Dear robotics graduate students and newcomers to robotics,

If you are what I imagine delving into robotics to be like today, the majority of your time is spent as follows:

• navigating Slack channels while tuning into some online lectures,
• trying to figure out whether you should be reading more papers, coding more, or if you are just a slow reader/coder/etc,
• and if you are a grad student, in particular, having a never-ending cycle of self-doubt questions that seem super important, such as “what is a research question anyway? And how do you find one that no one has tackled before — and not because it’s a dumb question? Oh wait… is my question dumb? Can I find out without asking my prof?”

Much of the questions I had as a grad student stemmed from my lack of knowledge about what is considered to be normal by academia, the robotics community, and more narrowly the subdomain of robotics I belonged to. The friends/colleagues/people I met along the way helped me fill that much-needed knowledge gap about the norm because many of us were on the same journey with similar struggles/questions.

As an assistant professor who spent the majority of my professorship in the COVID-19 pandemic mode, I worry that my students’ grad school journey has not offered the same kind of shared experience and camaraderie with people in the domain that I am now seeing the huge benefit of.

The upcoming IEEE International Conference on Robotics and Automation 2022 will be the first robotics conference that many of you attend in-person since the pandemic (I’m in this category). For many of you, it may be your first time attending an academic conference. For even more of you, this may be your first virtual attendance at ICRA.

ICRA is a multi-track, full-week of robotics festivity that draws in thousands. It can pass by you in a blink.

So, in a series of short blog posts (because, who has the time these days), I am going to highlight a few things in the form of a millennial’s guide to ICRA.

I’m assuming that you, the reader, may be as impatient a reader as I am, who likes information presented in a short, snappy, and organized way. The more bullet points the better.

So let’s get started.

** Full disclosure, I’m one of the two publicity co-chairs for the ICRA conference. If you want to be on the grounds of ICRA as a student science communicator, reach out to us. **

Image credit: wata1219 on flickr (CC BY-NC-ND 2.0)

It has been almost half a year since the mass resignation of the editors and editorial board of the Journal of Field Robotics. In a new turn of events, Peter Corke has recently relaunched Field Robotics as an online open-access journal with the old editorial board. Field Robotics deals with the fundamentals of robotics in unstructured and dynamic environments. Papers are now being accepted at their website.

The story of the mass resignation was reported on Silicon Valley Robotics on 26 August, in their post Is it farewell to the Journal of Field Robotics?, which we will reproduce below.

Original post from Silicon Valley Robotics

2020 is proving to be a watershed year. First COVID-19 has forced the cancellation (eg. ROSCon 2020, Hannover Messe) or the complete redesign of almost every major robotics conference. Now it seems that the science publishing community is also undergoing a sea change, as yesterday’s mass resignation of the editors and editorial board of the Journal of Field Robotics suggests. Stay posted for updates about the future of a field robotics research journal.

August 25, 2020

Dear Colleagues,

We would like to inform you about an upcoming major transition for the Journal of Field Robotics.

After 15 years of service, John Wiley and Sons, the publisher has decided not to renew the contract of the Editor in Chief (Sanjiv Singh) and the Managing Editor (Sanae Minick) and hence our term will expire at the end of 2020.

This comes after two years of discussions between new Wiley representatives and the Editorial Board have failed to converge to a common set of principles and procedures by which the journal should operate. The Editorial Board has unanimously decided to resign.

We do want to assure the authors who have papers under review that we see it as our responsibility to bring these documents to resolution during our term. We will continue to process new submissions until the end of the year. More about the future at the end of this note.

While the issue at the heart of our disagreement with Wiley is about academic independence, it should be noted that there is a structural issue here. Scholarly publishing is broadly in flux at the moment in the search for a sustainable model. Currently, academics are not paid to create and review articles. In fact, they often have to pay fees to publish, and, readers have to pay to access the work through a pay per view system, or, through subscriptions.

Plan S, an international consortium makes the dilemma clear:

“Monetising the access to new and existing research results is profoundly at odds with the ethos of science (Merton, 1973 )… In the 21st century, science publishers should provide a service to help researchers disseminate their results. They may be paid fair value for the services they are providing, but no science should be locked behind paywalls!”

While this moment calls for creativity and collaboration with the scholarly community to find new models, Wiley is intent on making broad changes to the way that the Journal of Field Robotics is operated, guided mostly by an economic calculation to increase revenue and decrease costs. To do this, they have unilaterally decided to change the terms of the contract that has been constant since the JFR was started in 2005. Wiley has confronted a similar case (European Law Journal) with similar effect – the entire editorial board has resigned in January of 2020:

Wiley insists that the new contract is covered under a confidentiality agreement that not even the Editorial Board can examine. What we can say is that the net effect of Wiley’s demands would make the Editors contractors to the publisher rather than having them respond to the board. We see this as a breach of academic autonomy.

In resigning, the Editorial Board of the Journal of Field Robotics reaffirms its commitment to dissemination and discussion of research. In the near future we will announce a new forum for research in Field Robotics that will maintain the academic integrity of our editorial process while also ensuring open dissemination of your research.

The Editorial Board of the Journal of Field Robotics

• Simon Lacroix, LAAS
• David Wettergreen, CMU
• Cédric Pradalier, GeorgiaTech Lorraine
• Tim Barfoot, University of Toronto
• Roland Siegwart, ETH
• Giuseppe Loianno, NYU
• Henrik I Christensen, UC San Diego
• Marco Hutter, ETH
• Kazuya Yoshida, Tohoku University
• Aarne Halme, Aalto University
• Hanumant Singh, Northeastern University
• Matthew Berkemeier, Continental
• Anibal Ollero, University of Seville
• Jonathan Roberts, QUT
• Hajime Asama, University of Tokyo
• Satoshi Tadokoro, Tohoku University
• Raja Chatila, Sorbonne University
• Peter Corke, QUT
• Matt Spenko, Illinois Institute of Technology
• Larry Matthies, NASA JPL
• Salah Sukkarieh, University of Sydney
• Stefan Williams, University of Sydney
• Sanjiv Singh, CMU

Update: The response to Tertill’s crowdfunding campaign has amazed and delighted us! Pledges totalling over $250,000 have come from 1000+ backers. We’re shipping to all countries, with over a fifth of Tertill’s supporters coming from outside the United States. But the end is near; Tuesday (11 July) is the last full day of the campaign. After that Tertill’s discounted campaign price will no longer be available and delivery in time for next year’s (northern hemisphere) growing season cannot be assured.

Franklin Robotics has launched a Kickstarter campaign for Tertill, their solar-powered, garden-weeding robot.

Tertill lives in your garden, collecting sunlight to power its weed patrol, and cutting down short plants with a string trimmer/weed whacker with almost no intervention required. Available for about $300USD, the fully autonomous Tertill is the first weeding robot available to home gardeners.

Tertill is round, short, has four wheel drive and extreme camber wheels. It uses proprietary algorithms to ensure that it finds as many weeds as it can, using its sensors to distinguish between weeds and crops based on height. As Tertill sees it: small plants are weeds, big plants are crops. But what if you have seedlings? Simply place a plant collar around seedlings to inform Tertill that this plant is wanted.

Tertill doesn’t use a camera for weed discrimination because Franklin wants to put Tertill to work in your garden now. In the not-too-distant future, inexpensive vision systems may reliably distinguish between plants and crops even when both are seedlings. But, according to Franklin, such system are not yet ready.

Tertill is designed such that one robot can easily keep up with the weeds in a typical sized garden (that’s about 100 square feet in the USA). Tertill will usually handle gardens considerably larger than this, but how much larger depends on weather and local weed types. And Terrill’s can work in teams, coexisting harmoniously in the same space. Multiple Tertill robots will simply avoid each other.

Check out the video below:

But Tertill needs a small barrier to keep it from wandering off beyond the borders of your garden. A garden fence, the wooden border of a raised bed, or any sort of edging that is two or more inches tall will work.

Otherwise, Tertill is designed to live in your garden and require very little attention. Summer showers won’t bother it. And, under normal conditions, you won’t have to worry about charging Tertill—the sun will see to that. But, if Tertill has been in the dark for so long (months) that its battery is completely exhausted, a USB connection can be used to charge the battery.

Franklin believes that small, simple robots can help solve big, complex problems. Franklin’s robots aim to help gardeners by reducing the tedium and physical challenge of weeding, making gardening more fun. The data their robots collect helps gardeners garden better, improving yield and quality. Their robots aim to benefit the environment by eliminating the need for herbicides and returning organic matter to the soil.

Click here to visit Franklin’s website and learn more.

A new robot under development can send information on the stiffness, look and feel of a patient to a doctor located kilometres away. Image credit: Accrea

A robotic doctor that can be controlled hundreds of kilometres away by a human counterpart is gearing up for action. Getting a check-up from a robot may sound like something from a sci-fi film, but scientists are closing in on this real-life scenario and have already tested a prototype.

‘The robot at the remote site has different force, humidity and temperature sensors, all capturing information that a doctor would get when they are directly palpating (physically examining) a patient,’ explains Professor Angelika Peer, a robotics researcher at the University of the West of England, UK.

Prof. Peer is also the project coordinator of the EU-funded ReMeDi project, which is developing the robotic doctor to allow medical professionals to examine patients over huge distances.

Through a specially designed surface mounted on a robotic arm, stiffness data of the patient’s abdomen is displayed to the human, allowing the doctor to feel what the remote robot feels. This is made possible thanks to a tool called a haptic device, which has a soft surface reminiscent of skin that can recreate the sense of touch through force and changing its shape.

During the examination, the doctor sits at a desk facing three screens, one showing the doctor’s hand on the faraway patient and a second for teleconferencing with the patient, which will remain an essential part of the exchange.

The third screen displays a special capability of the robot doctor – ultrasonography. This is a medical technique that sends sound pulses into a patient’s body to create a window into the patient. It reveals areas of different densities in the body and is often used to examine pregnant women.

Ultrasonography is also important for flagging injuries or disease in organs such as the heart, liver, kidneys or spleen and can find indications for some types of cancer, too.

‘The system allows a doctor from a remote location to do a first assessment of a patient and make a decision about what should be done, whether to transfer them to hospital or undergo certain treatments,’ said Prof. Peer.

The robot currently resides in a hospital in Poland but scientists have shown the prototype at medical conferences around the world. And they have already been approached by doctors from Australia and Canada where it can take several hours to transfer rural patients to a doctor’s office or hospital.

With the help of a robot, a doctor can talk to a patient, manoeuvre robotic arms, feel what the robot senses and get ultrasounds. Image credit: ReMeDi

‘This is to support an initial diagnosis. The human is still in the loop, but this allows them to perform an examination remotely,’ said Prof. Peer.

Telemedicine

The ReMeDi project could speed up a medical exam and save time for patients and clinics. Another EU-funded project – United4Health (U4H) – looks at a different technology that could be used to remotely diagnose or treat people.

‘We need to transform how we deliver health and care,’ said Professor George Crooks, director of the Scottish Centre for Telehealth & Telecare, UK, which provides services via telephone, web and digital television and coordinates U4H.

This approach is crucial as Europe faces an ageing population and a rise in long-term health conditions like diabetes and heart disease. Telemedicine empowers these types of patients to take steps to help themselves at home, while staying in touch with medical experts via technology. Previous studies showed those with heart failure can be successfully treated this way.

These patients were given equipment to monitor their vital signs and send data back to a hospital. A trial in the UK comparing this self-care group to the standard-care group showed a reduction in mortality, hospital admissions and bed days, says Prof. Crooks.

A similar result was shown in the demonstration sites of the U4H project which tested the telemedicine approach in 14 regions for patients with heart failure, diabetes and chronic obstructive pulmonary disease (COPD). For diabetic patients in Scotland, they kept in touch with the hospital using text messages. For COPD, some patients used video consultations.

Prof. Crooks stresses that it is not all about the electronics – what matters is the service wraparound that makes the technology acceptable and easy to use for patients and clinical teams.

‘It can take two or three hours out of your day to go along to a 15 minute medical appointment and then to be told to keep taking your medication. What we do is, by using technology, patients monitor their own parameters, such as blood sugar in the case of diabetes, how they are feeling, diet and so on, and then they upload these results,’ said Prof. Crooks.

‘It doesn’t mean you never go to see a doctor, but whereas you might have gone seven or eight times a year, you may go just once or twice.’

Crucially, previous research has shown these patients fare better and the approach is safe.

‘There can be an economic benefit, but really this is about saving capacity. It frees up healthcare professionals to see the more complex cases,’ said Prof. Crooks.

It also empowers patients to take more responsibility for their health and results in fewer unplanned visits to the emergency room.

‘Patient satisfaction rates were well over 90 %,’ said Prof. Crooks.

A record number of teams submitted beautiful robot-created artwork for the second year of this 5-year worldwide competition. In total, there were 38 teams from 10 countries who submitted 200 different artworks!

Winners were determined based on a combination of public voting (over 3000 people with a Facebook account), judges consisting of working artists, critics, and technologists, and by how well the team met the spirit of the competition—that is, to create something beautiful using a physical brush and robotics, and to share what they learned with others.  Learn more about the goals of the contest and its rules here.

Teams are encouraged to hang on to their artwork as there will be a physical exhibition of robotic-created artwork following next year’s competition (Summer 2018).  This exhibition, most likely in Seattle, WA, will showcase winners of the 2017 and 2018 competition.  The goal is to test Andy Warhol’s theory that  “You know it’s ART, when the check clears.”

… and now, for the team winners of the 2017 Robot Art Competition:

1st Place—$40,000—PIX18 / Creative Machines Lab—Columbia University (USA)

House, by PIX18 & Creative Machines Lab

This project from Columbia shows a high level of skill with brushstrokes. This, along with some deep learning algorithms, produces some lovely paintings from sources or scratch. When they used a photograph as the source they were able to create plenty of variation from the original and using a fluid medium to produce an atmospheric and open-ended visual experience.  Much of their work had a painterly and contemporary presentation.

2nd Place—$25,000—CMIT ReART, Kasetsart University (Thailand)

King Bhumibol Adulyadej, by CMIT ReArt

Artists program this robot brushstroke by stroke, using a haptic recording system that generates volumes of data about the position of the brush and the forces being exerted. When re-played, reART will generate a perfect reproduction of the original strokes. Haptic recording and playback allows for remarkably high-quality inkbrush drawings

One of the aspects of a success commercial artist is to know their market.  In this case, the students chose to paint the popular and recently deceased Thai King and therefore were able to get many students to vote for their team.

3rd Place—$10,000—CloudPainter—Gonzaga (USA)

Hunter 2017 (Deep Learning Abstract Portraiture), by CloudPainter

All of this teams offerings are important. They are aiming at an interpretation of the optical properties of oil paint and applying them to deep learning. Spontaneous paint, “mosaicing” of adjacent tones, layering effects and the graphical interplay between paint strokes of varying textures, are all hand/eye, deeply neurally sophisticated aspects of oil painting that this team is trying to evince together with a robot.

This team also won the $5,000 prize for technical contribution.

4th Place $6000—e-David—University of Konstanz (Germany)

Homage to Jackson Pollock, by e-David

Using software that enables a collaboration between a human artist and roboticist, e-David mimics closely the approach a human painter would work on the canvas. An accompanying academic paper goes into deep detail about their approach to the project.

5th Place $4000—JACKbDU—New York University Shanghai (China)

WWF, by JACKbDU

Clean lines, interesting abstractions, both familiar and abstract subjects made me appreciate the overall body.  In particularly, purely from aesthetics, these were found to be most compelling.

6th Place $2000—HEARTalion—Halmstad University (Sweden)

MISERABLE, by HEARTalion

If this body of work was exhibited at a gallery and I was told that the artist aimed to capture emotion through color, composition, and textures—I would buy (says one of our professional judges). The bold brush strokes, cool or warm templates to match the emotional quality expressed, all made sense—but felt alive. Loved them.

7th Place $2000—Late Night Projects—Independent

Koi: Layered Painting, by Late Night Projects

Loved the composition of both of these pieces.  Both pieces bring an emotive calm with just a few colors, technique, and simplicity.  Gem-style optical sketch, intimate scale, good formal balance, medium application and value panels.

8th Place $2000—Wentworth Institute of Technology (USA)

Profile 03, by Wentworth Institute of Technology

This project uses the precision of a robot to take crude brush dabs to astonishing levels. By incorporating 3d scans into its image generation, this bot operates with a complete understanding of its subject; much like a human painter would have someone sitting for them. The team was kind enough to publish their 3d models and source code, so others can learn from and build off of their work.

9th Place $2000—CARP—Worcester Polytechnic Institute (USA)

Geometry No1, by CARP

Very good line. Lots of space in this drawing and central form is simultaneously dense and traversable.  Good architectural reference in clear tooling, but enlivened by sumo type or Franz Kline strokes. If the shapes can continue to be explored while maintaining the hand/machine balance, this will remain a strong venue.

Slightly synesthetic. Beautiful and balanced fusion of technical and handmade, crystalline and organic.

10th Place $2000—BABOT—Massachusetts Institute of Technology (USA)

People in Space, by BABOT

MIT’s BABOT was able to produce this and other inspiring vistas.

Special recognition prizes $1000/each

Chicago Engineering Design Team – University of Illinois at Chicago (USA)

JacksonBot – University of Heidelberg (Germany)

Christian H. Seidler – The Jeffersonian Institute (USA)

Anguis – Oregon State University (USA)

Manibus Team – Ballet Des Moines (USA)

Stacy Nagata, Peter Malarkey, Jonathan Zornow.

Click here to see more works of RobotArt, and to learn more about the competition.

Robo Done, the robotic academy franchise for kids from Osaka, Japan, celebrated Japan’s Day of the Children on the 5th of May at their annual event, Robot Festival 2017 or RoboFes. The event welcomed over 1,000 attendees, including children and their parents.

This was the 2nd time Robo Done has celebrated the festival. In only one year, the number of attendees has increased threefold (350 attendees in 2016 to over 1,012 in 2017). It was celebrated in the KANDAI MeRise Campus of the Kansai University in Osaka, Japan and has become the biggest event at the campus.

The main activity was the Robot Contest, using LEGO Mindstorm, with morning and afternoon leagues. Over 200 children — from 6 years and up — participated in the championship. The kids built robots in pairs and programmed their creations, repeating the process of trial-and-error against a time limit. Several IT and robot related companies had booths, as well as, students of the university, which offered a variety of activities for the kids to enjoy.

Robo Done will hold RoboFes again in 2018, hoping to inspire even more kids to enjoy robotics and programming. We hope RoboFes will become a regular event during Japan’s “Golden Week!”

D’Arpino Perez discusses her work teaching Optimus to pick up a bottle. Image: Jason Dorfman, MIT CSAIL

Most robots are programmed using one of two methods: learning from demonstration, in which they watch a task being done and then replicate it, or via motion-planning techniques like optimization or sampling, which require a programmer to explicitly specify a task’s goals and constraints.

Both methods have drawbacks. Robots that learn from demonstration can’t easily transfer one skill they’ve learned to another situation and remain accurate. On the other hand, motion planning systems that use sampling or optimization can adapt to these changes, but are time-consuming, since they usually have to be hand-coded by expert programmers.

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have recently developed a system that aims to bridge the two techniques: C-LEARN, which allows non-coders to teach robots a wider range of tasks simply by providing some information about how objects are typically manipulated and then showing the robot a single demo of the task. Importantly, this enables users to teach robots skills that can be automatically transferred to other robots with different “kinematics” (ways of moving) – a key time- and cost-saving measure for companies that want a range of robots to perform similar actions.

“By combining the intuitiveness of learning from demonstration with the precision of motion-planning algorithms, this approach can help robots do new types of tasks that they haven’t been able to learn before, like multi-step assembly using both of their arms,” says Claudia Pérez-D’Arpino, a PhD student who wrote a paper on C-LEARN with MIT professor Julie Shah.

The team tested the system on Optimus, a new two-armed robot designed for bomb disposal that they programmed to perform tasks like opening doors, transporting objects and extracting objects from containers.

(The robot is made of components from multiple manufacturers, including a manipulation system by RE2, a Husky unmanned ground vehicle by Clearpath, 3-finger grippers by Robotiq and the multisense sensor from Carnegie Robotics that is used in the Atlas robot.)

In simulations they showed that Optimus’ learned skills could be seamlessly transferred to Atlas, CSAIL’s six-foot-tall, 400-pound humanoid robot.

The paper was recently accepted to the IEEE International Conference on Robotics and Automation (ICRA), which takes place May 29-June 3 in Singapore.

How it works

With C-LEARN the user first gives the robot a knowledge base of information on how to reach and grasp different objects that have different constraints. (The C in C-LEARN stands for “constraints.”) The operator then uses a 3D interface to show the robot a single demonstration of the specific task, which is represented by a sequence of relevant moments known as “keyframes.” By matching these keyframes with the knowledge base, the robot can automatically suggest motion plans for the operator to approve or edit as needed.

“This approach is actually very similar to how humans learn in terms of seeing how something’s done and connecting it to what we already know about the world,” says Pérez-D’Arpino. “We can’t magically learn from a single demonstration, so we take new information and match it to previous knowledge about our environment.”

One challenge was that existing constraints that could be learned from demonstrations weren’t accurate enough to enable robots to precisely manipulate objects. To overcome that, the researchers developed constraints inspired by computer-aided design (CAD) programs that can tell the robot if its hands should be parallel or perpendicular to the objects it is interacting with.

The team also showed that the robot performed significantly better in collaboration with humans. While the robot successfully executed tasks 87.5 percent of the time on its own, it did so 100 percent of the time when it had a operator that could correct minor sensory inaccuracies.

“Having a knowledge base is fairly common, but what’s not common is integrating it with learning from demonstration,” says Dmitry Berenson, an assistant professor of computer science at the University of Michigan who was not involved in the research. “That’s very helpful, because if you are dealing with the same objects over and over again, you don’t want to then have to start from scratch to teach the robot every new task.”

The system is part of a larger wave of research focused on making LfD approaches more adaptive. If you’re a robot that has learned to take an object out of a tube from a demonstration, you might not be able to do it if there’s an obstacle in the way that requires you to move your arm differently. However, C-LEARN can do this, because it does not learn one specific way to perform the action.

“It’s good for the field that we’re moving away from directly imitating motion, towards actually trying to infer the principles behind the motion,” says Berenson. “By using these learned constraints in a motion planner, we can make systems that are far more flexible than those which just try to mimic what’s being demonstrated”

Shah says that advanced LfD methods could prove important in time-sensitive scenarios like bomb disposal and disaster response, where robots are currently tele-operated at the level of individual joint movements.

“Something as simple as picking up a box could take 20-30 minutes, which is significant for an emergency situation,” says Pérez-D’Arpino.

C-LEARN can’t yet handle certain advanced tasks, like avoiding collisions or planning for different step sequences for a given task. But the team is hopeful that incorporating more insights from human learning will give robots an even wider range of physical capabilities.

“Traditional programming of robots in real-world scenarios is difficult, tedious and requires a lot of domain knowledge,” says Shah. “It would be much more effective if we could train them more like how we train people: by giving them some basic knowledge and a single demonstration. This is an exciting step towards teaching robots to perform complex multi-arm and multi-step tasks necessary for assembly manufacturing and ship or aircraft maintenance.”

Click here to access the full paper.

We are happy to announce that Qualifier 3 is now open for the Agile Robotics for Industrial Automation Competition (ARIAC)! ARIAC is a simulation-based competition is designed to promote agility in industrial robot systems by utilizing the latest advances in artificial intelligence and robot planning. The goal is to enable industrial robots on the shop floors to be more productive, more autonomous, and to require less time from shop floor workers.

You can learn more about the competition here. The top performing teams will be invited to present at a workshop held during IROS 2017 in Vancouver. So far we have completed Qualifiers 1 and 2, highlights of which you can see here:

and here:

While the first two Qualifiers are now closed, there’s still time to join the competition. You have until May 15, 2017 to submit your results from Qualifier 3 and secure a spot in the final competition, to be held in early June. To learn how to participate, visit the ARIAC site.

Image: Oliver Deussen and Thomas Lindemeier, University of Konstanz

The 2017 second annual robotic art competition with $100,000 in cash prizes is now open for team registration. An international competition for all ages, the contest’s goal is to challenge teams to produce something visually beautiful with robotics—that is, to have a robot use physical brushes and paint to create an artwork. It’s ideal for students or professionals involved in robotic planning and image processing, especially those who have an appreciation for art.

There is no cost to enter and any robotic system can be used to produce the artwork. While most teams are using existing robotic arms, the contest is open to custom hardware. Teams can enter up to six paintings in each of the competition categories of “original artwork” and “reinterpreted artwork” where a reference image or existing artwork is used as a reference.

We will again have a combination of professional art critics, tech celebrities, and the general public score the artwork. You can also signup now to be notified when it’s time to come see the artwork submissions and vote.

Image: National Taiwan University

In last year’s competition, the expectations were modest—get as many teams as possible to setup a robot that can do any sort of painting. We believe that we succeeded. In total, there were 16 teams from seven countries who submitted over 70 different artworks!

Winners were determined based on a combination of public voting (over 2200 people with a Facebook account voted), five judges consisting of working artists, critics, and technologists, and by how well the team met the spirit of the competition—in this case, to create something beautiful using a physical brush and robotics and to share what they learned with others.

Details, team signup, and last year’s winners are at robotart.org.

ABB, one of the Big Four global robot makers, has acquired Austrian industrial automation integrator and components manufacturer B&R (Bernecker & Rainer) Automation for an undisclosed amount but estimated to be “close to $2 billion.” The purchase is being funded from ABB’s own cash and is expected to close by mid-year.

ABB has an installed base of over 300,000 industrial robots in 53 countries around the world. At this week’s Automate/ProMat trade show in Chicago, ABB is presenting their whole line of industrial and collaborative robots including their new IRB 1660ID welding robot and YuMi collaborative robot.

B&R Automation has a complete line of control systems including their new X90 for mobile equipment and outdoor applications like agriculture, forestry, construction and municipal vehicles.

By combining B&R with ABB:

• B&R said the acquisition will enable it to provide comprehensive industrial automation solutions globally and across the entire spectrum of technology and software in measurement, control, actuation, robotics, electrification and digitalization.
• ABB said the acquisition would increase its sales in industrial automation to around $15 billion by adding B&R’s annual sales of more than $600 million. According to Reuters, it would also “consolidate ABB’s No.2 position in the $130 billion processing and industrial sector behind Siemens but ahead of rivals such as Emerson, Rockwell Automation and General Electric.

Investor AB, a 10% owner of ABB, spokesman Stefan Stern said:

As we see it, this is a very important, strategically sound acquisition which clearly strengthens ABB’s position in factory automation. It is important for ABB to continue to work with internal efficiency and at the same time invest for the future within strategic areas.

A new book by Lentin Joseph, ROS Robotics Programming, outlines more than 14 robotics projects using ROS that can be engaged with without requiring a lot of hardware. The book starts with an introduction to ROS and its installation procedure. After discussing the basics, you’ll be taken through great projects such as building a self-driving car, an autonomous mobile robot, and image recognition using deep learning and ROS. You can find ROS robotic applications for beginner, intermediate, and expert levels inside.

This book is unique in that it focuses on ROS via the lens of robotics projects only. Lentin is the CEO/Founder of Qbotics Labs and the author of Learning Robotics using Python and Mastering ROS for Robotics Programming.

Below is an overview of the chapters in the book:

Chapter 1, Getting Started with ROS Robotics Application Development, is for absolute beginners to ROS. No need to worry if you don’t have experience in ROS; this chapter will help you give you an idea of the ROS software framework and its concepts.

Chapter 2, Face Detection and Tracking Using ROS, OpenCV and Dynamixel Servos, takes you through a cool project that you can do with ROS and the OpenCV library. This project basically creates a face tracker application in which your face will be tracked in such a way that the camera will always train on your face. We will use intelligent servos such as Dynamixel to rotate the robot on its axis.

Chapter 3, Building a Siri-Like Chatbot in ROS, is for those of you who want to make your robot interactive and intelligent without much hassle. This project creates a chatterbot in ROS that you can communicate with using text or speech. This project will be useful if you’re going to create social or service robots.

Chapter 4, Controlling Embedded Boards Using ROS, helps you build a robot using Arduino, an embedded compatible board, Raspberry Pi, or Odroid and an interface to ROS. In this chapter, you will see a wide variety of embedded boards and the interfacing projects made with them.

Chapter 5, Teleoperate a Robot Using Hand Gestures, will teach you how to build a gesture control device using Arduino and IMU. The gestures are translated into motion commands by ROS nodes.

Chapter 6, Object Detection and Recognition, has an interesting project for detecting objects. You will learn both 2D and 3D object recognition using powerful ROS packages.

Chapter 7, Deep Learning Using ROS and TensorFlow, is a project made using a trending technology in robotics. Using the TensorFlow library and ROS, we can implement interesting deep-learning applications. You can implement image recognition using deep learning, and an application using SVM can be found in this chapter.

Chapter 8, ROS on MATLAB and Android, is intended for building robot applications using ROS, Matlab, and Android.

Chapter 9, Building an Autonomous Mobile Robot, is about creating an autonomous mobile robot with the help of ROS. You can see how to use packages such as navigation, gmapping, and AMCL to make a mobile robot autonomous.

Chapter 10, Creating a Self-driving Car Using ROS, is one of the more interesting projects in this book. In this chapter, we will build a simulation of the self-driving car using ROS and Gazebo.

Chapter 11, Teleoperating Robot Using VR Headset and Leap Motion, shows you how to control a robot’s actions using a VR headset and Leap Motion sensor. You can play around with virtual reality (a trending technology these days).

Chapter 12, Controlling Your Robots over the Web, we will see how to build interactive web applications using rosbridge in ROS.

The book uses ROS Kinetic and Indigo installed on latest Ubuntu L.T.S 16.04.01. The codes are also compatible with ROS Jade.

A basic knowledge in R.O.S, GNU/Linux, and C++ programming concepts is recommended.

The book is published by PACKT and can be purchased at Amazon Global and PACKT Publishing.

This week (April 05-07, 2017), the Sands Expo Center in Las Vegas will host ISC West 2017 in what they say is “the largest security industry trade show in the U.S.”, drawing an estimated 29,000+ security professionals. The show will feature ISC West’s first-ever Unmanned Security Expo focused on Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs) and various systems to support robotic technology. The Expo will include a fully functional flight cage and robotics demonstration area, booth exhibits and free education sessions.

Robolliance—a forum for technology partners and industry experts in robotics, surveillance and security, created to advance the understanding and awareness of the Autonomous Robotics marketplace—is one of the four primary supporters of the Unmanned Security Expo. Robolliance sponsors—comprised of technology companies, security integrators and industry thought leaders—will be on hand April 5th-7th to welcome attendee inquiries and address the greater questions of “Why Robotics?  Why Security? Why Now?”.

Robolliance welcomes anyone curious about robotics for security to visit Booths 3135 and 4136. Unmanned Security Expo Booth 3135 will feature all the Sponsors of Robolliance, who have come together to share their resources and shine a light on the importance of unmanned systems to augment current security technology and human guarding. Directly across from the Robolliance Sponsor Showcase, in Booth 4136, will be the Expert Corner. Experts will be available all three days to talk one-on-one with attendees and address their questions. Robolliance experts will also be participating in Security Industry Association (“SIA”) education panels that some include an attending charge. Topics and session times are:

• Robotics: Upcoming Legislation and Government Regulations You Need to Know Now: April 4th, 2017 – 1:30-2:30PM
• Protecting Your Facility to the Edge Using Autonomous Robotics: April 5th, 2017 – 11:15AM-12:15PM
• Drones & Droids 2020: The Future of Unmanned Robotics: April 6th, 2017 – 3:45-4:30PM

Robolliance expects to have 10-15 experts there throughout the three-day event. The slate of experts has been curated by founding Robolliance Sponsor, Sharp Electronics Corporation, who will also be on hand in the Sponsor Showcase as well as in their Booth #3137 that features the Sharp INTELLOS Automated Unmanned Ground Vehicle (A-UGV).

• Todd Kleperis – CEO of Hardcar Security

“Hardcar was pleasantly surprised to find Robolliance in the unmanned industry. The entire industry is growing rapidly and we are thrilled to be part of the team.  What you get when you add experts in a field together is a powerhouse ability to radically change markets. Robolliance is built to do just that.”

• Chris Peckham – SVP, CTO & Special Projects at Kratos Public Safety & Security Solutions

“As robotic platforms continue to develop and improve, we anticipate that they will be leveraged in a variety of new and exciting applications. The development of autonomous and semi-autonomous vehicles will bring greater opportunity for both unmanned aerial and ground vehicles to be used as a larger part of a complex surveillance and security system. Robolliance allows interested parties to connect to experts in the field and increase awareness of the technology and its use. The new Unmanned Security Expo @ ISCWest 2017 will bring these new systems together in one location where attendees can view and interact with many in this growing field.”

• Brian Higgins, CPP, CSSP – President of Group 77 Security Consulting

“The Unmanned Security Expo, held @ ISC West 2017, will be the first significant step in the adoption of Unmanned Vehicles as a standard business practice in the security and public safety sectors. It is exactly this kind of “meeting-of-the-minds” event that Robolliance encourages. I expect more ideas on how best to use unmanned vehicles and robotics because of this event.”

• Mark Brodie – Director of MiB Mediaworks

“Through the development and design of UAV’s and UGV’s, dramatic changes have encouraged film makers to find new ways of story-telling, and have allowed us to capture images never before possible. That ability to capture images transcends not only the work I do, but the need for improved visual surveillance in security. This conference is the perfect incubator for sharing ideas between our industries. As a Robolliance sponsor, I am grateful to be participating as a panelist and an expert in my field.”

• John Nagy – Co-President of NagyHeller Solutions, Inc.

“The Unmanned Security Expo provides a venue for us to share our expertise in security operations and the associated financial aspects. Robotics will alter the landscape of the industry, and I am proud to participate in conversations that articulate the momentous opportunity robotics brings to the security industry from both an efficacy and cost efficiency perspective.”

• BG William J. Marshall III, USA (Retired) – AVP for Govt & Military Relations at New Jersey Institute of Technology (NJIT)

“When we launched Robolliance a year ago at PSA TEC 2016, I said the early adopters of this advanced autonomous technology (namely integrators) would reap the benefit of getting in before robotics for security skyrocketed. Now the market for security robots is here as demonstrated by the Unmanned Security Expo. It’s time to get onboard or look in your rearview mirror at the missed opportunity.”

To foster awareness and understanding about UGVs for security, visit Robolliance.com.

What do you get when you put together wood and rope? Well according to Plymouth University’s Professor Guido Bugmann: a low-cost, open source, 2 meter tall robot! All buildable for under £2000. The Cheap Arm Project (CHAP) began as an MSc project aimed at developing an affordable mobile robot arm system that could be used by wheelchair users to access daily objects at inaccessible heights or weights (the extreme case being 2 litre bottle).

Now the project has turned open source in the hope of continuing development and gathering more feedback. With the entire hardware and software details available online, with detailed instructions, the team hopes that community members will attempt to construct their own CHAP robot.

While building an entire robot may conventionally seem a complex and expensive undertaking, CHAP stands out with its use of 3D printed parts and wood. In fact, the “under £2000” price tag of required parts is primarily made up of the use six Dynamixel motors, each costing up to £200 and used by the project due to having numerous available during development. With this in mind, future work will look at the use of lower cost motors.

Rather than a robot ‘brain’, CHAP is powered by a human one. To accomplish this, CHAP’s Raspberry Pi was used to host a local web page to enable tele-operation from any connected device, all accessible through a WiFi hotspot also hosted by the Pi. This was an important design choice as it removed the need for platform specific apps and associated costs, simultaneously simplifying deployment and testing.

This was done deliberately to keep the design simple, and to allow users the freedom to interact with their world. Early user-tests discovered that some level of intelligence would be needed to simplify controls. With currently three joints, and three axis available; some steps are needed to automate object interaction (pull requests welcome!).

The next step for CHAP will be refinement of hardware and software; however the very first will be building a community! With full design documents available it is hoped that with support these can be improved and simplified, while simultaneously improving the effectiveness of the software.

Link to the github site can be found here: https://mobile-chap.github.io/Web/

The National Science Foundation (NSF) announced a $6.1 million, five-year award to accelerate fundamental research on wireless communication and networking technologies through the foundation’s Platforms for Advanced Wireless Research (PAWR) program.

Through the PAWR Project Office (PPO), award recipients US Ignite, Inc. and Northeastern University will collaborate with NSF and industry partners to establish and oversee multiple city-scale testing platforms across the United States. The PPO will manage nearly $100 million in public and private investments over the next seven years.

“NSF is pleased to have the combined expertise from US Ignite, Inc. and Northeastern University leading the project office for our PAWR program,” said Jim Kurose, NSF assistant director for Computer and Information Science and Engineering. “The planned research platforms will provide an unprecedented opportunity to enable research in faster, smarter, more responsive, and more robust wireless communication, and move experimental research beyond the lab — with profound implications for science and society.”

Over the last decade, the use of wireless, internet-connected devices in the United States has nearly doubled. As the momentum of this exponential growth continues, the need for increased capacity to accommodate the corresponding internet traffic also grows. This surge in devices, including smartphones, connected tablets and wearable technology, places an unprecedented burden on conventional 4G LTE and public Wi-Fi networks, which may not be able to keep pace with the growing demand.

NSF established the PAWR program to foster use-inspired, fundamental research and development that will move beyond current 4G LTE and Wi-Fi capabilities and enable future advanced wireless networks. Through experimental research platforms that are at the scale of small cities and communities and designed by the U.S. academic and industry wireless research community, PAWR will explore robust new wireless devices, communication techniques, networks, systems and services that will revolutionize the nation’s wireless systems. These platforms aim to support fundamental research that will enhance broadband connectivity and sustain U.S. leadership and economic competitiveness in the telecommunications sector for many years to come.

“Leading the PAWR Project Office is a key component of US Ignite’s mission to help build the networking foundation for smart communities,” said William Wallace, executive director of US Ignite, Inc., a public-private partnership that aims to support ultra-high-speed, next-generation applications for public benefit. “This effort will help develop the advanced wireless networks needed to enable smart and connected communities to transform city services.”

Establishing the PPO with this initial award is the first step in launching a long-term, public-private partnership to support PAWR. Over the next seven years, PAWR will take shape through two multi-stage phases:

• Design and Development. The PPO will assume responsibility for soliciting and vetting proposals to identify the platforms for advanced wireless research and work closely with sub-awardee organizations to plan the design, development, deployment and initial operations of each platform.
• Deployment and Initial Operations. The PPO will establish and manage each platform and document best practices as it progresses through the lifecycle.

“We are delighted that our team of wireless networking researchers has been selected to take the lead of the PAWR Project Office in partnership with US Ignite, Inc.,” said Dr. Nadine Aubry, dean of the college of engineering and university distinguished professor at Northeastern University. “I believe that PAWR, by bringing together academia, industry, government and communities, has the potential to make a transformative impact through advances spanning fundamental research and field platforms in actual cities.”

The PPO will work closely with NSF, industry partners and the wireless research community in all aspects of PAWR planning, implementation and management. Over the next seven years, NSF anticipates investing $50 million in PAWR, combined with approximately $50 million in cash and in-kind contributions from over 25 companies and industry associations. The PPO will disperse these investments to support the selected platforms.

Additional information can be found on the PPO webpage.

This announcement will also be highlighted this week during the panel discussion, “Wireless Network Innovation: Smart City Foundation,” at the South by Southwest conference in Austin, Texas.

The feedback system enables human operators to correct the robot’s choice in real-time – Jason Dorfman, MIT CSAIL

For robots to do what we want, they need to understand us. Too often, this means having to meet them halfway: teaching them the intricacies of human language, for example, or giving them explicit commands for very specific tasks. But what if we could develop robots that were a more natural extension of us and that could actually do whatever we are thinking?

A team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Boston University is working on this problem, creating a feedback system that lets people correct robot mistakes instantly with nothing more than their brains.

Using data from an electroencephalography (EEG) monitor that records brain activity, the system can detect if a person notices an error as a robot performs an object-sorting task. The team’s novel machine-learning algorithms enable the system to classify brain waves in the space of 10 to 30 milliseconds.

While the system currently handles relatively simple binary-choice activities, the paper’s senior author says that the work suggests that we could one day control robots in much more intuitive ways.

“Imagine being able to instantaneously tell a robot to do a certain action, without needing to type a command, push a button or even say a word,” says CSAIL director Daniela Rus. “A streamlined approach like that would improve our abilities to supervise factory robots, driverless cars and other technologies we haven’t even invented yet.”

In the current study the team used a humanoid robot named “Baxter” from Rethink Robotics, the company led by former CSAIL director and iRobot co-founder Rodney Brooks.

The paper presenting the work was written by BU PhD candidate Andres F. Salazar-Gomez, CSAIL PhD candidate Joseph DelPreto, and CSAIL research scientist Stephanie Gil under the supervision of Rus and BU professor Frank H. Guenther.  The paper was recently accepted to the IEEE International Conference on Robotics and Automation (ICRA) taking place in Singapore this May.

Past work in EEG-controlled robotics has required training humans to “think” in a prescribed way that computers can recognize. For example, an operator might have to look at one of two bright light displays, each of which corresponds to a different task for the robot to execute.

The downside to this method is that the training process and the act of modulating one’s thoughts can be taxing, particularly for people who supervise tasks in navigation or construction that require intense concentration.

Rus’ team wanted to make the experience more natural. To do that, they focused on brain signals called “error-related potentials” (ErrPs), which are generated whenever our brains notice a mistake. As the robot indicates which choice it plans to make, the system uses ErrPs to determine if the human agrees with the decision.

The team uses EEG brain signals to detect if the person notices a mistake – Jason Dorfman, MIT CSAIL

“As you watch the robot, all you have to do is mentally agree or disagree with what it is doing,” says Rus. “You don’t have to train yourself to think in a certain way –  the machine adapts to you, and not the other way around.”

ErrP signals are extremely faint, which means that the system has to be fine-tuned enough to both classify the signal and incorporate it into the feedback loop for the human operator.

In addition to monitoring the initial ErrPs, the team also sought to detect “secondary errors” that occur when the system doesn’t notice the human’s original correction.

“If the robot’s not sure about its decision, it can trigger a human response to get a more accurate answer,” says Gil. “These signals can dramatically improve accuracy, creating a continuous dialogue between human and robot in communicating their choices.”

The feedback system enables human operators to correct the robot’s choice in real-time – Jason Dorfman, MIT CSAIL2

While the system cannot yet recognize secondary errors in real time, Gil expects the model to be able to improve to upwards of 90 percent accuracy once it can.

In addition, since ErrP signals have been shown to be proportional to how egregious the robot’s mistake is, the team believes that future systems could extend to more complex multiple-choice tasks.

Salazar-Gomez notes that the system could even be useful for people who can’t communicate verbally: a task like spelling could be accomplished via a series of several discrete binary choices, which he likens to an advanced form of the blinking that allowed stroke victim Jean-Dominique Bauby to write his memoir “The Diving Bell and the Butterfly.”

“This work brings us closer to developing effective tools for brain-controlled robots and prostheses,” says Wolfram Burgard, a professor of computer science at the University of Freiburg who was not involved in the research. “Given how difficult it can be to translate human language into a meaningful signal for robots, work in this area could have a truly profound impact on the future of human-robot collaboration.”

Read the paper here.

The project was funded in part by Boeing and the National Science Foundation.

If you enjoyed this article from CSAIL, you might also be interested in:

• MIT CSAIL’s 6-foot-tall NASA humanoid robot has landed
• Researchers add a splash of human intuition to planning algorithms
• Wearable AI that can detect the tone of a conversation
• Ingestible robots, glasses-free 3-D, and computers that explain themselves
• Design, simulate and build a custom drone

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Chances are that you’ve never given much thought to how insects walk, or what combination of leg movements–or gaits–is most stable or fastest, but, if like a group of scientists from Ramdya, Floreano and Ijspeert labs, NCCR Robotics, you are trying to create fast and robust robots, taking inspiration some of nature’s most agile movers might give you just the inspiration you need.

Multiple insect species walk fastest using a tripod gait, that means that three “feet” are on the ground and three are moving at any given time. Because insects find food and mates by walking around on leaves and climbing up trees, it has long been thought that evolutionary processes discovered the tripod gait to best achieve this kind of climbing over challenging terrain. In contrast, when moving quickly, quadruped vertebrates like dogs and horses tend to only keep one or two legs on the ground at a time. This difference in fast locomotor strategies between insects and vertebrates was puzzling to the researchers and suggested that the tripod gait might not be the best to use in ground-based bioinspired six-legged robots. To test this possibility, the team used computer simulations to model the running gaits of the fruit fly, Drosophila melanogaster. The team then used a Particle Swarm Optimization (PSO) algorithm on the in-silico insect model to discover optimally fast gaits in different environmental scenarios and configurations (e.g. climbing, walking on flat ground).

Indeed, they discovered that the tripod gait is best used for fast climbing while new, unexpected bipod gaits with two legs on the ground were faster during ground locomotion – the typical requirement of six-legged robots. To move beyond the simulation, the then tested these new bipod gaits in a hexapod robot. They found that it also moved faster than when using the tripod gait. Finally, the team discovered that when leg adhesion was blocked in real Drosophila melanogaster, insects used gaits more similar to those discovered in the simulation. These results reveal the importance of environmental and biomechanical context in designing locomotor control strategies. Bioinspired methods are finely tuned to the biological challenges they address and may not be appropriate for all robotics applications.

Why does this interest roboticists? In creating previous bioinspired robots, the group from NCCR Robotics have developed robots that are capable of accessing multiple types of environments to locate victims and better focus rescue efforts. One failing of legged robots thus far, however, is that they are not quick as their biological, or flying, counterparts. By discovering how walking robots can more efficiently move, they can make the robots more robust and also reduce the energy requirements for a mission.

Reference

P. Ramdya, R. Thandiackal, R. Cherney, T. Asselborn, R. Benton, A. Ijspeert, and D. Floreano. Climbing favors the tripod gait over alternative faster insect gaits. Nature Communications. DOI 10.1038/NCOMMS14494.

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See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

The Amazon is home to thousands of local indigenous communities spread across very remote areas. As a result, these sparsely populated communities rarely have reliable access to essential medicines and public health services. Local doctors in the region of Contamana report an average of 45 snakebites per month and no rapid access to antivenom, for example. We recently traveled to the rainforest to learn more about these challenges, and to explore whether cargo drones (UAVs) could realistically be used to overcome some of these problems in a sustainable manner.

We’re excited to share the results of our field tests in this new report (PDF); Spanish version here. For high-resolution photos of the field tests, please follow this link.

Our cargo drone flights were carried out in collaboration with the Peruvian Ministry of Health and local doctors. The field-tests themselves were coordinated by our local WeRobotics lab: Peru Flying Labs. Anti-venom was flown from the town of Contamana to the more remote village of Pampa Hermosa about 40 kilometers away. A regular boat (canoe) takes up to 6 hours to complete the journey. Our drone took around 35 minutes.

At night, we flew the drone back to Contamana with blood samples. While cargo drone projects typically use very expensive technology, WeRobotics prefers to use affordable and locally repairable solutions instead. Behind the scenes footage of the actual cargo drone flown in the Amazon is available in the video above.

Thanks to the success of our first drone deliveries, we’ve been invited back by the Ministry of Health and local doctors to carry out additional field tests. This explains why our Peru Flying Labs team is back in the Amazon this very week to carry out additional drone deliveries. We’re also gearing up to carry out deliveries across a distance of more than 100km using affordable drones. In parallel, we’re also working on this innovative Zika-control project with our Peru Flying Labs; drawing on lessons learned from our work in the Amazon Rainforest.

We’ll be giving a free Webinar presentation on all our efforts in Peru on Wednesday, February 22nd at 11am New York time / 4pm London. Please join our email-list for more information.

To support our local Flying Labs teams in Peru, Nepal and/or Tanzania with donations, kindly contact Peter Mosur (peter@werobotics.org). For media inquiries on the Amazon Rainforest project and WeRobotics, please contact Dr. Patrick Meier (patrick@werobotics.org). Ministry of Health officials and other local partners are also available for interviews.

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• The Drone Center’s Weekly Roundup: 2/13/17
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See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Kickstart Accelerator, one of Europe’s largest multi-corporate, zero-equity technology accelerators and an initiative of digitalswitzerland, has today announced that applications are open to international startups for its second programme.

Kickstart Accelerator will select a shortlist of up to 30 startups who will participate in an 11-week programme designed to help them develop their ideas and products across one of the programme’s verticals, including robotics & intelligent systems. In order to tap into unexplored innovation potential, Kickstart Accelerator has entered into a partnership with BaseLaunch (initiated and operated by BaselArea.swiss) to offer a healthcare-specific vertical in the life sciences hotspot Basel.

Image: Philippe Rossier

Successful applicants will be international startups with promising business ideas that have the potential to disrupt the chosen verticals and benefit the wider society. The accelerator will be operated by Impact Hub Zurich, which is part of the largest international network of entrepreneurship and innovation centers, giving the startups access to the brightest talent across the globe.

Patricia Schlenter, Programme Manager at Kickstart Accelerator, said,

Our inaugural year saw over 850 applications from across the globe and so we are looking forward to seeing the calibre of this year’s innovators. The Swiss ecosystem offers startups the opportunity to work with like-minded entrepreneurs, experienced mentors and some of the biggest corporate players to deliver impactful innovations to key industries.

This year’s shortlisted startups will meet with over 15 corporate partners and mentors, including Switzerland’s largest ICT provider Swisscom, multinational insurance firms Swiss Life and AXA Winterthur, Swiss financial services companies Credit Suisse, UBS and Raiffeisen, global consultancy firms EY and PwC, as well as Switzerland`s largest food retailers Migros and Coop among others.

Image: Philippe Rossier

The 2016 cohort led to six partnerships between the startups and corporates. Applicants for 2017 will also have the opportunity to secure a ‘Proof of Concept’ trial with one of the corporate partners. These trials will allow the startups to test the application and scalability of their products in a demanding corporate environment. The trials will also provide the startups with the best possible chance of exploring longer-term commercial partnerships with leading corporate titans and the opportunity to further develop their market offering.

Carola Wahl, Head of Transformation & Market Management at AXA Winterthur, says,

Innovation is a key area of focus for our business and we are committed to working closely with other companies to promote and strengthen Switzerland’s status as an innovation and digital hub. We are particularly interested in ‘smart cities’ as they will strongly influence society’s mobility and have a major impact on the insurance industry. Cooperation between the startups and corporate partners will allow the entrepreneurs to benefit from the corporates’ know-how and large customer networks, as well as enable them to develop new technologies and disruptive products together.

The programme will begin on 4 September, with the official Opening Ceremony taking place on 7 September in the accelerator space at the ewz-Unterwerk Selnau. The Final Demo Day and closing event will be held on 17 November, where each team will present their products to investors, corporate leaders and the media. The ewz-Unterwerk Selnau, an 8,611 sq ft. (800 sq m) industrial building, will house co-working areas, meeting rooms, brainstorming areas, pitching stages and relaxation zones designed from old shipping containers to create a collaborative environment for creativity and success.

Image: Philippe Rossier

Kickstart will not be taking any equity from the participating startups. Founders will have the chance to win up to 25`000 CHF, as well as receiving a monthly founder stipend and masterclass sessions with mentors from the corporate partners and the Swiss innovation ecosystem. Kickstart aims to develop close collaboration between the corporate partners and the startups, as well as between the startups themselves across the different verticals, during and beyond the duration of the programme.

Nicolas Bürer, Managing Director of digitalswitzerland, commented, “With the second edition taking place in Zurich and the extension of the programme to Basel, Kickstart will be one step closer to becoming the largest European startup Accelerator. digitalswitzerland is also looking to extend the scope of the programme and is carrying out research ahead of establishing Kickstart Accelerator and additional verticals in Lausanne. This is another step towards establishing Switzerland as a leading digital innovation hub worldwide.”

To apply for the programme, please visit: www.kickstart-accelerator.com.

Samsung Simband. Image: Jason Dorfman, MIT CSAIL

It’s a fact of nature that a single conversation can be interpreted in very different ways. For people with anxiety or conditions like Asperger’s, this can make social situations extremely stressful. But what if there was a more objective way to measure and understand our interactions?

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) say that they’ve gotten closer to a potential solution: an artificially intelligent wearable system that can predict if a conversation is happy, sad or neutral based on a person’s speech patterns and vitals.

“Imagine if, at the end of a conversation, you could rewind it and see the moments when the people around you felt the most anxious,” says graduate student Tuka Alhanai, who co-authored a related paper with PhD candidate Mohammad Ghassemi that they will present at next week’s Association for the Advancement of Artificial Intelligence (AAAI) conference in San Francisco. “Our work is a step in this direction, suggesting that we may not be that far away from a world where people can have an AI social coach right in their pocket.”

As a participant tells a story, the system can analyze audio, text transcriptions and physiological signals to determine the overall tone of the story with 83 percent accuracy. Using deep-learning techniques, the system can also provide a specific sentiment score for five-second intervals within a conversation.

“As far as we know, this is the first experiment that collects both physical data and speech data in a passive but robust way, even while subjects are having natural, unstructured interactions,” says Ghassemi. “Our results show that it’s possible to classify the emotional tone of conversations in real-time.”

The team is keen to point out that they developed the system with privacy strongly in mind: the algorithm runs locally on a user’s device as a way of protecting personal information. (Alhanai says that a consumer version would obviously need clear protocols for getting consent from the people involved in the conversations.)

How it works

Past studies in this area would often show participants “happy” and “sad” videos, or ask them to artificially act out specific emotive states. But in an effort to elicit more organic emotions, the team instead asked subjects to tell a happy or sad story of their own choosing.

Subjects wore a Samsung Simband, a device that captures high-resolution physiological waveforms to measure features like movement, heart rate, blood pressure, blood flow and skin temperature,. The system also captured audio data and text transcripts to analyze the speaker’s tone, pitch, energy, and vocabulary.

Mohammad Ghassemi and Tuka Alhanai converse with the wearable. Image: Jason Dorfman MIT CSAIL

“The team’s usage of consumer market devices for collecting physiological data and speech data shows how close we are to having such tools in everyday devices,” says Björn Schuller, professor and chair of Complex and Intelligent Systems at the University of Passau in Germany who was not involved in the research. “Technology could soon feel much more emotionally intelligent, or even ‘emotional’ itself.”

After capturing 31 different conversations of several minutes each, the team trained two algorithms on the data: one classified the overall nature of a conversation as either happy or sad, while the second classified each five-second block of every conversation as either positive, negative or neutral.

Alhanai notes that, in traditional neural networks, all features about the data are provided to the algorithm at the base of the network. In contrast, her team found that they could improve performance by organizing different features at the various layers of the network.

“The system picks up on how, for example, the sentiment in the text transcription was more abstract than the raw accelerometer data, says Alhanai. “It’s quite remarkable that a machine could approximate how we humans perceive these interactions, without significant input from us as researchers.”

Results

Indeed, the algorithm’s findings align well with what we humans might expect to observe. For instance, long pauses and a monotonous vocal tones were associated with sadder stories, while more energetic, varied speech patterns were associated with happier ones. In terms of body language, sadder stories were also strongly associated with increased fidgeting and cardiovascular activity, as well as certain postures like putting one’s hands on one’s face.

Graph showing real-time emotion detection. Image: MIT CSAIL

On average, the model could classify the mood of each five-second interval with an accuracy that was approximately 18 percent above chance, and a full 7.5 percent better than existing approaches. In future work, the team hopes to collect data on a much larger scale, potentially using commercial devices like the Apple Watch that would allow them to more easily deploy the system out in the world.

“Our next step is to improve the algorithm’s emotional granularity so it can call out boring, tense, and excited moments with greater accuracy instead of just labeling interactions as ‘positive’ or ‘negative’,” says Alhani. “Developing technology that can take the pulse of human emotions has the potential to dramatically improve how we communicate with each other.”

Click here to download the research paper.

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See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

The Harvard team’s exosuit is a wearable robot that directly targets the ankle joints and is made of functional textiles, cable-based actuation and a biologically-inspired control system. In this version, actuation, electronics and battery units (shown on the left) have been off-boarded to better allow the researchers to quantify the exosuit’s energy-saving potential in healthy wearers. Credit: Wyss Institute at Harvard University

Last year, Harvard’s soft exosuit team provided first proof-of-concept results showing that its wearable robot could lower energy expenditure in healthy people walking with a load on their back. Made of functional textiles, cable-based actuation and a biologically-inspired control system, the exosuit targets specific leg joints – instead of the full leg – and delivers assistance that is synchronized with the wearer’s walking mechanics in individuals ranging from people that carry heavy loads to people that are disabled by stroke or other health complications.

The team is led by Conor Walsh, a Wyss Institute Core Faculty member, John L. Loeb Associate Professor of Engineering and Applied Sciences at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) and the Founder of the Harvard Biodesign Lab.

But the question still remained. What is the effect of increasing assistance levels on the overall energy savings to wearers? These savings are the result of the energy spared due to the assistive force provided to the ankle motions, and this must be balanced with the additional energy expended by the wearer when carrying the exosuit’s mass.

Now, in a new study published as the cover article in Science Robotics, Walsh’s multidisciplinary team of roboticists, engineers, biomechanics experts and apparel designers isolated the beneficial effects of the exosuit. By removing the actuation, electronics and battery units, leaving only the exosuit’s light wearable textiles and pulling cables, the researchers were able to calculate the impact on the wearers’ joints. The study is selected as the cover article of the issue.

“In a test group of seven healthy wearers, we clearly saw that the more assistance provided to the ankle joints, the more energy the wearers could save with a maximum reduction of almost 23% compared to walking with the exosuit powered-off,” said Walsh. “To our knowledge, this is the highest relative reduction in energy expenditure observed to date with a tethered exoskeleton or exosuit.”

Interestingly, the team found that wearers significantly adapted their gait with increasing levels of assistance. The changes were most significant at the ankle joint but also at the hip as the exosuit included straps coupling the assistance from the back of the lower legs to the front of the hip in a beneficial manner.

“Other studies had reported that there can be an energy transfer between the ankle and other joints. However, by having a textile couple the ankle and hip with our soft exosuit, may have amplified this effect, contributing to the considerable energy savings we found,” said Brendan Quinlivan, a graduate student working with Walsh and one of the two first-authors on the study.

The exosuit’s soft textiles strategically position the actuating cables that assist with the ankle motion at the back of the lower legs and, through additional straps, transfer energy produced at the ankle to the front of the hip to also assist with the gait’s hip motion. Credit: Wyss Institute at Harvard University

The authors acknowledge that future studies are now required to compare the effects of fully body-worn versions of the exosuit, with the weight of the actuation and battery carried by the wearer, to walking with normal clothes to better quantify the true impact of the technology.

“This study represents another major step forward along a path that will bring this new type of ‘wearable robot’ into the marketplace where it will help soldiers and workers, as well as patients with disabilities,” said Wyss Institute Founding Director Donald Ingber, M.D., Ph.D., who is also the Judah Folkman Professor of Vascular Biology at Harvard Medical School and Professor of Bioengineering at Harvard SEAS.“The results from this study will help us find a sweet spot between the added system weight and the amount of force applied to achieve maximum benefits in energy expenditure in wearers. Defining these interdependencies in healthy people will ultimately also help us design versions suitable for people with gait abnormalities,” said Sangjun Lee, who also is a graduate student in Walsh’s team and a co-first author of the study.

The multi-disciplinary study was supported by the Wyss Institute for Biologically Inspired Engineering, the Harvard John A. Paulson School of Engineering and Applied Sciences, the Defense Advanced Research Projects Agency (DARPA)’s Warrior Web Program, the National Science Foundation, a Samsung Scholarship, the São Paulo Research Foundation and the Robert Bosch Stiftung.

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• Indego joins Ekso with FDA exoskeleton approvals
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• Exoskeletons: From helping people walk to controlling robots in space
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See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

The DOME Project of the Digger Foundation has been shortlisted as finalist of the
prestigious Robotics Award for Good taking place in Dubai in February 2017, where
a $1million price will be awarded to the best competitor among 10
international teams.

Mine clearance is a fundamental effort of international organizations like the United Nations, which is considering this threat as a top humanitarian priority. The world is now facing new kinds of warfare, as in Iraq and Syria, where cities are the main battlefields. Thus new Unexploded Ordnance (UXO) and Improvised Explosive Devices (IED) clearance challenges arise. The problem is so huge that the United Nations estimate that it will take decades to clear Syria of all unexploded devices, from hand grenades to hundreds kilogram aviation bombs and IED’s. Today, mine clearance is mainly done by hand, which is not only extremely dangerous, but also very slow and expensive.

https://www.youtube.com/watch?v=dkyXYx15vFE&feature=youtu.be

Since 1998, the Digger Foundation has successfully led de-mining projects in many countries, including Bosnia, Senegal, Mozambique and, in 2016, Angola. In rural areas, its main tool is the DIGGER 250, a 12-ton armored and remote controlled machine capable of destroying mines buried up to 20 centimeters into the ground. As a Swiss-based, non profit organization specialized in high-technology and robotics equipment dedicated to humanitarian demining, the Foundation will address the new challenge of de-mining safely in urban areas with the DOME project.

The DOME project combines robotics, bionic systems and pragmatism to bring an effective solution adapted to this new reality of urban mine clearance. The Digger Foundation has leveraged almost 20 years of field experience and research to come up with a reliable and cost-efficient concept. It solves two distinct issues: on one hand, the localization of explosive material under the rubble; on the other hand, its subsequent destruction. In a first step, mine detection dogs equipped with a DIGGER harnes–made of a GPS tracker, a camera and a loudspeaker–are deployed on the area. The combination of their smell and the smart harness will allow the dog’s handler to map precisely the location of explosives. Effective de-mining will begin with the rubble clearance and/or the transfer of the dangerous device to an open, secure space, to proceed with its destruction. This second step, during which an explosion can happen at any time, requires mechanical tools available on-site. The DOME solution is to use mass produced construction machines which will be transformed into robots and tele-operated from a safe distance, since the pilot will be able to operate the machine through a cockpit displayed in virtual reality goggles. Thus, no human lives will be put at risk even in case of a powerful explosion.

The demonstration of the DOME system will take place in Dubai on February 18 and 19
February 2017. The winner of the competition will be rewarded a $1million prize.

Click here for more information from the Digger Foundation.

If you enjoyed this article, you may also want to listen to these Robohub Podcasts:

• Robots Podcast #082: Demining and defusing, with Frédéric Guerne and Paul Bosscher
• Robots Podcast #219: ICRA 2016: Landmine Detection Challenge, with Edson Prestes

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Image: Gerd Altmann

The MIT Media Lab and the Berkman Klein Center for Internet and Society at Harvard University will serve as the founding anchor institutions for a new initiative aimed at bridging the gap between the humanities, the social sciences, and computing by addressing the global challenges of artificial intelligence (AI) from a multidisciplinary perspective.

“Artificial intelligence agents will impact every part of our lives in every society on Earth. Technology and commerce will see to that,” says Alberto Ibargüen, president and CEO of the John S. and James L. Knight Foundation, which is among those supporting the initiative.

Initially funded with $27 million from the Knight Foundation; LinkedIn co-founder Reid Hoffman; the Omidyar Network; the William and Flora Hewlett Foundation; and Jim Pallotta, founder of the Raptor Group, the Ethics and Governance of Artificial Intelligence Fund’s mission is to catalyze global research that advances AI for the public interest, with an emphasis on applied research and education. The fund will also seek to advance public understanding of AI.

“AI’s rapid development brings along a lot of tough challenges,” explains Joi Ito, director of the MIT Media Lab. “For example, one of the most critical challenges is how do we make sure that the machines we ‘train’ don’t perpetuate and amplify the same human biases that plague society? How can we best initiate a broader, in-depth discussion about how society will co-evolve with this technology, and connect computer science and social sciences to develop intelligent machines that are not only ‘smart,’ but also socially responsible?”

What makes this new initiative different and necessary is that it’s aimed at transcending barriers and breaking down silos among disciplines. As founding academic institutions, the Media Lab and Berkman Klein Center, along with other potential collaborators from the public and private sectors, will act as a mechanism to reinforce cross-disciplinary work and encourage intersectional peer dialogue and collaboration.

The fund — projected to operate with a phased approach over the next several years — will complement and collaborate with existing efforts and communities, such as the upcoming public symposium “AI Now,” which is scheduled for July 10 at the MIT Media Lab. The fund will also oversee an AI fellowship program, identify and provide support for collaborative projects, build networks out of the people and organizations currently working to steer AI in directions that help society, and also convene a “brain trust” of experts in the field.

A collaborative network

The Media Lab and the Berkman Klein Center for Internet and Society will leverage a network of faculty, fellows, staff, and affiliates who will collaborate on unbiased, sustained, evidenced-based, solution-oriented work that cuts across disciplines and sectors. This research will include questions that address society’s ethical expectations of AI, using machine learning to learn ethical and legal norms from data, and using data-driven techniques to quantify the potential impact of AI, for example, on the labor market.

Work of this nature is already being undertaken at both institutions. The Media Lab has been exploring some of the moral complexities associated with autonomous vehicles in the Scalable Cooperation group, led by Iyad Rahwan. And the Personal Robots group, led by Cynthia Breazeal, is investigating the ethics of human-robot interaction.

“AI could be as big a disruptor to the world as the Industrial Revolution was in the 18th and 19th centuries,” Rahwan says. He cites transportation systems and employment as among the areas that will likely be affected by automation and AI. “What we need is something that puts our entire society in the control loop of these systems … technologists, engineers, the public, ethicists, cognitive scientists, economists, legal scholars, anthropologists, faith leaders, government regulators — everyone crucial to protecting the public interest.”

“Artificial Intelligence provides the potential for deeply personalized learning experiences for people of all ages and stages,” says Breazeal, who emphasizes the need for AI to reach people in developing nations and underserved populations. But she adds that it is also “a kind of double-edged sword. What should it be learning and adapting to benefit you? And what should it do to protect your privacy and your security?”

Shared goals and governance

The Berkman Klein Center has been working to develop public interest-oriented solutions to many of the challenges of the digital age, incubating programs such as Creative Commons and the Digital Public Library of America. And, it is currently collaborating with the Media Lab on the “Assembly” program, which gathers high-level developers and tech industry professionals for a rigorous three-week course at Harvard University, followed by a 12-week collaborative development period to explore difficult problems in cybersecurity.

“The thread running through these otherwise disparate phenomena is a shift of reasoning and judgment away from people,” says Jonathan Zittrain, co-founder of the Berkman Klein Center and professor of law and computer science at Harvard University. “Sometimes that’s good, as it can free us up for other pursuits and for deeper undertakings. And sometimes it’s profoundly worrisome, as it decouples big decisions from human understanding and accountability. A lot of our work in this area will be to identify and cultivate technologies and practices that promote human autonomy and dignity rather than diminish it.”

The Ethics and Governance of Artificial Intelligence Fund will be governed by a small board, consisting of leadership from each participating foundation and institution. Ito says that convening all perspectives now is essential. “Instead of setting up an institution, we’ve decided to create a dynamic network. There are a number of areas where the deployment of machine learning is already starting to pose questions that are best answered in an interdisciplinary way. This project that we’re all embarking on is just the beginning.”

You might also enjoy the following articles about AI and AI/robotics policy:

• How artificial intelligence is changing our Christmas shop
• White House report: Artificial intelligence, automation, and the economy
• White House launches public workshops on AI issues
• White House releases reports focusing on opportunities and challenges of AI
• White House economic report looks to robotics for the future
• We the Geeks: Whitehouse hangouts celebrate robots, maker culture and more

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Fabrication and complete assembly of a Geneva drive device using the iMEMS method. The left panel shows the layer-by-layer fabrication of support structures and assembly of gear components. The image on the right shows the complete device after the layers have been sealed. Image: SauYin Chin, Columbia Engineering.

A team of researchers led by Biomedical Engineering Professor Sam Sia at Columbia Engineering has developed a way to manufacture microscale machines from biomaterials that can safely be implanted in the body. Working with hydrogels, which are biocompatible materials that engineers have been studying for decades, Sia has invented a new technique that stacks the soft material in layers to make devices that have three-dimensional, freely moving parts. The study, published online January 4, 2017, in Science Robotics, demonstrates a fast manufacturing method Sia calls “implantable microelectromechanical systems” (iMEMS).

By exploiting the unique mechanical properties of hydrogels, the researchers developed a “locking mechanism” for precise actuation and movement of freely moving parts, which can function as valves, manifolds, rotors, pumps, and drug delivery systems. They were able to tune the biomaterials within a wide range of mechanical and diffusive properties and to control them after implantation without a sustained power supply, such as a toxic battery. They then tested the payload delivery in a bone cancer model and found that the triggering of releases of doxorubicin from the device over 10 days showed high treatment efficacy and low toxicity, at 1/10^th of the standard systemic chemotherapy dose.

“Overall, our iMEMS platform enables development of biocompatible implantable microdevices with a wide range of intricate moving components that can be wirelessly controlled on demand and solves issues of device powering and biocompatibility,” says Sia, also a member of the Data Science Institute. “We’re really excited about this because we’ve been able to connect the world of biomaterials with that of complex, elaborate medical devices.  Our platform has a large number of potential applications, including the drug delivery system demonstrated in our paper which is linked to providing tailored drug doses for precision medicine.”

Most current implantable microdevices have static components rather than moving parts and, because they require batteries or other toxic electronics, have limited biocompatibility. Sia’s team spent more than eight years working on how to solve this problem. “Hydrogels are difficult to work with, as they are soft and not compatible with traditional machining techniques,” says Sau Yin Chin, lead author of the study, who worked with Sia. “We have tuned the mechanical properties and carefully matched the stiffness of structures that come in contact with each other within the device. Gears that interlock have to be stiff in order to allow for force transmission and to withstand repeated actuation. Conversely, structures that form locking mechanisms have to be soft and flexible to allow for the gears to slip by them during actuation, while at the same time they have to be stiff enough to hold the gears in place when the device is not actuated. We also studied the diffusive properties of the hydrogels to ensure that the loaded drugs do not easily diffuse through the hydrogel layers.”

The team used light to polymerize sheets of gel and incorporated a stepper mechanization to control the z-axis and pattern the sheets layer by layer, giving them three-dimensionality. Controlling the z-axis enabled the researchers to create composite structures within one layer of the hydrogel while managing the thickness of each layer throughout the fabrication process. They were able to stack multiple layers that are precisely aligned and, because they could polymerize a layer at a time, one right after the other, the complex structure was built in under 30 minutes.

Sia’s iMEMS technique addresses several fundamental considerations in building biocompatible microdevices, micromachines, and microrobots: how to power small robotic devices without using toxic batteries; how to make small biocompatible moveable components that are not silicon, which has limited biocompatibility; and how to communicate wirelessly once implanted (radio frequency microelectronics require power, are relatively large, and are not biocompatible). The researchers were able to trigger the iMEMS device to release additional payloads over days to weeks after implantation. They were also able to achieve precise actuation by using magnetic forces to induce gear movements that, in turn, bend structural beams made of hydrogels with highly tunable properties. (Magnetic iron particles are commonly used and are FDA-approved for human use as contrast agents.)

In collaboration with Francis Lee, an orthopedic surgeon at Columbia University Medical Center at the time of the study, the team tested the drug delivery system on mice with bone cancer. The iMEMS system delivered chemotherapy adjacent to the cancer, and limited tumor growth while showing less toxicity than chemotherapy administered throughout the body.

“These microscale components can be used for microelectromechanical systems, for larger devices ranging from drug delivery to catheters to cardiac pacemakers, and soft robotics,” notes Sia. “People are already making replacement tissues and now we can make small implantable devices, sensors, or robots that we can talk to wirelessly. Our iMEMS system could bring the field a step closer to developing soft miniaturized robots that can safely interact with humans and other living systems.”

The study, “Additive manufacturing of hydrogel-based materials for next-generation implantable medical devices,” was supported by an NSF CAREER award, NIH R01 grant (HL095477-05), and NSF ECCS-1509748. Chin was supported by the National Science Scholarship (PhD) awarded by the Agency for Science, Technology and Research (Singapore). The researchers have a patent pending.

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Image: Eunike Nugroho

What might the first western explorer have thought upon encountering whales, flamingos and iguanas? What would have they thought of these strange near-fantastical creatures?

What is it like for 21st-century people to encounter robots for the first time? Are they friend or foe? Where do they come from and what controls them? If we saw one for the first time tomorrow, what would we tell our friends?

This is the thinking behind Robot Life Survey – an alternative history series from design company After the flood that visualises Boston Dynamic’s Big Dog, Prox Dynamics’ Black Hornet Nano, and Harvard University’s Kilobot Swarm as if witnessed by an early explorer. What is our relationship with these strange, futuristic creations?

Max Gadney, After the flood director, comments: “In this series, mechanical intelligence is discovered for the first time by a mystery character and recorded for posterity and science. This explorer records their wonder, alluding to a wider picture of and cast of players. It was a fantastic project to work on at After the flood, combining the passion for art and complex mechanics that underpins much of what we do.”

This series of the Survey is available in three prints – each presenting a record of the explorer’s experiences along with snatches of field notes. A larger book based on Robot Life Survey and its explorer will follow in 2017. This will visualise more robots, notes and thinking around this new world of physical, connected intelligence.

The voice of the explorer is provided by Lloyd Shepherd, author of historical thrillers The English Monster, The Poisoned Island and Savage Magic published by Simon and Schuster. Lloyd has spent many years studying the age of enlightenment, and with a background in the tech industry he is the best person to breathe life into the world of Robot Life Survey. The art itself is by Indonesia-based artist named Eunike Nugroho, who specialises in botanical and animal life illustrations and paintings.

Enjoy the “Explorer Notes” by Lloyd Shepherd below:

Big Dog

Image: Eunike Nugroho

“Of all the creatures I’d encountered on my voyages, this was the one that most disturbed me. It stepped silently through the trees, its queasily familiar legs quick-stepping through the snow, as it sought to self-correct its balance at each contact with the ground. On its back was an enormous hessian sack, tied to its body with rope, the property, no doubt, of some Siberian tribesman who had captured this thing and adapted its programming for his own banal ends. It would have come up to above my waist, had I not been sitting in a tree watching it pass, and it was the biggest single machine I had yet seen. But this is not what disturbed me about it. What gave me pause was the memory of Lovelace’s wolf-hound, a great grey beast of enormous gentleness which had plodded this changing Earth with the exact same care as this silent machine. The dog must have been long-dead by that time, but my heart softened at the memory of it, as I watched this machine make its considered way through those deep, silent, freezing woods.”

Nano UAV

Image: Eunike Nugroho

The noise made by one of these tiny machines must be almost inaudible, but the little swarm we encountered down in the valley made an angry sound in its buzzing aggregate. I had seen plans for machines such as these in Lovelace’s study, and had marvelled at how the elegant lines had been squeezed and folded into such tiny mechanisms. Each was barely five centimetres in length, and they must be light indeed to hover in the air so effortlessly, though it is some time since anyone has caught one. Their yellow-and-silver sides reflected the harsh Afghan sun, and as they moved away from us their buzz mellowed, putting me in mind of English summer gardens, though no bee I ever saw cast a shadow such as these machines did on the orange rock, and no singular purpose guides a bee’s flight in the way it seemed to hold these little things in check, as they flew away from us, in inscrutable formation, for a destination at which I could only guess.

Kilobot Swarm

Image: Eunike Nugroho

There were too many of the little machines to count, but there may have been as many as a thousand. The herd moved like oil gliding down a steel ramp, slowly and with its only apparent purpose being adjacency, a need to hold itself together, to cohere into a shape that was a circle, then a triangle, then a square, a puddle of technology which could not make up its mind. Each of the little machines was five centimetres across, and moved on little feet which tapped on the concrete, making a sound like light rain. All each of them knew of the world was the location of its neighbours, and a simple task held in the code of no more than a dozen of them. So was this a single organism, a slime-mold made of plastic and silicon, its intention rippling across tiny invisible networks? Was each device’s awareness of its neighbours the beginnings of a kind of consciousness? And if Lovelace were standing with me now, watching these little machines ripple across the ground, would those machines detect a presence, and stop, and turn to their Creator? No answer came, and on the mechanistic puddle slid, aware only of itself.

If you enjoyed this article, you might also be interested in:

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• Why watching Westworld’s robots should make us question ourselves
• The new Westworld: Humanizing the un-human, or dehumanizing humankind?
• Ex Machina: When Turing meets Bechdel test
• President Obama discusses artificial intelligence with Media Lab Director Joi Ito

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

BMW says it will deploy a fleet of 40 cars in late 2017

Bumping up the timetables, BMW has declared it will have a fleet of 40 self-driving series 7 cars, using BMW’s technology combined with MobilEye and Intel. Intel has recently been making a push to catch up to Nvidia as a chipmaker supplier to automakers for self-driving. It hasn’t quite said what the cars will do, but they will be trying lots of different roads. So far BMW has mostly been developing its own tech. More interesting has been their announcement of plans to sell rides via their DriveNow service. This was spoken of a year ago but not much more has been said.

https://www.youtube.com/watch?v=sqRkYemvjr8

Intel also bought 15% of “HERE” the company formerly known as Navteq and Nokia. Last year, the German automakers banded together to buy HERE from Nokia and the focus has been on “HD” self-driving maps.

Hyundai, Delphi show off cars

There are demo cars out there from Delphi and a Hyundai Ioniq. Delphi’s car has been working for a while (it’s an Audi SUV) but recently they have also added a bunch of MobilEye sensors to it. Reports about the car are good, and they hope to have it ready by 2019, showing up in 2020 or 2021 cars on dealer lots.

Toyota sticks to concepts

Toyota’s main announcement is the Concept-i meant to show off some UI design ideas. It’s cute but still very much a car, though with all the silly hallmarks of a concept — hidden wheels, strangely opening doors and more.

Quanergy announces manufacturing plans for $250 solid state LIDAR

Quanergy (Note: I am on their advisory board) announced it will begin manufacturing this year of automotive grade $250 solid state LIDARs. Perhaps this will stop all the constant articles about how LIDAR is super-expensive and means that robocars must be super-expensive too. The first model is only a taste of what’s to come in the next couple of years as well.

New Ford has a sleeker design

Ford has become the US carmaker to watch (in addition to Tesla) with their announcement last year that they don’t plan to sell their robocars, only use them to offer ride service in fleets. They are the first and only carmaker to say this is their exclusive plan. Just prior to CES, Ford showed off a new test model featuring smaller Velodyne pucks and a more deliberate design.

I have personally never understood the desire to design robocars to “look like regular cars.” I strongly believe that, just like the Prius, riders in the early robocars will want them to look distinctive, so they can show off how they are in a car of the future. Ford’s carm based on the Fusion hybrid, is a nice compromise — clearly a robocar with its sensors, but also one of sleek and deliberate design.

Nvidia keeps its push

Nvidia has a new test car they have called BB8. (Do they have to licence that name?) It looks fairly basic, and they show a demo of it taking somebody for a ride with voice control, handling a lot of environments. It’s notable that at the end, the driver has to take over to get to the destination, so it doesn’t have everything, nor would we expect it. NVIDIA is pushing their multi-GPU board as the answer to how to get a lot of computing power to run neural networks in the car.

What’s coming

Announcements are due tomorrow from Nissan and probably others. I’ll report Friday from the show floor. See you there!

If you liked this post on robocars, you’ll also enjoy these articles:

• What if the city ran Waze and you had to obey it? Could this cure congestion?
• What are the right disability rules for robotaxis?
• Google car is now Waymo
• Comma.ai cancels comma-one add-on box after threats from NHTSA
• Robocar news: Comma One goes open source, creating simulations for robocars in New Zealand earthquakes

See all the latest robotics news on Robohub, or sign up for our weekly newsletter

Robotics is becoming more accessible for many people, but the complexities of legged robots mean they remain beyond the reach of most consumers. The complex mechanics, electronics and code algorithms make these robots much more difficult to simply get working. Four legged robots are especially difficult, they might seem very stable but simply lifting a leg up can cause the robot to fall.

QuadBot is an open-source, 3D printed robot that aims to change this.

QuadBot is suitable for makers of any skill level, it works straight out the box, and can be programmed using scratch based blocks. And when you’re ready to explore robotics further, every aspect of QuadBot can be changed, from the mechanical design to individual lines of Arduino code. Once you’ve mastered walking, you can add sensors to the board, and make QuadBot avoid obstacles, follow light and even dance to music!

QuadBot was released on Kickstarter and comes either as a bare bones (3D Print it yourself) kit, or as a full kit. Within the first week, QuadBot reached 100% of the £12,500 goal, and is nearly at £20,000 at the time of writing!

QuadBot is the creation of Jack Scott-Reeve and Josh Elijah, two graduates of Engineering who felt that Robotics Education needed a change. Now that it is funded (and with 5 weeks still left on the campaign), they can focus on improving the product for our early backers. This means adding goals like extra colours, sensor packs and improved learning resources. However, among many Kickstarter campaigns there’s a tendency to overstretch and under deliver, so Elijah and Reeve are letting the product sell on its base features rather than promising massive additions to the design.

Nevertheless, QuadBot has massive future potential for expansion and increased capability, whether using modular design to create QuadBot sensor modules or integrating a Raspberry Pi onto the board to allow Python coding and vision processing.

One emerging market for this technology is robotics in schools. Teaching programming is becoming more common in UK schools, as are products to augment learning like the BBC Microbit. QuadBot could potentially be integrated with existing products like Microbit and the Raspberry Pi, to bring practical robotics into schools. With large distributors like RS Components interested, we could soon see QuadBot being distributed internationally, and with a curriculum to go along with it.

At present it’s hard to say in what direction Elijah and Reeve will go with QuadBot but, with a fully funded Kickstarter already, it’s likely we’ll see many new iterations and designs in the near future!

Click here to visit the QuadBot website.

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See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

PhD student Tao Du watching the bunnycopter take off . Image credit: Jason Dorfman, MIT CSAIL

This fall’s new FAA regulations have made drone flight easier than ever for both companies and consumers. But what if the drones out on the market aren’t exactly what you want?

A new system from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) is the first to allow users to design, simulate and build their own custom drone. Users can change the size, shape and structure of their drone based on the specific needs they have for payload, cost, flight time, battery usage and other factors.
To demonstrate, researchers created a range of unusual-looking drones, including a five-rotor “pentacopter” and a rabbit-shaped “bunnycopter” with rotors of different sizes and heights.

“This system opens up new possibilities for how drones look and function,” says MIT professor Wojciech Matusik, who oversaw the project in CSAIL’s Computational Fabrication Group. “It’s no longer a one-size-fits-all approach for people who want to make and use drones for particular purposes.”

The interface lets users design drones with different rotors and rods. It also provides guarantees that its drones can take off, hover and land (which is no simple task considering the intricate technical trade-offs associated with drone weight, shape and control).

“For example, adding more rotors generally lets you carry more weight, but you also need to think about how to balance the drone to make sure it doesn’t tip,” says PhD student Tao Du, who is first author on a related paper about the system. “Irregularly-shaped drones are very difficult to stabilize, which means that they require establishing very complex control parameters.”

Du and Matusik co-authored a paper with PhD student Adriana Schulz, postdoctoral researcher Bo Zhu and assistant professor Bernd Bickel of IST Austria. It will be presented this week at the annual SIGGRAPH Asia conference in Macao, China.

How it works
Today’s commercial drones only come in a small range of options, typically with an even number of upward-facing rotors. But there are many emerging use cases for other kinds of drones. For example, having an odd number of rotors might create a clearer view for a drone’s camera, or allow the drone to carry objects with unusual shapes.

Designing these less conventional drones, however, often requires expertise in multiple disciplines, including control systems, fabrication and electronics.

“Developing multicopters like these that are actually flyable involves a lot of trial-and-error, tweaking the balance between all the propellers and rotors,” says Du. “It would be more or less impossible for an amateur user, especially one without any computer-science background.”

But the CSAIL group’s new system makes the process much easier. Users design drones by choosing from a database of parts and specifying their needs for things like payload, cost and battery usage. The system computes the sizes of design elements like rod lengths and motor angles, and looks at metrics such as torque and thrust to determine whether the design will actually work. It also uses an “LQR controller” that takes information about a drone’s characteristics and surroundings to optimize its flight plan.

One of the project’s core challenges stemmed from the fact that a drone’s shape and structure (its “geometry”) is usually strongly tied to how it has been programmed to move (its “control”). To overcome this, researchers used what’s called an “alternating direction method,” which means that they reduced the number of variables by fixing some of them and optimizing the rest. This allowed the team to decouple the variables of geometry and control in a way that optimizes the drone’s performance.

“Once you decouple these variables, you turn a very complicated optimization problem into two easy sub-problems that we already have techniques for solving,” says Du.

Du envisions future versions of the system that could proactively give design suggestions, like recommending where a rotor should go to accommodate a desired payload.

“This is the first system in which users can interactively design a drone that incorporates both geometry and control,” says Nobuyuki Umetani, a research scientist at Autodesk, Inc who was not involved in the paper. “This is very exciting work that has the potential to change the way people design.”

The project was supported in part by the National Science Foundation, the Air Force Research Laboratory and the European Union’s Horizon 2020 research and innovation program.

Click here to read the full paper.

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See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Image: Massachusetts Institute of Technology

MIT researchers and their colleagues have developed a new computational model of the human brain’s face-recognition mechanism that seems to capture aspects of human neurology that previous models have missed.

The researchers designed a machine-learning system that implemented their model, and they trained it to recognize particular faces by feeding it a battery of sample images. They found that the trained system included an intermediate processing step that represented a face’s degree of rotation — say, 45 degrees from center — but not the direction — left or right.

This property wasn’t built into the system; it emerged spontaneously from the training process. But it duplicates an experimentally observed feature of the primate face-processing mechanism. The researchers consider this an indication that their system and the brain are doing something similar.

“This is not a proof that we understand what’s going on,” says Tomaso Poggio, a professor of brain and cognitive sciences at MIT and director of the Center for Brains, Minds, and Machines (CBMM), a multi-institution research consortium funded by the National Science Foundation and headquartered at MIT. “Models are kind of cartoons of reality, especially in biology. So I would be surprised if things turn out to be this simple. But I think it’s strong evidence that we are on the right track.”

Indeed, the researchers’ new paper includes a mathematical proof that the particular type of machine-learning system they use, which was intended to offer what Poggio calls a “biologically plausible” model of the nervous system, will inevitably yield intermediary representations that are indifferent to angle of rotation.

Poggio, who is also a primary investigator at MIT’s McGovern Institute for Brain Research, is the senior author on a paper describing the new work, which appeared today in the journal Computational Biology. He’s joined on the paper by several other members of both the CBMM and the McGovern Institute: first author Joel Leibo, a researcher at Google DeepMind, who earned his PhD in brain and cognitive sciences from MIT with Poggio as his advisor; Qianli Liao, an MIT graduate student in electrical engineering and computer science; Fabio Anselmi, a postdoc in the IIT@MIT Laboratory for Computational and Statistical Learning, a joint venture of MIT and the Italian Institute of Technology; and Winrich Freiwald, an associate professor at the Rockefeller University.

Emergent properties

The new paper is “a nice illustration of what we want to do in [CBMM], which is this integration of machine learning and computer science on one hand, neurophysiology on the other, and aspects of human behavior,” Poggio says. “That means not only what algorithms does the brain use, but what are the circuits in the brain that implement these algorithms.”

Poggio has long believed that the brain must produce “invariant” representations of faces and other objects, meaning representations that are indifferent to objects’ orientation in space, their distance from the viewer, or their location in the visual field. Magnetic resonance scans of human and monkey brains suggested as much, but in 2010, Freiwald published a study describing the neuroanatomy of macaque monkeys’ face-recognition mechanism in much greater detail.

Freiwald showed that information from the monkey’s optic nerves passes through a series of brain locations, each of which is less sensitive to face orientation than the last. Neurons in the first region fire only in response to particular face orientations; neurons in the final region fire regardless of the face’s orientation — an invariant representation.

But neurons in an intermediate region appear to be “mirror symmetric”: That is, they’re sensitive to the angle of face rotation without respect to direction. In the first region, one cluster of neurons will fire if a face is rotated 45 degrees to the left, and a different cluster will fire if it’s rotated 45 degrees to the right. In the final region, the same cluster of neurons will fire whether the face is rotated 30 degrees, 45 degrees, 90 degrees, or anywhere in-between. But in the intermediate region, a particular cluster of neurons will fire if the face is rotated by 45 degrees in either direction, another if it’s rotated 30 degrees, and so on.

This is the behavior that the researchers’ machine-learning system reproduced. “It was not a model that was trying to explain mirror symmetry,” Poggio says. “This model was trying to explain invariance, and in the process, there is this other property that pops out.”

Neural training

The researchers’ machine-learning system is a neural network, so called because it roughly approximates the architecture of the human brain. A neural network consists of very simple processing units, arranged into layers, that are densely connected to the processing units — or nodes — in the layers above and below. Data are fed into the bottom layer of the network, which processes them in some way and feeds them to the next layer, and so on. During training, the output of the top layer is correlated with some classification criterion — say, correctly determining whether a given image depicts a particular person.

In earlier work, Poggio’s group had trained neural networks to produce invariant representations by, essentially, memorizing a representative set of orientations for just a handful of faces, which Poggio calls “templates.” When the network was presented with a new face, it would measure its difference from these templates. That difference would be smallest for the templates whose orientations were the same as that of the new face, and the output of their associated nodes would end up dominating the information signal by the time it reached the top layer. The measured difference between the new face and the stored faces gives the new face a kind of identifying signature.

In experiments, this approach produced invariant representations: A face’s signature turned out to be roughly the same no matter its orientation. But the mechanism — memorizing templates — was not, Poggio says, biologically plausible.

So instead, the new network uses a variation on Hebb’s rule, which is often described in the neurological literature as “neurons that fire together wire together.” That means that during training, as the weights of the connections between nodes are being adjusted to produce more accurate outputs, nodes that react in concert to particular stimuli end up contributing more to the final output than nodes that react independently (or not at all).

This approach, too, ended up yielding invariant representations. But the middle layers of the network also duplicated the mirror-symmetric responses of the intermediate visual-processing regions of the primate brain.

“I think it’s a significant step forward,” says Christof Koch, president and chief scientific officer at the Allen Institute for Brain Science. “In this day and age, when everything is dominated by either big data or huge computer simulations, this shows you how a principled understanding of learning can explain some puzzling findings.”

“They’re very careful,” Koch adds. “They’re only looking at the feed-forward pathway — in other words, the first 80, 100 milliseconds. The monkey opens its eyes, and within 80 to 100 milliseconds, it can recognize a face and push a button signaling that. The question is what goes on in those 80 to 100 milliseconds, and the model that they have seems to explain that quite well.”

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Research and development of robotic assistive technologies has gained tremendous momentum in the last decade due to several factors such as the maturity level reached by several technologies, the advances in robotics and AI and the fact that more than 700 million of persons have some kind of disability or handicap. For many people with mobility impairments, essential and simple tasks, such as dressing or feeding, require the assistance of dedicated people. Thus, the use of devices providing independent mobility can have a large impact on their quality of life.

A team of researchers from the University of Cassino, under the direction of Professor Filippo Arrichiello, is working on the development of a control architecture for assistive robotic systems operated via a Brain-Computer Interface (BCI) to allow a user to operate a robot using his thoughts. The proposed software architecture relies on widely used frameworks to operate BCIs and robots (namely, BCI2000 for the operation of the BCI and ROS for the control of the robot) integrating control, perception and communication modules developed for the application at hand.

In particular, using the BCI2000 framework, they developed a Graphical User Interface (GUI) based on P300 paradigm to allow the user to select options via a BCI Epoch+. The user can select objects from the scene and the action to perform by focusing attention on the corresponding flashing icons on a screen. By counting how many times the icon he wants to select flashes, the user generates a P300 potential that is detected via the BCI and translated in a selection operation on the GUI.

Messages related to the object and actions selected via the GUI are automatically sent to a 7DOF lightweight robot manipulator Jaco2 that has to operate in the environment. The robot control architecture relies on a perception module that allows to detection and localization of objects, and the face/mouth of the user, using a RGB-D sensor (Kinect one). The motion of the manipulator is then controlled relying on a closed loop inverse kinematic algorithm that simultaneously manages multiple setbased and equality-based tasks. Preliminary experiments have been performed where the user commands the robot via the BCI in order to move objects on a table or to select a bottle from where to drink.

Team members: prof. Filippo Arrichiello, Paolo Di Lillo (PhD student), Daniele Di Vito (PhD student), prof. Gianluca Antonelli, prof. Stefano Chiaverini.

Click here for more information on the Robotics Research Group of the DIEI.

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Paul, a member of the Care-O-bot 4 robot family, has been greeting customers in Saturn-Markt Ingolstadt since the end of October 2016 and directing them towards their desired products. Image: Saturn.

In January 2015, Fraunhofer IPA presented a prototype of the “Care-O-bot 4” service robot. The charming helper is now proving its worth in the real world. “Paul” the robot has been greeting customers in Saturn-Markt Ingolstadt since the end of October 2016 and directing them towards their desired products.

Care-O-bot 4, alias Paul, approaches Saturn customers and welcomes them to the store. If they ask him about a certain product, he accompanies the customer to the department and points them in the direction of the relevant shelf. As he indulges in small talk about the weather, or another subject, Paul turns out to be a most charming contact partner. However, he prefers to leave actual customer service to his human colleagues. Paul is able to call another member of staff for support via “Voice over IP”. Before Paul bids a fond farewell and returns to the store entrance, he still has time to ask for feedback. In this way, he discovers whether customers appreciated the interaction or not. Martin Wild, Chief Digital Officer at Media-Saturn-Holding: “With Paul, we are offering our customers the opportunity to get to know one of the most advanced robots in the world.”

Image: Fraunhofer IPA, Photographer: Rainer Bez (2015)

Modular construction facilitates a wide range of application areas

The interactive robot prototype was originally developed by Fraunhofer IPA as an assistant robot to actively support humans in areas such as the household, hotels, care homes and hospitals. IPA scientists worked together with the design studio Phoenix Design and Schunk over a period of three years to complete the fourth generation of the robot.  While the predecessors of Care-O-bot 4 were used primarily in the development of technological fundamentals from 1989 onwards, the latest version is a modular product family offering elementary commercial solutions for the first time. Fraunhofer IPA is now working with its spin-off company “Unity Robotics” to ensure the solution is suitable for use in various commercial application areas. Saturn is the first customer to make use of the robot platform in real-life scenarios. The project will initially run for six months, with practical findings from customer interactions and employees directly incorporated into the further development of robot technology.

Care-O-bot 4 providing assistance in the kitchen. Image: Phoenix Design 2015

For the assignment with Saturn, the Stuttgart-based scientists supplemented Care-O-bot 4’s software and hardware with additional features. Ulrich Reiser, IPA project manager, explains: “For example, we specified Paul’s navigation, dialog communication and facial expressions. Semvox and Phoenix Design helped implement these changes.” Laser scanners at shin level help with Paul’s orientation. Paul’s front camera and the “Shore” software developed by Fraunhofer IIS allow him to ascertain the mood of the person he is dealing with and express their emotional state. Microphones and cameras for speech and gesture recognition respectively allow Paul to communicate with those around him.

Care-O-bot 4 open for research and industry

Care-O-bot 4 is not only making a positive impression as a retail assistant. With its modular construction and open software interface, the system can be used in many different application areas. It could, for example, conceivably be used as a mobile information kiosk in shops and airports, for pick-up and delivery services in homes and offices, for security applications or as an attraction in a robotics museum. “We retain an interest in ensuring that as many scientists and companies as possible are able to work with the system in order to expand its potential areas of application”, Reiser explains. Within the framework of the Care-O-bot 4 research platform, experts from around the world can further develop the software and hardware components. Together with the spin-off company Unity Robotics, IPA experts are intending to adapt applications to the needs of companies, making Care-O-bot 4 available as a commercial product.

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Clearpath Robotics, a leading provider of mobile robotic platforms for research and development, has partnered with ARGO XTR to release Warthog – a large, amphibious, all-terrain mobile robot designed for application development. Warthog enables researchers to reliably test, validate, and advance their robotics research faster than ever before in real-world conditions, whether on land or in water.

“ARGO XTR (Xtreme Terrain Robotics) has a terrific record of manufacturing rock-solid outdoor platforms,” says Julian Ware, General Manager for Research Solutions at Clearpath Robotics. “Combined with our expertise in robotics, we’ve developed rugged platform suitable for a wide range of robotics applications in mining, agriculture, forestry, space, and environmental monitoring.”

Warthog’s light-weight aluminum chassis, low ground pressure, passive suspension system, and 24” traction tires allow it to easily traverse a variety of tough terrains including soft soils, thick muds and steep grades, all while carrying up to 272 kg of payload.    With built-in bilge pumps and an IP rating of 67, Warthog is fully weather-proof and amphibious, capable of moving through deep waterways at up to 4 km/h, or travel at speeds of up to 18 km/h while on land. The all-electric, skid steer platform has expandable power allowing for a runtime of 6 hrs and can be outfitted with quad tracks for ultimate traction and maneuverability in snow and sand.

“ARGO XTR is excited to partner with a progressive robotics company like Clearpath with our platform,” says Jason Scheib, ARGO XTR Robotics Program Director.  “The combination of our proven experience in amphibious and extreme terrain environments with our platforms with the progressive software and sensor integration from Clearpath Robotics, has created a second to none solution for a myriad of research and commercial applications.”

Designed for end-to-end integration and customization, Warthog includes an internal computer, IMU, wheel encoders, and mounting racks, as well as accessible user power and communication ports for integrating sensors, manipulators, and other third-party hardware.   Warthog is shipped with the Robot Operating System (ROS) preconfigured and a Gazebo simulation model, allowing researchers to get started quickly with existing research and widely available open-source ROS libraries.

Click here for more information.

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I’m delighted to announce a new game-changing standard for building robot hardware components: H-ROS (the Hardware Robot Operating System). H-ROS provides manufacturers tools for building interoperable robot components that can easily be exchanged or replaced between robots. H-ROS is about supporting a common environment of robot hardware components, where manufacturers comply with standard interfaces built upon the popular Robot Operating System (ROS).

Powered by ROS and built with industry and developers in mind, H-ROS classifies robot components in 5 types: sensing — used to perceive the world; actuation — allowing interaction with the environment; communication — providing a means of interconnection; cognition — the brain of the robot and hybrid; and the components that group together different sub-components under a common interface. These building-block-style parts come as reusable and reconfigurable components, allowing developers to easily upgrade their robots with hardware from different manufacturers, and add new features in seconds.

Motivation and origin:

Building a robot is tricky. Therefore, it makes sense to reuse existing work in an effort to reduce complexity. Unfortunately, nowadays there are few projects, either in industry or academy, that reuse hardware. Robots are generally built by multidisciplinary teams (generally a whole research group or company division), and different engineers get involved in the mechanical, electrical and logical design. Most of the time is spent dealing with the hardware/software interfaces, and little effort is put into behavior development or real-world scenarios.

Existing hardware platforms—although becoming more common—lack extensibility. Examples can be seen in several commercial and industrial robots that hit the market recently, and already include a common software infrastructure (generally the Robot Operating System(ROS)) but lack of a hardware standard.

With H-ROS, building robots will be about placing H-ROS-compatible hardware components together to build new robot configurations. Constructing robots will no longer be restricted to a small elite with high technical skills. It will be extended to the wider majority with a general understanding of the sensing and actuation needed for a particular scenario.

H-ROS was initially funded by the US Defense Advanced Research Projects Agency (DARPA) through the Robotics Fast Track program in 2016 and developed by Erle Robotics.

H-ROS was showcased and presented officially at ROSCon 2016 (October 8th-9th) in Seoul, South Korea, and is now available for selected industry partners and will soon be released for the wider robotics community.

Click here for the official H-ROS website

As of yesterday, you can get the adorable and versatile humanoid robot NAO from Aldebaran Robotics for yourself, even if you are not an academic or a hardcore developer. According to Génération Robots, a European partner of Aldebaran Robotics, they are selling NAO Next Gen (that’s the fourth of the four versions of NAOs out there) with the starting price of 5628 €. In North America, the RobotsLab is offering NAO for $7990 – down from $16,000.

If you are a roboticist (especially an academic), you’ve surely come across NAO before at your colleagues’ labs, conferences, and exhibition booths. It’s a 25-DOF platform that has been a definite friend to researchers studying human-robot interaction (HRI).

To give you an idea, NAO was at the 9th ACE/IEEE International Conference on Human-Robot Interaction last week and helped host the voting of the best video awards. On behalf of the chair of the video session, it sat on the podium and instructed the audience to clap and cheer on for the video submissions to be selected for the awards.

Apart from the fact that it has two HD cameras, four microphones, 2 hi-Fi speakers, and has been on TED more than once, this beloved robot comes with fun applications such as “NAO plays soccer”. Although I couldn’t tell you how well the application works, you can check it out for yourself from March 18th to 22nd if you’ll be attending Innorobo this year.

It’s still too pricey to be purchased as the next Christmas present for a child in your family. But it looks like that’s not what Aldebaran and  Génération intends to sell it for anyway. It’s definitely not too expensive as a platform to do HRI research on, use it to start your own robots-at-home or robots-for-entertainment business, or have it as your own personal robot because you’re just that kind of a person.

So, would you buy it? What would you use it for?

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There’s not much more about Sphero 2.0 on the Orbotix website other than this two-minute video, but Evan Ackerman of Automaton has a little more information. The following is from his article:

Today, Orbotix is introducing the Sphero 2.0, packed with new hardware that makes it faster, smarter, faster, brighter, and faster than ever. And did we mention it’s faster? Because it’s definitely faster.