AUTOMATICA, the world’s largest robot and automation event, took place at the end of June, but the debates it stimulated about the future of industry carried over into July. Robohub Associate Editor, Adriana Hamacher, and SPARC robotics explored the promise of Industry 4.0 and how close we really are to ‘a turning point in the robotics and automation marketplace’.

And while Industry 4.0 may or may not be here yet (depending on your point of view), robots are certainly no longer just science fiction, and questions of robot ethics are becoming more relevant. Dr Yueh-Hsuan Weng interviewed European Parliment MEP Mady Delvaux-Stehres about the importance of new robot laws as the robot revolution continues.

Getting down to Robot Business

July robotics business news saw Softbank’s move to buy UK company ARM holdings for £24 billion and Midea’s confirmed majority ownership (85.69%) of KUKA shares. But while some other big acquisitions (such as Toyota’s plans to purchase Boston Dynamics) haven’t quite come to fruition, the ever increasing popularity of robotics is reflected in not just one, but more than a hundred reports looking at the robotics industry – with reports on drones making up a third (35)!

Robotics startups making waves

But of course, it’s not just about the big names – robotics start-ups make a huge contribution to the industry and often have some of the most exciting and innovative ideas and products for direct-to-consumer marketing. Success stories include consumer robot maker Anki with their Cozmo SDK toy robots that are more than just toys, Iran-based Ikap Robotics’ Olive, the world’s first intelligent suitcase, and the first ever open-source CNC farming machine by FarmBot, which just reached its $100,000 pre-order goal.

Start-up competition Robot Launch (by Silicon Valley Robotics and Robohub) has an open call to find the world’s best robotics and AI start-ups for 2016. Past winners of this competition have continued on winning public support and more awards, as well as, getting their product ready for market. It’s a prize that could change a start-up’s future – entries close on 15 August!

In the lab

Meanwhile in the lab, researchers are working on some seriously cool stuff. Engineers at the University of Michigan took their bipedal robot MARLO for a walk across the wave field (a patch of grass with an uneven, wavy surface). While the robot may have a looked a little unsteady, the algorithms it uses to keep its balance will be useful in other types of robotics, including prosthetics.

In Lausanne, Switzerland, researchers developed a salamander robot that can help to understand the evolution of vertebrates. And in the US, a team is working to recreate ancient vertebrate’s first step on dry land with a robot. Lastly, researchers at Harvard have designed a robotic stingray that could help our understanding of the human heart.

Research like this is fascinating and important if we want to make progress in robotics. But how good is the robotics community at communicating with the publics? And how can it ensure that people do not distrust or fear progress being made? In the aftermath of Brexit, Michael Szollosy explored on Robohub what lessons the robotics community can learn about communication and how the science community can engage people now.

Fun stories in robotics

To finish our Digest, we like to take a look back at a couple of happy robotics stories of the month, one of which, must be the story of 10 year old Eva, whose fascination with robots and determination to make the world a better place landed her a spot on the Paris Summer Innovation Fellowship programme. A summary of her pitch: “The streets of Paris are sad. I want to build a robot that will make them happy again. I’ve already starting learning how to code on Thymio robots, but I have trouble making it work. I want to join the program so the mentors can help me.” Well done, Eva!

Another great story is Verity Studios of Zurich in collaboration with Cirque du Soleil using flying robot lampshades as performance pieces on Broadway. Paramour just surpassed its 100th performance, still astonishing audience members with a spectacle of colours and lights. Some readers may recognise them as the direct siblings of the similarly dressed quadcopters in Sparked: A Live Interaction Between Humans and Quadcopters.

Upcoming events in August

ICMA 2016 – IEEE International Conference on Mechatronics and Automation: August 7-10, Harbin, China

DISCOVER 2016: IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics: August 13-14, National Institute of Technology Karnataka, Surathkal.

Next-Generation Collaborative Robotics & Automation Technologies Automotive Manufacturing 2016: August 17-18, Detroit, MI.

URAI 2016 – International Conference on Ubiquitous Robots and Ambient Intelligence: August 19-22, Renmin Square, Xian, China.

CASE 2016 – IEEE International Conference on Automation Science and Engineering: August 21-25, Fort Worth, TX.

ICRAE 2016- International Conference on Robotics and Automation Engineering: August 27-31, Jeju Island, Korea.

RO-MAN 2016 – IEEE International Symposium on Human and Robot Interactive Communication: August 26-31, Columbia University, New York.

MMAR 2016 – 21st International Conference on Methods and Models in Automation and Robotics: August 29 – September 1, Miedzyzdroje, Poland.

]]> Why football, not chess, is the true final frontier for robotic artificial intelligence https://robohub.org/why-football-not-chess-is-the-true-final-frontier-for-robotic-artificial-intelligence/ Wed, 20 Jul 2016 11:51:24 +0000 http://robohub.org/why-football-not-chess-is-the-true-final-frontier-for-robotic-artificial-intelligence/ Robocup 2013 in Eindhoven. Photo credit: Ralf Roletschek via Wikimedia Commons.

Robocup 2013 in Eindhoven. Photo credit: Ralf Roletschek via Wikimedia Commons.

The perception of what artificial intelligence was capable of began to change when chess grand master and world champion Garry Kasparov lost to Deep Blue, IBM’s chess-playing program, in 1997. Deep Blue, it was felt, had breached the domain of a cerebral activity considered the exclusive realm of human intellect. This was not because of something technologically new: in the end, chess was felled by the brute force of faster computers and clever heuristics. But if chess is considered the game of kings, then the east Asian board game Go is the game of emperors.

Significantly more complex, requiring even more strategic thinking, and featuring an intricate interweaving of tactical and strategical components, it posed an even greater challenge to artificial intelligence. Go relies much more on pattern recognition and subtle evaluation of the general positions of playing pieces. With a number of possible moves per turn an order of magnitude greater than chess, any algorithm trying to evaluate all possible future moves was expected to fail.

Until the early 2000s, programs playing Go progressed slowly, and could be beaten by amateurs. But this changed in 2006, with the introduction of two new techniques. First was the Monte Carlo tree search, an algorithm that rather than attempting to examine all possible future moves instead tests a sparse selection of them, combining their value in a sophisticated way to get a better estimate of a move’s quality. The second was the (re)discovery of deep networks, a contemporary incarnation of neural networks that had been experimented with since the 1960s, but which was now cheaper, more powerful, and equipped with huge amounts of data with which to train the learning algorithms.

The combination of these techniques saw a drastic improvement in Go-playing programs, and ultimately Google DeepMind’s AlphaGo program beat Go world champion Lee Sedol in March 2016. Now that Go has fallen, where do we go from here?

The future of AI is in physical form

Following Kasparov’s defeat in 1997, scientists considered that the challenge for AI was not to conquer some cerebral game. Rather, it needed to be physically embodied in the real world: football.

Football is easy for humans to pick up, but to have a humanoid robot running around a field on two legs, seeing and taking control of the ball, communicating under pressure with teammates, and all mostly without falling over, was considered completely out of the question in 1997. Only a handful of laboratories were able to design a walking humanoid robot. Led by Hiroaki Kitano and Manuela Veloso, the ambitious goal set that year was to have by 2050 a team of humanoid robots able to play a game of football against the world champion team according to FIFA rules, and win. And so the RoboCup competition was born.

The RoboCup tournament held its 20th competition in Leipzig this year. Its goal has always been to improve and challenge the capacity of artificial intelligence and robotics, not in the abstract but in the much more challenging form of physical robots that act and interact with others in real time. In the years since, many other organisations have recognised how such competitions boost technological progress.

The first RoboCup featured only wheeled robots and simulated 2D football leagues, but soon leagues that permitted Sony’s four-legged AIBO robot dogs were introduced and, since 2003, humanoid leagues. In the beginning, the humanoids’ game was quite limited, with very shaky robots attempting quivering steps, and where kicking the ball almost invariably caused the robot to fall. In recent years, their ability has significantly improved: many labs now boast five or six-a-side humanoid robot teams.

No ordinary ballgame

In order to push competitors on to reach the goal of a real football match by 2050, the conditions are made harder every year. Last year, the green carpet was replaced by artificial turf, and the goalposts and the ball coloured white. This makes it harder for robots to maintain stability and poses a challenge of recognising the goals and ball. So while the robots may seem less capable this year than the year before, it’s because the goalposts are moving.

The tasks involved in playing football, although much more intuitive to humans than chess or Go, are a major challenge for robots. Technical problems of hitherto unimaginable complexity have to be solved: timing a kick while running, identifying the ball against a glaring sun, running on wet grass, providing the robot with sufficient energy for 45 minutes’ play, even the materials that go into constructing a robot can’t disintegrate during a forceful game. Other problems to be solved will define important aspects of our life with robots in the future: when a robot collides with a human player, who can take how much damage? If humans commit fouls, may a robot foul back?

RoboCup offers up in miniature the problems we face as we head towards intelligent robots interacting with humans. It is not in the cerebral boardgames of chess or Go, but here on the pitch in the physical game of football that the frontline of life with intelligent robots is being carved out.

This article was originally published on The Conversation. Read the original article.

If you enjoyed this article, you may also want to read:

]]> Live coverage: Robocup 2016 https://robohub.org/live-coverage-robocup-2016/ Fri, 01 Jul 2016 01:13:16 +0000 http://robohub.org/live-coverage-robocup-2016/ RoboCup 2016, Credit: Tom Schulze

RoboCup 2016, Credit: Tom Schulze

Robocup 2016 kicks off today in Leipzig, Germany. The event brings together 3,500 participants from 45 countries and regions competing in 17 disciplines.

“After ten years, RoboCup is finally coming back to Germany,” says Prof. Gerhard Kraetzschmar, General Chair of RoboCup 2016. “Innovation is what drives our society. Therefore we are particularly happy to welcome innovative minds of all ages to RoboCup in Leipzig, and to take part in a unique event.”

Soccer, Rescue, Industrial & Home

RoboCup 2016, Credit: Tom Schulze

RoboCup 2016, Credit: Tom Schulze

Young scientists, Ph.D. students and professors will be competing in the RoboCup Major category. Soccer-playing robots are at the center of most competitions. Three other disciplines focus on application scenarios. As part of RoboCup@Home, robots demonstrate how they can help with household tasks, while robots in RoboCup Rescue conduct a search and rescue mission, and yet other robots optimize work and logistics processes in RoboCup Industrial.

In RoboCup Junior, students up to 20 years of age use their robots to play soccer, present a choreography, or master a rescue course.

RoboCup 2016, Credit: Tom Schulze

RoboCup 2016, Credit: Tom Schulze

The Amazon Picking Challenge is also running in parallel to RoboCup this year. The competition features robots that pick and place everyday objects on a shelf, you may remember the competition from our ICRA coverage in 2015.

Europe, under the umbrella of SPARC, also launched the new European Robotics League (ERL) featuring competitions focussed on robots at work, at home, and in rescue scenarios. The first teams will be scored following the ERL rules during the RoboCup competition. As in real sport, the idea is to allow teams to meet for “matches” in dedicated arenas throughout the world. Their points will be tallied up over the year to determine the winners of the league.

We’ll be posting pictures, videos, and results here, in the mean time, you can follow live tweets below. Some of the teams even have their own live stream!


]]> In praise of robot football https://robohub.org/in-praise-of-robot-football/ Tue, 09 Sep 2014 20:51:00 +0000 http://robohub.org/in-praise-of-robot-football/ Republished here is a short piece for The Conversation reflecting on robot football: 4-4-2 becomes 0101: inside the competitive world of robot football, published 4 August 2014.

This summer the whistle was blown on one of the most thrilling events on the international sporting calendar. It took place in Brazil and pitted teams from all over the world against each other, each hoping to make it into the history books. But no managers were fired, no grass had to be watered and certainly no one got bitten. The event was the Robocup, a tournament that sees professional footballers replaced by robots. It’s one of a number of regular tournaments for teams of programmers and robotics experts to show off their latest work.
The Robocup standard platform league matches play out on a much smaller scale than your average World Cup match. An arena of around 6 metres by 9 metres is marked out as a miniature pitch and 10, rather than 22 players file on to battle it out. The players are NAO robots, state of the art bipedal humanoid robots that stand about 60cm tall.

This is not what you might describe as a high-speed contest. The robots walk to take a kick or a tackle and, really, waddle might be a more apt word for their approach. The ball never gets lofted into the air and that’s probably for the best – a header might cause a serious malfunction.

2014 RoboCup SPL Grand Final

But the game is far from boring. Sitting around the arena, boys and girls (with family standing behind) are rapt, cheering with every contact. And make no mistake, the robots are properly playing. They pass, position and defend just like human players.

On either side of the pitch a row of desks can be found. This is where the brains behind the teams sit. Behind a row of laptops, they anxiously watch their players perform. But they are not controlling the robots. These coder/managers send the command to start the players when the referee signals kick-off, but during the match the robots are completely autonomous.

This is what makes robot football so extraordinary. The robots are not just being moved around the pitch by remote control; they are making their own decisions. They control where they run (or waddle), pass the ball and shoot for the goal without any live direction from a human. Their choices are based on what they see and the position of the ball, their teammates and the opposing team.

It’s what’s inside that counts

While a team of human players often comes complete with a dazzling array of ridiculous haircuts and tattoos, it is much harder to tell a team of robots apart. All the players are physically identical – the only visible differences on a robot football pitch are coloured markings to differentiate the two teams.

But appearances can be deceptive. Under their plastic skins the teams are far from the same. Each runs completely different software that has been painstakingly crafted by the team coders. The software to make these robots play football cannot be downloaded from an app store. It has to be crafted from scratch. Football is a complex sport and there are potentially limitless strategies that a team could use to win. This is hard-core coding.

The contest is, in effect, a battle of software. All things being equal – and at the moment they pretty much are – the team with the smartest programming, coding the cleverest plays will emerge victorious. At the end of the first-half the robots are brought to a halt. At this point, the team coders can be seen furiously attacking their laptops. This is their chance to quickly modify their robots’ software after seeing how they performed in the first half. They might have as little as ten minutes to do it, which seems like a risky strategy.

There’s a chance that the coders could make a mistake that renders the robots incapable of doing anything at all, let alone play a better game, but it’s a chance worth taking. If, in the first-half, the other team breaks out some nifty new moves, running rings (perhaps literally) around their opponents, this is the best opportunity the coders will get to raise their team’s game. It’s the robot equivalent of the tough talking in the half-time dressing room.

It’s easy to see why Robocup and the FIRA world cup, the two major international competitions, are so successful. Both contests have been running since around 1996. Some teams enter every year, building tremendous experience and a sophisticated code base. And several world-leading research groups use these contests as a test-bed for new approaches to multi-robot collaboration, publishing their findings in leading robotics journals afterwards.

As a robotics competition, robot football ticks all the boxes: a game with universal appeal yet also hugely demanding for robots; it’s a fun way for young roboticists to learn robot programming, and it’s a great spectator sport too.

Acknowledgements: this article was commissioned and edited by The Conversation Technology Editor Laura Hood.

Related blog posts:
FIRA 2012 Robot World Cup to be hosted by the Bristol Robotics Lab

]]> ShanghAI Lectures: Xiao’an Li “Action Skill Developmental Learning for Autonomous Soccer Robots” https://robohub.org/shanghai-lectures-xiaoan-li-action-skill-developmental-learning-for-autonomous-soccer-robots/ Thu, 20 Feb 2014 16:08:56 +0000 http://robohub.org/?p=17250
Robocub 2013 in Eindhoven.

DustinLi_0Guest talk in the ShanghAI Lectures, 2009-12-17

In the past decade, soccer robots have been developed greatly, and some of the robot soccer games have been showing more and more attractions. However, many open problems such as how to train soccer robots in the real world, and how the soccer robots learn the action skills incrementally or communicatively have been challenging us.

https://www.youtube.com/watch?v=nygqkdknA-I

In this talk, we will firstly introduce the motivations for our research. Then, some prospective approaches related with developmental learning of autonomous robots will be discussed. Also, we will propose some ideas and show some practical work for action skill developmental learning in autonomous mobile robots. At last, we will point out the potential advantages of developmental learning and the further research directions.

Xiaoan Li received the B.S. and M.S. degrees in Computer Information Engineering in 1987, Control Theory and Application in 1990, and Ph.D. degree in Computer Application Technology in 2000, respectively, all from the department of computer, Northwestern Polytechnical University, Xi’an city, China. Since 1997 he has been an associate professor. From May 2008 to May 2009, he was a visiting scholar in the Department of Computer Science and Engineering at the Michigan State University. Dr. Li is the founder of the Soccer Robot team in Northwestern Polytechnical University, and the chief advisor from 2003 to 2009. His research interests include computational intelligence, learning and cooperation of multi-agent robot system, the real-time intelligent control.

The ShanghAI Lectures are a videoconference-based lecture series on Embodied Intelligence, run and organized by Rolf Pfeifer (from 2009 till 2012), Fabio Bonsignorio (since 2013), and me with partners around the world. 

The ShanghAI Lectures have brought us a treasure trove of guest lectures by experts in robotics. You can find the whole series from 2012 here. Now, we’re bringing you the guest lectures you haven’t yet seen from previous years, starting with the first lectures from 2009 and releasing a new guest lecture every Thursday until all the series are complete. Enjoy!

]]> ep.003: Robots Podcast: Robot soccer, with Manuela Veloso https://robohub.org/robots-robot-soccer/ Fri, 04 Jul 2008 06:03:00 +0000 http://robotspodcast.com/beta/wordpress/?p=6

Link to audio file (32:34)

To close this year’s soccer season after Spain’s victory in the EUROCUP we went to the robotic kingdom to see who was driving the game. With Prof. Manuela Veloso from Carnegie Mellon University and President-Elect of
the International RoboCup Federation, we’ll be looking behind the scene of the best known competition in robot soccer. Finally, we’ll be discussing the future of artificial dribblers and their odds against the human 2050 world champions with a poll and discussion on our forum.

Manuela Veloso

Robot SoccerManuela Veloso is Professor of Computer Science at Carnegie Mellon University in Pittsburgh Pennsylvania, President-Elect of
the International RoboCup Federation and leader of the Coral Research Group (Cooperate, Observe, Reason, Act, and Learn). Since the first official Robocup soccer games in 1997, she’s been active in presenting teams to the Sony AIBO 4-legged league, the smallsize league, simulation league and Segway league with a great success at adding stars to the shirts of her robotic players with numerous first places in world and US championships.

In this episode we’ll be focusing on CMDragons’ cookie-box-like omni-wheeled robots from the small size league and their off-board perception.

We’ll also be looking at how the CMDash team has tamed the AIBO robot dogs to perceive their world and cooperate in a decentralized manner. Veloso gives us some insight on the challenges related to competing against different opponents and the need for teams to adapt during the games.

Finally, since the AIBOs are no longer produced, the RoboCup Federation is now making way for the Nao humanoid as the next challenging platform in robot soccer along with other research driving leagues such as the Nanogram league previously featured in Talking Robots (see Brad Nelson‘s interview).

Poll

Do you think robots will be able to beat humans at soccer by 2050?

Yes
No


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Pixar Animation’s Wall-E Robot Movie, the cutest robots as selected by TIME magazine and the 2008 European Robot Football Cup mentioned in the podcast.

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