In this episode, Abate flew to Denver, Colorado, to get a behind-the-scenes look at the future of recycling with Joe Castagneri, the head of AI at Amp Robotics. With Materials Recovery Facilities (MRFs) processing a staggering 25 tons of trash per hour, robotic sorting is the clear long-term solution.

Recycling is a for-profit industry. When the margins don’t make sense, the items will not be recycled. This is why Amp’s mission to use robotics and AI to bring down the cost of recycling and increase the number of items that can be sorted for recycling is so impactful.

Joe Castagneri
Joe Castagneri graduated with his Master of Science in Applied Mathematics, with an undergrad degree in Physics. While still in university, he first joined the team at Amp Robotics in 2016 where he worked on Machine Learning models to identify recyclables in video streams of Trash in Materials Recovery Facilities (MRFs). Today, he is the Head of AI at Amp Robotics where he is changing the economics of recycling through automation.

Yu Sun, Professor of Computer Science and Engineering at the University of South Florida, created and organized the Robotic Grasping and Manipulation Competition. Yu talks about the impact robots will have in domestic environments, the disparity between industry and academia showcased by competitions, and the commercialization of research.

Yu Sun

Yu Sun is a Professor in the Department of Computer Science and Engineering at the University of South Florida (Assistant Professor 2009-2015, Associate Professor 2015-2020, Associate Chair of Graduate Affairs 2018-2020). He was a Visiting Associate Professor at Stanford University from 2016 to 2017, and received his Ph.D. degree in Computer Science from the University of Utah in 2007. Then he had his Postdoctoral training at Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA (2007-2008) and the University of Utah (2008-2009).

He initiated the IEEE RAS Technical Committee on Robotic Hands, Grasping, and Manipulation and served as its first co-Chair. Yu Sun also served on several editorial boards as an Associate Editor and Senior Editor, including IEEE Transactions on Robotics, IEEE Robotics and Automation Letters (RA-L), ICRA, and IROS.

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Bill Smart, Professor of Mechanical Engineering and Robotics at Oregon State University, helped start competitions as part of ICRA. In this episode, Bill dives into the high-level decisions involved with creating a meaningful competition. The conversation explores how competitions are there to showcase research, potential ideas for future competitions, the exciting phase of robotics we are currently in, and the intersection of robotics, ethics, and law.

Bill Smart

Dr. Smart does research in the areas of robotics and machine learning. In robotics, Smart is particularly interested in improving the interactions between people and robots; enabling robots to be self-sufficient for weeks and months at a time; and determining how they can be used as personal assistants for people with severe motor disabilities. In machine learning, Smart is interested in developing strategies for teaching robots to act effectively (or even optimally), based on long-term interactions with the world and given intermittent and at times incorrect feedback on their performance.

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In this episode, Audrow Nash speaks to Tim Chung, Program Manager in the Tactical Technology Office at the Defense Advanced Research Projects Agency (DARPA), on the DARPA Subterranean (SubT) Challenge. The SubT Challenge is a robotics challenge that aims to develop innovative technologies that would augment operations underground. In this conversation, they talk about the motivation of the SubT Challenge, its systems (hardware) and virtual (simulation) challenges, how the resulting technology has been shared with the world, on benchmarking the robotics behaviors in simulation, on the challenge of scoping the SubT Challenge, and on building the final environment for the systems challenge.

Episode Links

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Lilly interviews Stephanie Schneider, a PhD candidate at Stanford working on unconventional space robotics. Schneider explains her work on Reachbot, a long-reach crawling and anchoring robot, which repurposes extendable booms for mobile manipulation. They discuss the challenges and exciting elements of robotic prototyping for low-gravity or otherwise unique environments.

Stephanie Schneider

Stephanie Schneider is a Ph.D. candidate in Aeronautics and Astronautics in the Autonomous Systems Laboratory at Stanford, working with Professor Marco Pavone. She received her BS in Mechanical Engineering from Cornell University in 2014. Stephanie’s research interests include real-time spacecraft motion-planning, grasping and manipulation in space, and adaptive control for autonomous robotics.

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RE2 Sapien 6M Robot Arm

In this episode, Shihan Lu interviews Travis Schneider, Business Development Manager at RE2 Robotics, focusing on their work on mobile outdoor manipulation. Travis introduces several robotic products and services of RE2, including RE2 Sapien robotic arms and human-centered system integration, and how they can be used to help rather than replace workers. He also shares his thoughts about challenges in human-robot collaboration.

Travis Schneider
Travis Schneider, Business Development Manager at RE2 Robotics (RE2), works to foster commercial B2B partnerships with groups interested in leveraging RE2’s robotic technology for applications in a wide variety of markets, including aviation, construction, energy, and medical. With a background in Mechanical Engineering, Travis has an intimate knowledge of many of the fundamentals associated with robotic systems including electromechanical design, motion control, and associated software.

Links

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Matt Bilsky, founder and CEO of FLX Solutions, discusses the snake-like robot he invented called the FLX BOT. The FLX BOT consists of modular links, each with a joint that can extend and rotate to get into tight spaces. Each link includes sensors including inertial measurement units and a camera. The robot is used to navigate and work in challenging environments, such as above ceilings and within walls. Matt discusses the key innovations of his product as well as his academic and entrepreneurial journey that led him to the FLX BOT.

Matt Bilksy

Matt Bilsky, PhD, PE is the inventor of the FLX BOT, a licensed Professional Engineer, a Mechanical Engineering professor at Lehigh University, and a former repair/maintenance contractor. In 2017, he was awarded with the Lehigh University Entrepreneurship Educator of the Year. Matt has a Mechanical Engineering Ph.D. from Lehigh University focused on smart product design, Technical Entrepreneurship, and mechatronics. He holds two additional Lehigh degrees: a BS in Mechanical Engineering with an Electrical Engineering minor and a Master of Engineering degree also in Mechanical Engineering. Since he was a child Matt has been an innovator. In his basement shop he designed and built numerous electronic gadgets. In 2003 he started his first company, Mattcomp Services LLC, offering computer repair, networking, home theater, and handyman services. He also created a web hosting company in 2005, Mattcomp Hosting, including all necessary back-end components on dedicated servers.

Links

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Interesting discussion with Prof. Ali Khademhosseini, CEO of the Terasaki Institute, and one of the pioneers of the Bioengineering field. Prof. Ali’s journey from Harvard and UCLA to the Terasaki Institute is truly inspiring. What does the institute do to bring a product to the real world? Learn about the design challenges of biomaterials, organs on a chip, and soft robotics in this episode of the IEEE RAS Soft Robotics Podcast.

You can also listen to this podcast on Spotify and Apple Podcasts, and explore support options on Patreon.

MIT researchers have developed an automated way to design customized hardware, or “brains,” that speeds up a robot’s operation. Image credits: Jose-Luis Olivares, MIT

By Daniel Ackerman

Contemporary robots can move quickly. “The motors are fast, and they’re powerful,” says Sabrina Neuman.

Yet in complex situations, like interactions with people, robots often don’t move quickly. “The hang up is what’s going on in the robot’s head,” she adds.

Perceiving stimuli and calculating a response takes a “boatload of computation,” which limits reaction time, says Neuman, who recently graduated with a PhD from the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). Neuman has found a way to fight this mismatch between a robot’s “mind” and body. The method, called robomorphic computing, uses a robot’s physical layout and intended applications to generate a customized computer chip that minimizes the robot’s response time.

The advance could fuel a variety of robotics applications, including, potentially, frontline medical care of contagious patients. “It would be fantastic if we could have robots that could help reduce risk for patients and hospital workers,” says Neuman.

Neuman will present the research at this April’s International Conference on Architectural Support for Programming Languages and Operating Systems. MIT co-authors include graduate student Thomas Bourgeat and Srini Devadas, the Edwin Sibley Webster Professor of Electrical Engineering and Neuman’s PhD advisor. Other co-authors include Brian Plancher, Thierry Tambe, and Vijay Janapa Reddi, all of Harvard University. Neuman is now a postdoctoral NSF Computing Innovation Fellow at Harvard’s School of Engineering and Applied Sciences.

There are three main steps in a robot’s operation, according to Neuman. The first is perception, which includes gathering data using sensors or cameras. The second is mapping and localization: “Based on what they’ve seen, they have to construct a map of the world around them and then localize themselves within that map,” says Neuman. The third step is motion planning and control — in other words, plotting a course of action.

These steps can take time and an awful lot of computing power. “For robots to be deployed into the field and safely operate in dynamic environments around humans, they need to be able to think and react very quickly,” says Plancher. “Current algorithms cannot be run on current CPU hardware fast enough.”

Neuman adds that researchers have been investigating better algorithms, but she thinks software improvements alone aren’t the answer. “What’s relatively new is the idea that you might also explore better hardware.” That means moving beyond a standard-issue CPU processing chip that comprises a robot’s brain — with the help of hardware acceleration.

Hardware acceleration refers to the use of a specialized hardware unit to perform certain computing tasks more efficiently. A commonly used hardware accelerator is the graphics processing unit (GPU), a chip specialized for parallel processing. These devices are handy for graphics because their parallel structure allows them to simultaneously process thousands of pixels. “A GPU is not the best at everything, but it’s the best at what it’s built for,” says Neuman. “You get higher performance for a particular application.” Most robots are designed with an intended set of applications and could therefore benefit from hardware acceleration. That’s why Neuman’s team developed robomorphic computing.

The system creates a customized hardware design to best serve a particular robot’s computing needs. The user inputs the parameters of a robot, like its limb layout and how its various joints can move. Neuman’s system translates these physical properties into mathematical matrices. These matrices are “sparse,” meaning they contain many zero values that roughly correspond to movements that are impossible given a robot’s particular anatomy. (Similarly, your arm’s movements are limited because it can only bend at certain joints — it’s not an infinitely pliable spaghetti noodle.)

The system then designs a hardware architecture specialized to run calculations only on the non-zero values in the matrices. The resulting chip design is therefore tailored to maximize efficiency for the robot’s computing needs. And that customization paid off in testing.

Hardware architecture designed using this method for a particular application outperformed off-the-shelf CPU and GPU units. While Neuman’s team didn’t fabricate a specialized chip from scratch, they programmed a customizable field-programmable gate array (FPGA) chip according to their system’s suggestions. Despite operating at a slower clock rate, that chip performed eight times faster than the CPU and 86 times faster than the GPU.

“I was thrilled with those results,” says Neuman. “Even though we were hamstrung by the lower clock speed, we made up for it by just being more efficient.”

Plancher sees widespread potential for robomorphic computing. “Ideally we can eventually fabricate a custom motion-planning chip for every robot, allowing them to quickly compute safe and efficient motions,” he says. “I wouldn’t be surprised if 20 years from now every robot had a handful of custom computer chips powering it, and this could be one of them.” Neuman adds that robomorphic computing might allow robots to relieve humans of risk in a range of settings, such as caring for covid-19 patients or manipulating heavy objects.

“This work is exciting because it shows how specialized circuit designs can be used to accelerate a core component of robot control,” says Robin Deits, a robotics engineer at Boston Dynamics who was not involved in the research. “Software performance is crucial for robotics because the real world never waits around for the robot to finish thinking.” He adds that Neuman’s advance could enable robots to think faster, “unlocking exciting behaviors that previously would be too computationally difficult.”

Neuman next plans to automate the entire system of robomorphic computing. Users will simply drag and drop their robot’s parameters, and “out the other end comes the hardware description. I think that’s the thing that’ll push it over the edge and make it really useful.”

This research was funded by the National Science Foundation, the Computing Research Agency, the CIFellows Project, and the Defense Advanced Research Projects Agency.

In this episode, Abate follows up with Benjamin Pietro Filardo, founder of Pliant Energy Systems and NACROM, the North American Consortium for Responsible Ocean Mining. Pietro talks about the deep sea mining industry, an untapped market with a massive potential for growth. Pietro discusses the current proposed solutions for deep sea mining which are environmentally destructive, and he offers an alternative solution using swarm robots which could mine the depths of the ocean while creating minimal disturbance to this mysterious habitat.

Benjamin “Pietro” Filardo
After several years in the architectural profession, Pietro founded Pliant Energy Systems to explore renewable energy concepts he first pondered while earning his first degree in marine biology and oceanography. With funding from four federal agencies he has broadened the application of these concepts into marine propulsion and a highly novel robotics platform.

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Interesting discussion with Hod Lipson, head of Creative Machines Lab, Columbia University in New York. Can robots be self-aware? Can they design other robots and self-repair? Why should we evolve robots to do tasks that animals do so well? Why don’t we have useful autonomous robots in the real world yet? Find out Hod’s answers to these questions and updates on VoxCAD development for designing and simulation of soft robots in this episode of the IEEE RAS Soft Robotics Podcast.

What’s more, Hod gave his personal advice to roboticists being interviewed for an assistant professorship and to 1st-year robotics PhD students looking for a thesis topic, and he also commented on his approach to the ethical dilemma of military funding scientific research. I hope you enjoy listening to the episode! You can check Hod Lipson’s portfolio here: https://www.hodlipson.com/

You can also listen to this podcast on Spotify and Apple Podcasts, and explore support options on Patreon.

Abate interviews Benjamin “Pietro” Filardo, CEO and founder of Pliant Energy Systems. At PES, they developed a novel form of actuation using two undulating fins on a robot. These fins present multiple benefits over traditional propeller systems including excellent energy efficiency, low water turbulence, and an ability to maneuver in water, land, and ice. Aside from its benefits on a robot, Pietro also talks about its advantages for harnessing energy from moving water.

Watch this episode on YouTube

Benjamin “Pietro” Filardo
After several years in the architectural profession, Pietro founded Pliant Energy Systems to explore renewable energy concepts he first pondered while earning his first degree in marine biology and oceanography. With funding from four federal agencies he has broadened the application of these concepts into marine propulsion and a highly novel robotics platform.

Links

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In this episode, Abate interviews Josh Lessing, co-founder and CEO of Root AI. At Root AI they are developing a system that tracks data on the farm and autonomously harvests crops using delicate grippers and computer vision. Lessing talks about the path they took to build a product with good market fit and how they brought a venture capital backed startup to market.

Josh Lessing

Josh is one of the world’s leading minds on developing robotics and AI systems for the food industry, previously serving as the Director of R&D at Soft Robotics Inc. His current venture, Root AI, is integrating advanced robotics, vision systems and machine perception to automate agriculture. Josh was a Postdoctoral Fellow in Materials Science & Robotics at Harvard University, having earned his Ph.D. studying Biophysics & Physical Chemistry at the Massachusetts Institute of Technology and received an Sc.B. in Chemistry from Brown University.

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In this episode, Audrow Nash interviews Eric Diller, Assistant Professor at the University of Toronto, on wireless micro-scale robots that could eventually be used in human surgery.  Diller speaks about the design, control, and manufacture of micro-scale surgical robotic devices, as well as when we might see this technology in the operating room.

Eric Diller

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In this episode, Kate Zhou interviews Farrell Helbling, postdoctoral fellow at Harvard Microrobotics lab, who has worked on developing the RoboBee, an insect-inspired robot that is the lightest vehicle to achieve untethered flight. Farrell discusses challenges with building the robot at centimeter-scale as well as integration of sensors and power electronics particularly in considerations with weight trade-offs.

Farrell Helbling

Farrell Helbling is a post doctoral fellow at Harvard University, where she focuses on the systems-level design of the Harvard RoboBee, an insect-scale flapping wing robot. Her research looks at the integration of the control system, sensors, and power electronics within the strict weight and power constraints of the vehicle. Her work led to the first untethered flight of an insect scale vehicle and was recently featured on the cover of Nature. She is the recipient of a NSF Graduate Research Fellowship, a 2018 Rising Star in EECS, and co-author on the IROS 2015 Best Student Paper for an insect-scale, hybrid aerial-aquatic vehicle. Her work on the RoboBee project is also featured at the Boston Museum of Science, World Economic Forum, London Science Museum, and the Smithsonian, as well as in the popular press (PBS NewsHour, The New York Times, Science Friday, BBC, and Wired). She is interested in the codesign of mechanical and electrical systems for mass-, power-, and computation-constrained robots.

Links

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In this episode, Shihan Lu interviews Dr. Heather Culbertson, Assistant Professor in the Computer Science Department at the University of Southern California, about her work in haptics. Dr. Culbertson discusses the data-driven realistic texture modeling and rendering, haptic technologies in the social touch, the combination of haptics and robots, expectations and obstacles of haptics in the next 5 years.

Heather Culbertson

Heather Culbertson is a Gabilan Assistant Professor of Computer Science at the University of Southern California. Her research focuses on the design and control of haptic devices and rendering systems, human-robot interaction, and virtual reality. Particularly she is interested in creating haptic interactions that are natural and realistically mimic the touch sensations experienced during interactions with the physical world. Previously, she was a research scientist in the Department of Mechanical Engineering at Stanford University where she worked in the Collaborative Haptics and Robotics in Medicine (CHARM) Lab. She received her PhD in the Department of Mechanical Engineering and Applied Mechanics (MEAM) at the University of Pennsylvania in 2015 working in the Haptics Group, part of the General Robotics, Automation, Sensing and Perception (GRASP) Laboratory.

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In this episode, our interviewer Audrow Nash speaks to Gil Weinberg, Professor in Georgia Tech’s School of Music and the founding director of the Georgia Tech Center for Music Technology. Weinberg leads a research lab called the Robotic Musicianship group, which focuses on developing artificial creativity and musical expression for robots and on augmented humans. Weinberg discusses several of his improvisational robots and how they work, including Shimon, a multi-armed robot marimba player, as well as his work in prosthetic devices for musicians.

Below is a video that includes Shimon on marimba, Jason Barnes playing drums with a prostetic, and Prof. Gil Weinberg on bass guitar.

Gil Weinberg

Gil Weinberg is a professor in Georgia Tech’s School of Music and the founding director of the Georgia Tech Center for Music Technology, where he leads the Robotic Musicianship group. His research focuses on developing artificial creativity and musical expression for robots and augmented humans. Among his projects are a marimba playing robotic musician called Shimon that uses machine learning for jazz improvisation, and a prosthetic robotic arm for amputees that restores and enhances human drumming abilities. Weinberg has presented his work worldwide in venues such as The Kennedy Center, The World Economic Forum, Ars Electronica, Smithsonian Cooper-Hewitt Museum, SIGGRAPH, TED-Ed, DLD and others. His music has been performed with orchestras such as Deutsches Symphonie-Orchester Berlin, the National Irish Symphony Orchestra, and the Scottish BBC Symphony while his research has been disseminated through numerous journal articles and patents. Weinberg received his M.S. and Ph.D. in Media Arts and Sciences from MIT and his B.A. from the interdisciplinary program for fostering excellence in Tel Aviv University.

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Source: CC0

In this episode, Audrow Nash interviews Zhuoran Zhang, PhD student at the University of Toronto, about how robots can be used to assist in artificial insemination. Zhang discusses how precise robotic manipulators can be used to extract a single sperm and how sperm can be evaluated for fitness using computer vision. Zhang also discusses his future plans.

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In this episode, Lauren Klein interviews Michal Luria, a PhD candidate in the Human-Computer Interaction Institute at Carnegie Mellon University, about research that explores the boundaries of Human-Robot Interaction. Michal draws inspiration from the Medieval Times for her project to test how historical automata can inform modern robotics. She also discusses her work with cathartic objects to support emotional release.

Michal Luria

Michal Luria is a PhD candidate in the Human-Computer Interaction Institute at Carnegie Mellon University, advised by Professors Jodi Forlizzi and John Zimmerman. Michal’s research centers on exploring alternative ways for humans to interact with agents and social robots. Prior to her PhD, Michal studied Interactive Communication at the Interdisciplinary Center Herzliya in Israel.

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In this episode, Audrow Nash interviews Bernt Børnich, CEO, CTO, and Co-founder of Halodi Robotics, about Eve (EVEr3), a general purpose full-size humanoid robot, capable of a wide variety of tasks.  Børnich discusses how Eve can be used in research, how Eve’s motors have been designed to be safe around humans (including why they use a low gear ratio), how they do direct force control and the benefits of this approach, and how they use machine learning to reduce cogging in their motors.  Børnich also discusses the longterm goal of Halodi Robotics and how they plan to support researchers using Eve.

Below are two videos of Eve. The first is a video of how Eve can be used as a platform to address several research questions.  The second shows Eve moving a box and dancing.

Bernt Børnich

Photographer: Tonje Kornelie

Bernt Børnich is the CEO and CTO of Halodi Robotics, and the inventor of their Revo1 motors. He holds a bachelor of robotics and nano-electronics from the University of Oslo.

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In this episode, Audrow Nash speaks with Ian Bernstein, Founder and Head of Product at Misty Robotics, about a robotics platform designed for developers called Misty II.  Bernstein discusses the motivation behind making a robotics platform for developers (relating it to personal computers), Misty II’s hardware extensibility and software “skills,” and the future direction of Misty Robotics.

A video introducing the Misty II platform:

Ian Bernstein

Ian Bernstein is Founder and Head of Product at Misty Robotics, a spin-off company from Sphero, Inc. focused on building personal robots for the home and office. In this role, Bernstein leads Misty Robotics’ product development and design. Prior to Misty Robotics, Bernstein served as Founder and Chief Technology Officer at Sphero, Inc. that has shipped more than 3 million robots to date. Bernstein joined TechStars in 2010 with Sphero co-founder Adam Wilson and created Sphero, the original app-enabled robotic ball. Bernstein has had a lifelong passion for robotics and creation and has been building robots since he was 12. Ian holds a BS in Electrical and Electronics Engineering from Colorado State University.

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In this interview, Audrow Nash interviews Kristoffer Richardsson, Michael Zillich, and Paulo Alvito.

Kristoffer Richardsson, Developer at Bitcraze in Sweden, speaks about small open-source unmanned aerial vehicle and different ways of localizing them.

Michael Zillich, CTO of Blue Danube Robotics in Austria, discusses a robotics platform for picking up toys, called Kenny. Zillich talks about Kenny’s design, including sensors and its robotic arm. He discusses how children can show the robot where to put specific toys, about how this large European project was coordinated, and about possibly commercializing this platform.

Paulo Alvito, CEO and CTO and Co-founder of IDMind in Portugal, discusses remote inspection robot for sewers. Alvito discusses what it means to be remotely autonomous, the design of the robot, and how these robots communicate data.

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In this interview, Audrow Nash interviews Gabriel Lopes, Robot and Control Scientist at Robot Care Systems, and Bernt Børnich, CEO and Co-founder of Halodi Robotics.

Gabriel Lopes speaks about an assistive walker with additional functionality.  Lopes discusses how the walker can be used, including driving up to the person, helping them stand up, exercise, and make video calls.  He also discusses building a software language for non-technical people that work with older adults. Lopes also speaks about the future of their company.

Bernt Børnich speaks about the humanoid platform at Halodi Robotics. Børnich’s long-term goal is to create an affordable home robot. He discusses the design and form factor of this robot. This includes the custom motor designed for high torque, and how the robot can “lean in” from a joint at its ankle. Børnich discusses the timeline, speculated cost, and working with investors on the long term goal of building an affordable humanoid robot.

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In this episode, Audrow Nash interviews Alexandros Kogkas, Katie Driggs-Campbell, and Martin Karlsson about the work they presented at the 2018 International Conference on Intelligent Robots and Systems (IROS) in Madrid, Spain.

Alexandros Kogkas is a PhD Candidate at the Imperial College London and he speaks about an eye tracking framework to understand where a person is looking.  This framework can be used to understand a person’s intentions, for example to hand a surgeon the correct tool or helping a person who is paraplegic.   Kogkas discusses how the framework works, possible applications, and his future plans for this framework.

Katie Driggs-Campbell is a Post Doctoral Researcher at Stanford’s Intelligent System Laboratory and is—soon to be—an Assistant Professor at University of Illinois Urbana-Champaign (UIUC). She speaks about making inferences about the world from human actions, specifically in the context of autonomous cars.  In the work she discusses, they use a model of a human driver that they use infer what is happening in the world, for example a human using a crosswalk. Driggs-Campbell talks about how they evaluate this work.

Martin Karlsson is a PhD student at Lund University in Sweden, and he speaks about a haptic interface to mirror robotic arms that requires no force sensing.  He discusses a feedback law that allows a mirroring of forces and his future work to deal with joint friction.

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In this episode, Audrow Nash interview Magnus Egerstedt, Professor at the Georgia Institute of Technology, about a way for anyone interested in swarm robotics to test their ideas on hardware, called the Robotarium.  The Robotarium is a 725-square-foot lab at the the Georgia Institute of Technology that houses nearly 100 rolling and flying robots.  To test their ideas, people can write their own programs, upload them to the Robotarium, and then watch the machines carry out their commands.

In this interview, Egerstedt speaks about the kinds of robots used in the Robotarium, design decisions in making the Robotarium, the differences between doing research in simulation and on hardware, and about lessons learnt and the challenges of building the Robotarium.

Magnus Egerstedt

Dr. Magnus Egerstedt is the Steve W. Chaddick School Chair and Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. He holds secondary appointments in the Woodruff School of Mechanical Engineering, the School of Interactive Computing, and the Guggenheim School of Aerospace Engineering, and has previously served as the Executive Director for the Institute for Robotics and Intelligent Machines at Georgia Tech. He received the M.S. degree in Engineering Physics and the Ph.D. degree in Applied Mathematics from the Royal Institute of Technology, Stockholm, Sweden, the B.A. degree in Philosophy from Stockholm University, and was a Postdoctoral Scholar at Harvard University. Dr. Egerstedt conducts research in the areas of control theory and robotics, with particular focus on control and coordination of complex networks, such as multi-robot systems, mobile sensor networks, and cyber-physical systems. Magnus Egerstedt is a Fellow of the IEEE and has received a number of teaching and research awards, including the Ragazzini Award from the American Automatic Control Council, the Outstanding Doctoral Advisor Award and the HKN Outstanding Teacher Award from Georgia Tech, the Alumni of the Year Award from the Royal Institute of Technology, and the CAREER Award from the U.S. National Science Foundation.

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In this episode, Marwa ElDiwiny interview Peer Fisher, a Professor of Physical Chemistry at the University of Stuttgart and the Director of the Micro Nano and Molecular Systems Lab at the Max Planck Institute for Intelligent Systems.  Fischer discusses micro robots that has been designed to move inside of environments similar to the human body called, “micro swimmers.”  He talks about how they are fabricated, powered, and how they can move with light or “nano propellers.”  Fischer also discusses simulating human tissue and the future of micro and nano robots, including how they could be a replacement for certain surgeries.

Peer Fischer

Peer Fischer is a Professor of Physical Chemistry at the University of Stuttgart and he heads the independent Micro Nano and Molecular Systems Lab at the Max Planck Institute for Intelligent Systems in Stuttgart.

He received a BSc. degree in Physics from Imperial College London and a Ph.D. from the University of Cambridge. He was a NATO (DAAD) Postdoctoral Fellow at Cornell University, before joining the Rowland Institute at Harvard. At Harvard he held a Rowland Fellowship and directed an interdisciplinary research lab for five years. In 2009 he received an Attract Award from the Fraunhofer Society which led him to set up a photonics lab at the Fraunhofer Institute for Physical Measurement Techniques in Freiburg. In 2011 he moved his lab to the Max Planck Institute for Intelligent Systems in Stuttgart, and since 2013 he is a Professor at the University of Stuttgart.

Links

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• Micro Swimmers
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In this episode, Marwa Mohammed Alaa Eldean Eldiwiny interviews Yong-Lae Park, Associate Professor at Seoul National University in South Korea, about the bio-inspired design and manufacture of soft robots and microrobots for healthcare. Park’s research goal is to analyze the design and dynamics of biological systems and transform them into robotic/mechatronic systems for human life. Some of the his projects include development of artificial skin sensors, soft Muscle Actuators, and wearable robots for human rehabilitation.

Yong-Lae Park

Yong-Lae Park is an Associate Professor in the Department of Mechanical and Aerospace Engineering at Seoul National University.  Previously, Park was an Assistant Professor at Carnegie Mellon University in the Robotics Institute and the School of Computer Science.  Park received his Doctoral and Master’s Degrees in Mechanical Engineering from Stanford. He earned his Batchelor’s Degree from Kansas State University in Manufacturing Systems Engineering and a Batchelor’s of Engineering in Industrial Engineering at Korea University.

Links

• Soft Robotics and Bionics Lab
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• Download MP3 (8.52 MB)

In this episode, Audrow Nash interviews Juxi Leitner, a Postdoctoral Research Fellow at QUT; and Nicholas Panitz, Ben Wilson, and James Brett, from CSIRO.

Leitner speaks about the Amazon Picking challenge, a challenge to advance the state of robotic grasping, and their robot which won the challenge in 2017. Their robot is similar to a cartesian 3D printer in form and uses either a suction cup or a pinch gripper for grabbing objects. Their robot has a depth camera and uses a digital scale to determine if an object has been picked up successfully. Leitner discusses what their team did differently from other teams that helped them win the competition.

Panitz, Wilson, and Brett speak about their hexapod robots. Their hexapods are for several purposes, such as environmental monitoring and remote inspection. They choose to use hexapods because they are statically stable. They discuss the design of their hexapods and how research works at Commonwealth Scientific and Industrial Research Organization, or CSIRO.

A video of the robot Leitner discusses, #Cartman:

An example of CSIRO’s hexapod robots for inspection:

Links

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In this interview, Audrow Nash speaks with Sangin Park, Senior Research Engineer at Hyundai, about exoskeletons. Park describes three exoskeleton prototypes: one for helping workers reduce back pain, one for assisting a person with paraplegia, and an exoskeleton for soldiers. Park discusses the sensors and actuators of each exoskeleton, as well as Hyundai’s exoskeleton ambitions.

Sangin Park

Sangin Park is a Senior Research Engineer at Hyundai.

Links

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In this interview, Audrow speaks with Andrea Bajcsy and Dylan P. Losey about a method that allows robots to infer a human’s objective through physical interaction. They discuss their approach, the challenges of learning complex tasks, and their experience collaborating between different universities.

Some examples of people working with the more typical impedance control (left) and Bajcsy and Losey’s learning method (right).

To learn more, see this post on Robohub from the Berkeley Artificial Intelligence Research (BAIR) Lab.

Andrea Bajcsy
Andrea Bajcsy is a Ph.D. student in Electrical Engineering and Computer Sciences at the University of California Berkeley. She received her B.S. degree in Computer Science at the University of Maryland and was awarded the NSF Graduate Research Fellowship in 2016. At Berkeley, she works in the Interactive Autonomy and Collaborative Technologies Laboratory researching physical human-robot interaction.

Dylan P. Losey

Dylan P. Losey received the B.S. degree in mechanical engineering from Vanderbilt University, Nashville, TN, USA, in 2014, and the M.S. degree in mechanical engineering from Rice University, Houston, TX, USA, in 2016.

He is currently working towards the Ph.D. degree in mechanical engineering at Rice University, where he has been a member of the Mechatronics and Haptic Interfaces Laboratory since 2014.  In addition, between May and August 2017, he was a visiting scholar in the Interactive Autonomy and Collaborative Technologies Laboratory at the University of California, Berkeley.  He researches physical human-robot interaction; in particular, how robots can learn from and adapt to human corrections.

Mr. Losey received an NSF Graduate Research Fellowship in 2014, and the 2016 IEEE/ASME Transactions on Mechatronics Best Paper Award as a first author.

Links

• InterACT Lab
• MAHL Lab
• Andrea Bajcsy’s website
• Dylan’s Losey’s website
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In this episode, Audrow Nash speaks with Monica Daley about learning from birds about legged locomotion. To do this, Daley analyzes the gaits of guineafowl in various experiments to understand the mechanical principles underlying gaits, such as energetic economy, mechanical limits, and how the birds avoid injury. She then tests her ideas about legged locomotion on legged robots with collaborators, including Jonathan Hurst from Oregon State University. Daley also speaks about her experience with interdisciplinary collaborations. 

Monica Daley

Monica Daley earned an HBSc in Biology with a Chemistry minor at the University of Utah, where she was inspired to pursue an academic career through her research on human locomotor-ventilatory integration with Dennis Bramble and David Carrier. Daley then spent a year as a research technician at the University of Utah, investigating motor control of singing in zebra finches in the lab of Franz Goller. These experiences initiated a long-standing fascination with the interplay of mechanics and neural control.

Daley went on to Harvard University, where she earned her MA and PhD in Organismic and Evolutionary Biology. Her research on muscle-tendon dynamics and biomechanics of avian bipedal locomotion was supported by a prestigious Predoctoral Fellowship award from the Howard Hughes Medical Institute and supervised by Andrew Biewener at the Concord Field Station of Harvard University (CFS Website).

After completing her PhD, Daley was awarded a Research Fellowship by the U.S. National Science Foundation to develop models of the dynamics and control of bipedal locomotion, working with Dan Ferris in the Human Neuromechanics Lab at University of Michigan, in collaboration with Auke Ijspeert in the Biologically Inspired Robotics Group at the Swiss Federal Institute of Technology in Lausanne.

Daley is a faculty member of the Structure and Motion Lab, where she leads research in Comparative Neuromechanics— a field that seeks to understand the interplay of morphology, mechanics and sensorimotor control that influences how animals move through their environment.

Links

• Structure and Motion Lab
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In this episode, Abate interviews Peter Harris from HighRes Biosolutions about automation in the field of drug discovery. At HighRes Biosolutions they are developing modular robotic systems that work alongside scientists to automate laboratory tasks. Because the requirements of each biomedical research laboratory are so varied, the robotic systems are specifically tailored to meet the requirements of each lab.

Peter Harris
Peter Harris is the CEO of HighRes Biosolutions. Prior to HighRes, Peter was VP and Managing Director at Axel Johnson, Inc. He spent most his career as the President & CEO of Cadence, Inc., a high technology medical device manufacturing and engineering firm enabling medical companies to bring better devices to market faster. Peter has been a Visiting Executive Lecturer at the Darden School of Business at the University of Virginia for over 10 years.

Links

• HighRes Biosolutions Website
• “Collaborative robots are redefining lab automation” by Peter Harris
• Peter Harris LinkedIn
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Image: ICRA 2017

In this episode, Audrow Nash interviews several companies at the International Conference for Robotics and Automation (ICRA). ICRA is the IEEE Robotics and Automation Society’s biggest conference and one of the leading international forums for robotics researchers to present their work.

Interviews:

Howard Michel, Chief Technology Officer of UBTECH Education, on a humanoid robot for STEM (Science, Technology, Engineering, and Math) education for a range of ages. (Begins at 1m 20s.)

Li Bingbing, Software Engineer and Cofounder of Transforma in Singapore, on a robot for painting tall buildings. (Begins at 10m 25s.)

Xianbao Chen, Associate Researcher at Shanghai Jiao Tong University, on a hexapod robot for large parts machining. (Begins at 17m 5s.)

Lester Teh Chee Onn, Environmental Engineer at Advisian, on a watercraft for environmental monitoring. (Begins at 27m.)

Links

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In this episode, Audrow Nash interviews Peter Adamczyk, Assistant Professor at the University of Wisconsin-Madison, on semi-active foot and ankle prostheses. The difference is that active below-knee prostheses work to move the person’s weight, emulating the calf muscle, while semi-active devices use small amounts of power to improve the performance of the prosthesis. Adamczyk discusses the motivation for semi-active devices and gives three examples: shiftable shapes, controllable keels, and alignable ankles.

Peter Adamczyk

Peter Adamczyk directs the University of Wisconsin-Madison Biomechatronics, Assistive Devices, Gait Engineering and Rehabilitation Laboratory (UW BADGER Lab) which aims to enhance physical and functional recovery from orthopedic and neurological injury through advanced robotic devices.

“We study the mechanisms by which these injuries impair normal motion and coordination, and target interventions to encourage recovery and/or provide biomechanical assistance. Our work primarily addresses impairments affecting walking, running, and standing. One core focus is advanced semi-active foot prostheses for patients with lower limb amputation. Additional research addresses assessment and rehabilitation of balance impairments, hemiparesis, and other neurologically-based mobility challenges.”

Links

• Download mp3 (19.6 MB)
• UW BADGER Lab
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In this interview, Audrow Nash interviews Marco Hutter, Assistant Professor for Robotic Systems at ETH Zürich, about a quadrupedal robot designed for autonomous operation in challenging environments, called ANYmal. Hutter discusses ANYmal’s design, the ARGOS oil and gas rig inspection challenge, and the advantages and complexities of quadrupedal locomotion. 

Here is a video showing some of the highlights of ANYmal at the ARGOS Challenge.

Here is a video that shows some of the motions ANYmal is capable of.

Marco Hutter

Marco Hutter is assistant professor for Robotic Systems at ETH Zürich since 2015 and Branco Weiss Fellow since 2014. Before this, he was deputy director and group leader in the field of legged robotics at the Autonomous Systems Lab at ETH Zürich. After studying mechanical engineering, he conducted his doctoral degree in robotics at ETH with focus on design, actuation, and control of dynamic legged robotic systems. Beside his commitment within the National Centre of Competence in Research (NCCR) Digital Fabrication since October 2015 Hutter is part of the NCCR robotics and coordinator of several research projects, industrial collaborations, and international competitions (e.g. ARGOS challenge) that target the application of high-mobile autonomous vehicles in challenging environments such as for search and rescue, industrial inspection, or construction operation. His research interests lie in the development of novel machines and actuation concepts together with the underlying control, planning, and optimization algorithms for locomotion and manipulation.

Links

• Download mp3 (11.1 MB)
• Robotic Systems Lab
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In this episode, Audrow Nash interviews Chris Gerdes, Professor of Mechanical Engineering at Stanford University, about designing high-performance autonomous vehicles. The idea is to make vehicles safer, as Gerdes says, he wants to “develop vehicles that could avoid any accident that can be avoided within the laws of physics.”

In this interview, Gerdes discusses developing a model for high-performance control of a vehicle; their autonomous race car, an Audi TTS named ‘Shelley,’ and how its autonomous performance compares to ameteur and professional race car drivers; and an autonomous, drifting Delorean named ‘MARTY.’

Chris Gerdes

Chris Gerdes is a Professor of Mechanical Engineering at Stanford University, Director of the Center for Automotive Research at Stanford (CARS) and Director of the Revs Program at Stanford. His laboratory studies how cars move, how humans drive cars and how to design future cars that work cooperatively with the driver or drive themselves. When not teaching on campus, he can often be found at the racetrack with students, instrumenting historic race cars or trying out their latest prototypes for the future. Vehicles in the lab include X1, an entirely student-built test vehicle, and Shelley, an Audi TT-S capable of turning a competitive lap time around the track without a human driver. Professor Gerdes and his team have been recognized with a number of awards including the Presidential Early Career Award for Scientists and Engineers, the Ralph Teetor award from SAE International and the Rudolf Kalman Award from the American Society of Mechanical Engineers.

Links

• Download mp3 (21.7 MB)
• Dynamic Design Lab
• Center for Automotive Research at Stanford
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In this episode, Audrow Nash interviews Ayanna Howard, Professor at the Georgia Institute of Technology, about her work to help children with the movement disorder cerebral palsy. Howard discusses how robots and tablet can be used to “gamify” pediatric therapy. The idea is that if therapy is fun and engaging children are more likely to do it, and thus, they are more likely to see the long-term benefits of the therapy. Howard discusses how therapy is “gamified,” how a small humanoid robot is used to coach children, and how they work with pediatricians.

Ayanna Howard

Ayanna Howard, Ph.D. is Professor and Linda J. and Mark C. Smith Endowed Chair in Bioengineering in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. She also holds the position of Associate Chair for Faculty Development in ECE. She received her B.S. in Engineering from Brown University, her M.S.E.E. from the University of Southern California, and her Ph.D. in Electrical Engineering from the University of Southern California.

Her area of research is centered around the concept of humanized intelligence, the process of embedding human cognitive capability into the control path of autonomous systems. This work, which addresses issues of autonomous control as well as aspects of interaction with humans and the surrounding environment, has resulted in over 200 peer-reviewed publications in a number of projects – from scientific rover navigation in glacier environments to assistive robots for the home. To date, her unique accomplishments have been highlighted through a number of awards and articles, including highlights in USA Today, Upscale, and TIME Magazine, as well as being named a MIT Technology Review top young innovator and recognized as one of the 23 most powerful women engineers in the world by Business Insider.

In 2013, she also founded Zyrobotics, which is currently licensing technology derived from her research and has released their first suite of therapy and educational products for children with differing needs. From 1993-2005, Dr. Howard was at NASA’s Jet Propulsion Laboratory, California Institute of Technology. She has also served a term as the Associate Director of Research for the Georgia Tech Institute for Robotics and Intelligent Machines and a term as Chair of the multidisciplinary Robotics Ph.D. program at Georgia Tech.

Links

• Download mp3 (21.9 MB)
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In this episode, Audrow Nash interviews Katsu Yamane, Senior Research Scientist at Disney, about robotics in Disney. Yamane discusses Disney’s history with robots, how Disney currently uses Robots, how designing robots at Disney is different than in academia or industry, a realistic robot simulator used by Disney’s animators, and on becoming a Disney Research “Imagineer.”

Katsu Yamane

Katsu received his PhD in mechanical engineering from University of Tokyo in 2002. Following postdoctoral work at Carnegie Mellon University from 2002 to 2003, he was a faculty member at University of Tokyo until he joined Disney Research, Pittsburgh, in October 2008. His main research area is humanoid robot control and motion synthesis, in particular methods involving human motion data and dynamic balancing. He is also interested in developing algorithms for creating character animation. He has always been fascinated by the way humans control their bodies, which led him to the research on biomechanical human modeling and simulation to understand human sensation and motor control.

Links

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A magnetic folding robot arm can grasp and bend thanks to its pattern of origami-inspired folds and a wireless electromagnetic field. Credit: Wyss Institute at Harvard University

The traditional Japanese art of origami transforms a simple sheet of paper into complex, three-dimensional shapes through a very specific pattern of folds, creases, and crimps. Folding robots based on that principle have emerged as an exciting new frontier of robotic design, but generally require onboard batteries or a wired connection to a power source, making them bulkier and clunkier than their paper inspiration and limiting their functionality.

A team of researchers at the Wyss Institute for Biologically Inspired Engineering and the John A. Paulson School of Engineering and Applied Sciences (SEAS) at Harvard University has created battery-free folding robots that are capable of complex, repeatable movements powered and controlled through a wireless magnetic field.

“Like origami, one of the main points of our design is simplicity,” says co-author Je-sung Koh, Ph.D., who conducted the research as a Postdoctoral Fellow at the Wyss Institute and SEAS and is now an Assistant Professor at Ajou University in South Korea. “This system requires only basic, passive electronic components on the robot to deliver an electric current—the structure of the robot itself takes care of the rest.”

The research team’s robots are flat and thin (resembling the paper on which they’re based) plastic tetrahedrons, with the three outer triangles connected to the central triangle by hinges, and a small circuit on the central triangle. Attached to the hinges are coils made of a type of metal called shape-memory alloy (SMA) that can recover its original shape after deformation by being heated to a certain temperature. When the robot’s hinges lie flat, the SMA coils are stretched out in their “deformed” state; when an electric current is passed through the circuit and the coils heat up, they spring back to their original, relaxed state, contracting like tiny muscles and folding the robots’ outer triangles in toward the center. When the current stops, the SMA coils are stretched back out due to the stiffness of the flexure hinge, thus lowering the outer triangles back down.

The power that creates the electrical current needed for the robots’ movement is delivered wirelessly using electromagnetic power transmission, the same technology inside wireless charging pads that recharge the batteries in cell phones and other small electronics. An external coil with its own power source generates a magnetic field, which induces a current in the circuits in the robot, thus heating the SMA coils and inducing folding. In order to control which coils contract, the team built a resonator into each coil unit and tuned it to respond only to a very specific electromagnetic frequency. By changing the frequency of the external magnetic field, they were able to induce each SMA coil to contract independently from the others.

“Not only are our robots’ folding motions repeatable, we can control when and where they happen, which enables more complex movements,” explains lead author Mustafa Boyvat, Ph.D., also a Postdoctoral Fellow at the Wyss Institute and SEAS.

Just like the muscles in the human body, the SMA coils can only contract and relax: it’s the structure of the body of the robot — the origami “joints” — that translates those contractions into specific movements. To demonstrate this capability, the team built a small robotic arm capable of bending to the left and right, as well as opening and closing a gripper around an object. The arm is constructed with a special origami-like pattern to permit it to bend when force is applied, and two SMA coils deliver that force when activated while a third coil pulls the gripper open. By changing the frequency of the magnetic field generated by the external coil, the team was able to control the robot’s bending and gripping motions independently.

There are many applications for this kind of minimalist robotic technology; for example, rather than having an uncomfortable endoscope put down their throat to assist a doctor with surgery, a patient could just swallow a micro-robot that could move around and perform simple tasks, like holding tissue or filming, powered by a coil outside their body. Using a much larger source coil — on the order of yards in diameter — could enable wireless, battery-free communication between multiple “smart” objects in an entire home. The team built a variety of robots — from a quarter-sized flat tetrahedral robot to a hand-sized ship robot made of folded paper — to show that their technology can accommodate a variety of circuit designs and successfully scale for devices large and small. “There is still room for miniaturization. We don’t think we went to the limit of how small these can be, and we’re excited to further develop our designs for biomedical applications,” Boyvat says.

“When people make micro-robots, the question is always asked, ‘How can you put a battery on a robot that small?’ This technology gives a great answer to that question by turning it on its head: you don’t need to put a battery on it, you can power it in a different way,” says corresponding author Rob Wood, Ph.D., a Core Faculty member at the Wyss Institute who co-leads its Bioinspired Robotics Platform and the Charles River Professor of Engineering and Applied Sciences at SEAS.

“Medical devices today are commonly limited by the size of the batteries that power them, whereas these remotely powered origami robots can break through that size barrier and potentially offer entirely new, minimally invasive approaches for medicine and surgery in the future,” says Wyss Founding Director Donald Ingber, who is also the Judah Folkman Professor of Vascular Biology at Harvard Medical School and the Vascular Biology Program at Boston Children’s Hospital, as well as a Professor of Bioengineering at Harvard’s School of Engineering and Applied Sciences.

The research paper was published in Science Robotics.

The Robot Academy is a new learning resource from Professor Peter Corke and the Queensland University of Technology (QUT), the team behind the award-winning Introduction to Robotics and Robotic Vision courses. There are over 200 lessons available, all for free.

The lessons were created in 2015 for the Introduction to Robotics and Robotic Vision courses. We describe our approach to creating the original courses in the article, An Innovative Educational Change: Massive Open Online Courses in Robotics and Robotic Vision. The courses were designed for university undergraduate students but many lessons are suitable for anybody, as you can easily see the difficulty rating for each lesson. Below are lessons from inverse kinematics and robot motion.

You can watch the entire masterclass on the Robot Academy website.

Introduction

In this video lecture, we will learn about inverse kinematics, that is, how to compute the robot’s joint angles given the desired pose of their end-effector and knowledge about the dimensions of its links. We will also learn about how to generate paths that lead to a smooth coordinated motion of the end-effector.

Inverse kinematics for a 2-joint robot arm using geometry

In this lesson, we revisit the simple 2-link planar robot and determine the inverse kinematic function using simple geometry and trigonometry.

Inverse kinematics for a 2-joint robot arm using algebra

You can watch the entire masterclass on the Robot Academy website.

If you liked this article, you may also enjoy:

• Smart Grasping System available on ROS Development Studio
• The Robot Academy: Lessons in image formation and 3D vision
• The Robot Academy: The open online robotics education resource 
• Machine Learning with OpenAI Gym on ROS Development Studio
• SICK LMS full LIDAR teardown
• Envisioning the future of robotics
• How to mount an external removable hard drive on a robot

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

Image: Harvard Gazette

By Peter Reuell, Harvard Staff Writer

At the beginning of the decade, George Whitesides helped rewrite the rules of what a machine could be with the development of biologically inspired “soft robots.” Now he’s poised to rewrite them again, with help from some plastic drinking straws.

Inspired by arthropod insects and spiders, Whitesides and Alex Nemiroski, a former postdoctoral fellow in Whitesides’ Harvard lab, have created a type of semi-soft robot capable of standing and walking. The team also created a robotic water strider capable of pushing itself along the liquid surface. The robots are described in a recently published paper in the journal Soft Robotics.

Unlike earlier generations of soft robots, which could stand and awkwardly walk by inflating air chambers in their bodies, the new robots are designed to be far nimbler. Though real-world applications are still far off, the researchers hope the robots eventually could be used in search operations following natural disasters or in conflict zones.

“If you look around the world, there are a lot of things, like spiders and insects, that are very agile,” said Whitesides, the Woodford L. and Ann A. Flowers University Professor at Harvard. “They can move rapidly, climb on various items, and are able to do things that large, hard robots can’t do because of their weight and form factor. They are among the most versatile organisms on the planet. The question was, how can we build something like that?”

The answer, Nemiroski said, came in the form of your average drinking straw.

“This all started with an observation that George made, that polypropylene tubes have an excellent strength-to-weight ratio. That opened the door to creating something that has more structural support than purely soft robots have,” he said. “That was the building block, and then we took inspiration from arthropods to figure out how to make a joint and how to use the tubes as an exoskeleton. From there it was a question of how far can your imagination go? Once you have a Lego brick, what kind of castle can you build with it?”

What they built, he said, is a surprisingly simple joint.

Whitesides and Nemiroski began by cutting a notch in the straws, allowing them to bend. The scientists then inserted short lengths of tubing which, when inflated, would force the joints to extend. A rubber tendon attached on either side would then cause the joint to retract when the tubing deflated.

Armed with that simple concept, the team built a one-legged robot capable of crawling, and moved up in complexity as they added a second and then a third leg, allowing the robot to stand on its own.

“With every new level of systems complexity, we would have to go back to the original joint and make modifications to make it capable of exerting more force or to be able to support the weight of larger robots,” Nemiroski said. “Eventually, when we graduated to six- or eight-legged arthrobots, making them walk became a challenge from a programming perspective. For example, we looked at the way ants and spiders sequence the motion of their limbs and then tried to figure out whether aspects of these motions were applicable to what we were doing or whether we’d need to develop our own type of walking tailored to these specific types of joints.”

While Nemiroski and colleagues were able to control simple robots by hand, using syringes, they turned to computers to control the sequencing of their limbs as the designs increased in complexity.

“We put together a microcontroller run by Arduino that uses valves and a central compressor,” he said. “That allowed us the freedom to evolve their gait rapidly.”

Though Nemiroski and colleagues were able to replicate ants’ distinctive “triangle” gait using their six-legged robot, duplicating a spider-like gait proved far trickier.

“A spider has the ability to modulate the speed at which it extends and contracts its joints to carefully time which limbs are moving forward or backward at any moment,” Nemiroski said. “But in our case, the joints’ motion is binary due to the simplicity of our valving system. Either you switch the valve to the pressure source to inflate the balloon in the joint, and thus extend the limb, or you switch the valve to atmosphere to deflate the joint and thus retract the limb. So in the case of the eight-legged robot, we had to develop our own gait compatible with the binary motion of our joints. I’m sure it’s not a brand-new gait, but we could not duplicate precisely how a spider moves for this robot.”

Developing a system that can fine-tune the speed of actuation of the legs, Nemiroski said, would be a useful goal for future research, and would require programmable control over the flow rate supplied to each joint.

“We hit that limitation in the system, which I’m actually pretty proud of, because it means we pushed it to its absolute limit,” he said. “We took the basic concept and asked how far can we go before we would have to make radical alterations to how these limbs work, and we found that limit at the eight-legged robot. We were able to make it walk, but if you wanted to make it walk faster, or to add more limbs — for example, to support a load — you would have to start rethinking the system from the ground up.”

Though it may be years before the robots find their way into real-world applications, Whitesides believes the techniques used in their development — particularly the use of everyday, off-the-shelf materials — can point the way toward future innovations.

“I don’t see any reason to reinvent wheels,” he said. “If you look at drinking straws, they can make them at, effectively, zero cost and with great strength, so why not use them? These are academic prototypes, so they’re very light weight, but it would be fairly easy to imagine building these with a lightweight structural polymer that could hold a substantial weight.”

“What’s really attractive here is the simplicity,” added Nemiroski. “This is something George has been championing for some time, and something I grew to appreciate deeply while I was in his lab. For all the complexity of movement and structural integrity we get out of these robots, they’re remarkably simple in terms of construction and control. Using a single, easy-to-find material and a single concept for an actuator, we could achieve complex, multidimensional motion.”

This post was originally published on The Harvard Gazette. Click here to view the original.

This research was supported with funding from the U.S. Department of Energy, DARPA, the Natural Sciences and Engineering Research Council of Canada, the National Science Foundation, the Swedish Research Council, and the Wyss Institute for Biologically Inspired Engineering at Harvard University.

Credit: sk.ru

In this episode, Audrow Nash and Christina Brester conduct interviews at the 2016 International Association of Science Parks and Areas of Innovation conference in Moscow, Russia. They speak with Vadim Kotenev of Rehabot and Motorica about prosthetic hands and rehabilatative devices; and Vagan Martirosyan, CEO of TryFit, a company that uses robotic sensors to help people find shoes that fit them well.

An image of one of the rehabilitative devices from Rehabot.

The robotic platform that scans your feet from TryFit.

Links

• Download mp3 (13.3 MB)
• IASP 2016 conference website
• Skolkovo’s robotics page
• Rehabot’s website
• TryFit’s website
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS

A researcher at the University of the West of England (UWE Bristol) is developing a bio-inspired ‘smart’ knee joint for prosthetic lower limbs. Dr Appolinaire Etoundi, based at Bristol Robotics Laboratory, is leading the research and will analyse the functions, features and mechanisms of the human knee in order to translate this information into a new bio-inspired procedure for designing prosthetics.

Dr Etoundi gained his PhD in bio-inspired technologies from the University of Bristol where he developed a design procedure for humanoid robotic knee joints.  He is now turning his attention to nature, a growing area in robotics known as Bio-mimicry, combining curiosity about how biological systems work with solving complex engineering problems, in order to develop a prototype smart knee joint for prosthetics.

Andy Lewis, a Paralympic Triathlon Gold Medallist (Rio 2016), who wears a lower limb prosthetic, will try out the new joint once developed, to compare its energy consumption and gait efficiency to current prosthetics. There are currently approximately 100,000 knee replacement operations performed every year in the UK.  Lower limb amputation has a profound effect on daily life, and prosthesis must be comfortable and adapted to people so they can maintain daily activities such as walking and running.

Looking for inspiration in nature, Dr Etoundi will examine how the human knee works, as well as looking closely at the design of knee replacements used in surgery and at current knee joints in prosthetic limbs.  These three areas of knowledge will inform a procedure for designing a knee that could give greater, more responsive movement, while offering the control and intelligence that comes from robotics.

Dr Etoundi says, “I have spent years designing knee joints for humanoid robots, but the human knee has evolved over millions of years and is incredibly successful.  The human knee is a very complex joint with ligaments, which guide the motion of the knee, and bones that perform the motion.  Current mechanisms in prosthetic knees have a straightforward pin joint with ball bearings that does not have the sophisticated range of motion and stability of the human knee with its cruciate ligaments.

“The complex interaction between the soft tissue (ligaments) and the bones in the knee joint is an area that has yet to be replicated in prosthetics.  We need to understand this better in order to provide a better knee joint for people to use. I will study the different mechanisms within the knee joint and look for ways to translate its beneficial functionalities into a design concept for prosthetics.

“I want to create a prosthetic knee that will give the greatest range of motion with the least friction, enabling walking, climbing stairs, squatting and stability, while also offering important attributes of current prosthetics and the benefits of robotic technology.”

Andy Lewis, who will try out Dr Etoundi’s nature-inspired design says, “I was pleased when Appo approached me. He understands the importance of a good prosthetic for sports people, and it will be interesting to see what he discovers that might make a better prosthetic which is more responsive.  I am looking forward to seeing his early designs next year and trying them out.”

The research team includes Professor Richie Gill (University of Bath), Dr Ravi Vaidyanathan (Imperial College London) and Dr Michael Whitehouse (University of Bristol).

Dr Etoundi is a Senior Lecturer in Mechatronics at UWE Bristol and is a member of the Medical Robotics group at Bristol Robotics Laboratory, which looks at the application of robotic technology in human-controlled and surgical applications.

In this episode, Audrow Nash and Christina Brester conduct interviews at the 2016 International Association of Science Parks and Areas of Innovation conference in Moscow, Russia. They speak with Roman Luchin, CEO of CyberTech Labs., about a robotics development platform called Trik. Trik is intended to be an intermediate step when learning about robotics between Lego Mindstorms and programming on an embedded platform. Trik allows users to program with a graphical interface by ordering blocks. These blocks contain code in several common programming languages (python, F#, Pascal, etc.) and the code can be modified directly.

This is the second of three interviews from the conference.

Roman Luchin

Roman Luchin is Founder of TRIK and CEO of CyberTech Labs in Russia. He started as a researcher in a laboratory of theoretical cybernetics in Saint Petersburg State University, where he developed multi-agent control systems. Faced with two challenges in Russian robotics, young professionals and outdated approaches, he decided to devote himself to the improvement of STEM education through robotics and the creation of new educational tools. As a result, he developed a unified cybernetic platform for education, research, and robotics projects called TRIK. There is a continuously growing community around TRIK, including universities, researchers, prototyping labs, and creative teams working to provide everyone with the opportunity to create modern intelligent robots that can “see”, “hear” and collaborate.

Links

• Download mp3 (16.5 MB)
• IASP 2016 conference website
• Skolkovo’s robotics page
• Trik’s homepage
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS

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.

Would you like to make a robot to grasp something, but you think that is impossible to you just because you can’t buy a robot arm? I’m here to tell that you can definitely achieve this without buying a real robot. Let’s see how:

How is that possible?

The Smart Grasping Sandbox built by Shadow Robotics is now available for everybody on the ROS Development Studio – a system that allows you to create and test your robot programs through simulations using only a web browser.

Wait, what is the Smart Grasping Sandbox?

The Smart Grasping Sandbox is a public simulation for the Shadow’s Smart Grasping System with theUR10 robot from Universal Robots. It allows you to make a robot to grasp something without having to learn everything related to Machine Learning, and being available on the ROS Development Studio, it allows you to test it without the hassle of installing all the requirements.

As Ugo Cupcic said:

I don’t want to have to specify every aspect of a problem — I’d rather the system learn the best way to approach a given problem itself.

Using the Development Environment for Grasping

In order to user the Smart Grasping Sandbox on ROS Development Studio—also known as RDS—just go to http://rds.theconstructsim.com and Sign In. Once logged in, go to the Public Simulations, select the Smart Grasping Sandbox simulation and press the red Launch this simulation button.

After pressing the Launch this simulation, a new screen will appears asking you to select the launch file. Just keep the default main.launch and press the red run button.

After a few seconds, the simulation will be loaded and you will be able interact with the simulation.

Once the simulation is loaded, you can see the instructions about how to control it on the left side.

In the center you have the simulation itself, and on the right side you can see the Integrated Development Environment and the Web Shell that allows you to modify the code and send Linuxcommands to the simulation respectively.

You can modify, the simulation, the control programs or even create your own manipulation control programs, make the grasping system learn using deep learning or else. This is up to you as developer!

You can also see what the robot sees using the online ROS 3D Visualizer (RViz) typing the following command on the Web Shell:

$ rosrun rviz rviz

After running Rviz, you press the red Open ROS Graphic Tools button on the bottom left side of the system and a new tab should appear with Rviz.

Conclusion

Here we have seen how to use a simulation to programme a robot to grasp something.

The simulation uses ROS as the middleware to control the robot.

If you want to learn ROS or master your ROS Skills, we recommend you to give a try to Robot Ignite Academy.

If you are a robot builder and want to have your own simulation available for everybody on RDS, or you want to have a course specifically for your robot, contact us through the email info@theconstructsim.com.

Hi, I’m Ben. I was a member of the team that developed a new walking mechanism, TrotBot, that we eventually scaled up to the size of a mini-van (you can read my original post here). Now, at DIYwalkers, I’ve posted plans for TrotBot Ver. 2, designed to handle the weight of the EV3 brick. As you can see in the following videos, we were able to improve TrotBot by borrowing some ideas from a galloping horse.

Just like lunges are more tiring by simply walking, robots with bumpy gaits require more power to walk. This may not be a problem at small scales, but as a robot’s weight increases, smoother gaits are required.  TrotBot Ver. 1 has somewhat of a bumpy gait, but as you can see in my video below, TrotBot’s weight at LEGO-scale is low enough that it walks well:

However, when I added LEGO’s relatively heavy EV3 brick to TrotBot it didn’t perform so well.  To reduce its power requirements, I needed to smooth TrotBot’s gait, which I did by adding active feet that mimic the leg action of a galloping horse.

Background on TrotBot and its Feet

Our goal for TrotBot was to create a walking mechanism capable of walking on rough terrain and areas generally inaccessible to wheeled vehicles, so we designed its linkage to step high. We initially prototyped TrotBot in LEGO with 8 legs.  It walked well enough that we—somewhat naively—thought we could scale it up to mini-van size with only 8 legs.  A team of us spent most of that summer’s boiling hot weekends building our large TrotBot in my garage, only to find, during our first walking test, that too much torque was required to drive the robot up from the gait’s low point.  As we discovered, large walkers should always have at least one foot in contact with the ground at each corner, like how a car’s four wheels are always in contact with the ground. Below, I simulated one corner of a 12 leg version of TrotBot, and as you can see we should have scaled up TrotBot with 12 legs like Theo Jansen’s famous Strandbeest has:

It would have required too many new parts to switch to a 12 leg version of TrotBot, and we didn’t want to start over from scratch, so instead we explored ideas for active feet that could smooth TrotBot’s 8 legged gait.

Using a galloping horse’s gait as inspiration we sought to add some sort of second foot to each leg that would mimic how a horse’s rear then front legs land in pairs. This resulted in the additional linkage that we call TrotBot’s “heel” which increased TrotBot’s foot-contact with the ground by about 10%, reduced how much the feet skidded, and increased the step-height of TrotBot’s rear legs. Shown below is a video comparing TrotBot with these “heels” to a galloping horse:

Next, we explored adding some sort of toe that would push down on the ground as the foot begins to lift, just like how humans use their toes to walk. We installed one of these toe ideas on our large TrotBot but they didn’t smooth the gait enough, and since they were attached to the legs at a fixed angle they tended to catch on obstacles. Catching on obstacles occasionally caused the linkage to lock and gears to grind. In other words, this toe compromised our main goal of creating a mechanism that could walk on rough terrain! Here’s a photo of that toe:

TrotBot’s fixed angle toe

Looking again to a galloping horse for inspiration we started to experiment with linkages that mimicked how horses paw their hooves backward and then keep them folded back as they lift their legs to strike the ground again. We discovered a few options that mimicked this action, and they smoothed the gait by increasing TrotBot’s foot-contact by another 10% while maintaining its high foot-path. I added one of these toe options to TrotBot Ver. 2, and while I couldn’t make a toe with accurate dimensions using LEGO’s integer-based beams, it still smoothes TrotBot’s gait significantly:

Click here for instructions on how to build your own TrotBot Ver. 2!

A few decades ago, touchscreens were impressive, yet clunky pieces of technology reserved for applications that did little more than show off that touchscreens were possible. Today’s touchscreens are commonplace and readily accepted as an easy way to interact with an operating system; they’re accurate, support multi-touch, are comfortable to use, and can even operate in multiple dimensions (pressing hard).

We may not have perfected touchscreens yet, but we’re getting close. Accordingly, engineers and researchers are already speculating about the next phase of UI development, especially for robotics control. So far, the leading candidate is gesture-based control—the use of physical gestures to relay commands.

The idea

The major limitation for touchscreens is the fact that they operate only in two dimensions; the third dimension introduced with force touch is extremely limited. Comparatively, hand gestures and physical movements can operate in three dimensions, and depending on how they’re designed, could feel more natural than swiping and tapping a smartphone screen.

Demand for three-dimensional gestural control is increasing with the onset of virtual reality (VR) and augmented reality (AR) technology; because the digital world we experience will be moving from two dimensions to three dimensions, the controls we use to manage those experiences will also need to change. With enough sophistication, these systems could provide better feedback to the users in control; rather than merely responding with visual feedback like movement or lights, users could feasibly be immersed with physical feedback like vibration or resistance.

Where we stand

Currently, one of the most advanced gestural systems is the Real-Time 3D Gesture Analysis for Natural Interaction with Smart Devices, a project headed by researchers in Linnaeus University in Sweden. However, simpler forms of gesture-based control are already available.

For example, there are projects that use Arduino to create a robot that can respond to four different simple hand gestures (plus a neutral position). Of course, iPhone technology also makes use of some simple “gestures,” such as shaking the phone to undo typing or rotating the phone to experience an immersive 360-degree view of an image or video.

The main obstacles

There are a few obstacles preventing gestures from being solidified as the next landmark in robotics control, however:

• Gesture complexity. The biggest obstacle described by researchers at Linnaeus University is the sheer complexity of physical gestures; to be effective, their recognition systems need to be able to gather and recognize thousands of tiny data points and interpret complex patterns to “understand” what movement is being attempted. Moreover, this big data-based interpretation needs to happen in real-time—especially for applications like live robotics control, or engagement in a VR-based video game. That demands not only an incredibly intelligent system, but a processor that can operate quickly.
• Accessibility. Gesture recognition systems would likely be developed to accommodate a “standard” human model, such as a “standard” right human hand. How would the system accommodate somebody whose hand was missing, or who is missing a few fingers? What about left-handed people? And people with Parkinson’s disease, or who are unable to operate their hands with precision control?
• Applications. For gestures to be accepted as a mainstream way to interact with robotics and virtual environments, robotics and virtual environments need to be available. Virtual reality (VR) technology has been advancing strongly for the past several years and is poised to take a big step forward in user adoption by 2020, but until those user adoption numbers are reliable, the demand for gestural systems is relatively low. Using hand movements and other physical gestures to control a two-dimensional screen, for example, would be largely ineffective.

On paper, gestures seem like the best form of control for the digital devices, robotics, and VR systems of the future. However, there are still many obstacles to overcome before we’re ready for large-scale adoption. Fortunately, researchers are ahead of the curve, already preparing the intelligent gesture-based recognition programs we’ll need when touchscreens become obsolete.

Six to ten years ago, exhibitors at Automate were promoting bin-picking in many, many booths. Bin picking wasn’t mentioned this year because it is an available option these days. For the last six years vendors have been promoting human-robot collaboration in manufacturing. Here’s what I saw this year at the big Automate and ProMat trade shows held last month in Chicago.

Chicago’s huge McCormick Place Conference Center was once again home to the Automate and ProMat trade shows: Automate for robotics; ProMat for material handling solutions. Over 900 exhibitors covered 350,000 sq ft at ProMat while 400+ companies exhibited at the Automate show.

Vendors appeared to be in the process of defining a broader definition of collaboration which includes all non-industrial robotics (mobile or stationary; manufacturing or service; professional or personal) in any way involving human-robot interaction. The interaction could be as little as different colored lights or as much as direct handling of materials back and forth to robots side-by-side on an assembly line.

Universal Robots (UR), the collaborative robot pioneer, has sold over 12,000 of their co-bots worldwide in their short career (they sold 52 in 2009). 2016 sales were 60% greater than 2015 and their goal for 2017 is 80% to 100% over 2016. UR’s booth, the biggest at Automate, was consistently crowded and noisy. Lots of attentive interest.

Recently UR added an app store for co-bot accessories, grippers and cameras. They presently offer 20 products but expect to have an additional 80 in the store by the end of 2017.

Another sign of UR’s success was the use of their robots in other vendor’s booths – 25 different exhibitors demonstrated their products with UR robots. Intelligrated used a UR to demonstrate their goods-to-picker application; Creaform used one equipped with their HandySCAN to demonstrate 3D scanning and inspection applications; ATI Industrial Automation used another UR outfitted with ATI’s multi-axis force sensor; Schunk used a UR to demonstrate their new CoAct EGP gripper (read more about this interesting gripper below) as did Robotiq, which used 3 UR’s to demonstrate their grippers, wrist cameras and force torque sensors.

Up until this year UR has had few active competitors. Most seemed to be waiting to see whether there’s a real market for these types of smaller, slower, less robust robots. They weren’t actively marketing their co-bot products.

• ABB and their two-armed YuMi (2-3 times as expensive as a UR)
• Fanuc’s CR-35iA, a green (for safe) soft-covered co-bot that can handle payloads of up to 75 pounds
• Kawada’s two-armed Nextage
• pi4 Robotics‘ Workerbots
• Kuka’s LBR iiwa which recently lowered its price to $60k
• Precise Automation’s SCARA and Cartesian desktop co-bots
• Rethink Robotics Baxter and Sawyer

UR and Rethink proved beyond doubt that there’s a willing marketplace for effective low-cost co-bot products, and the other companies are planning to take advantage of their many years of education and promotion. 2017 is the year many of the above companies – and many more new entrants – are beginning to seriously market into the industrial space. Here are some of the new co-bot entrants:

• The Aubo i5 (from the company that used to be called Smokie) looks like and has almost identical specs when compared to a UR5 except that it’s price is $10,000 less expensive and it has an open software system with ROS support.
• ABB plans to release a renamed version of the popular one-armed gomTec Roberta robot which was pulled from the market when ABB acquired gomTec.
• Bosch’s APAS which, although used internally for years is just beginning to be commercially marketed.
• Schunk’s stylish blue Powerball arm has been highlighted at every trade show so far this year.
• Denso is releasing their tiny desktop co-bot Cobotta.
• Kawasaki Robot is launching their duAro dual-arm SCARA co-bot and was using that co-bot to cone up and hand out soft-serve ice cream.
• Comau is launching their slick Racer 3 and 5 arms using their optional AURA add-on safety system.
• The highly anticipated Franka Emika $12,150 package of arm, gripper, teaching device and graphical software is already being delivered to academic clients. Commercial deliveries are expected shortly after this year’s Hannover Messe trade show.
• Staubli is launching their TX2 series of robot arms made safe by the optional use of their CS9 safety control system.
• And many more (read this article: 42 companies empowering robots and humans to work side-by-side).

Collaborative robots need safe, sensitive and flexible end-of-arm-tooling (EOAT) and there were many vendors – and some breakthrough technologies – demonstrated at the two shows. Details here: Smart grasping demonstrated at Automate and ProMat trade shows.

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/

Back pain is one of the leading causes of work absenteeism in the UK, with 8.8 million days lost to work-related muscoskeletal disorders per year. On average, each case causes 16 days of absenteeism, and chronic conditions can cause some absences to become permanent.

But working in a bent forward, back straining posture is unavoidable in a great many professions, like in hospital, agricultural and warehouses environments to name but a few. This regular exposure to demanding postures increases the risk of debilitating pain, which can severely reduce productivity and moral in the workforce.

The Laevo Exoskeleton aims to alleviate this problem. The Laevo is a unique, wearable back-support that aids users working in a bent forward posture or lifting objects. The wearable frame carries part of the upper body weight of the user, thereby decreasing the strain on the lower back and improves the long-term employability of employees.

Video 1: The product

Video 2: See it in action

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

The NAIST OpenHand M2S was developed by a team of students as part of the school’s annual CICP project (read the blog post about it here), in which students can propose and organize their own research projects. Based on the Yale OpenHand M2, the NAIST OpenHand M2S was developed for textile manipulation, sensitive grasping as well as pinpoint pushing with high loads. All of the parts (except motors, sensors etc.) can be downloaded from here and 3D printed.

For grasping and manipulation, the hand is equipped with two 3D force sensors that act as fingertips. Using these, the hand can gently grasp textiles and let them glide through its fingers, pull them taut when tension is required, and even recognize different materials. By rubbing its fingertips together, a force signal is generated which allows the hand to sense if it successfully grasped a textile, and which kind of material it is.

Lastly, the rigid finger of the hand allows it to push with high loads and tuck into small openings. This can be used for tasks like making a bed, but also during the manufacturing of a car or airplane seat, which is part of the team’s research. Future versions of the hand will be able to pick up thin objects from a table easily without moving the robot.

You can read the conference paper here. Download the 3D files here, to use the gripper in your own research.

If you liked this article, you may also enjoy these:

• Robotics, maths, python: A fledgling computer scientist’s guide to inverse kinematics
• Python programming your NAO robot
• Soft robotic gripper can pick up and identify wide array of objects
• Sticky business: Five adhesives tested for 3D printing
• 3-D printing hydraulically-powered robots, no assembly required

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

In this episode, Abate De Mey interviews two speakers from the Agricultural track of the RoboUniverse 2016 conference in San Diego: Dan Harburg of Soft Robotics Inc. and Matthew Borzage of BioTac. Borzage and Harburg discuss their distinct approaches to advancing gripping technology in Agriculture. Borzage stresses the importance of tactile sensing while Harburg pushes for low cost, soft grippers with no on-board sensors.

Dan Harburg
Dan Harburg is the Director of Business Development at Soft Robotics Inc., a venture-backed startup company developing a soft end-of-arm tool for the food industry. Harburg was previously an entrepreneur-in-residence at North Bridge Venture Partners, working with scientists at Harvard, MIT, and the University of Illinois to commercialize technologies at the intersection of materials, medicine, and advanced manufacturing.

Matthew Borzage
Matthew Borzage is a co-founder and the Head of Business Development for SynTouch. Borzage has a background in biomedical and medical device engineering and sales. As Head of Business Development, Matt is interested in how tactile sensing can be used in robotics and prosthesis. 

Links

• Soft Robotics Inc.
• Bakery Automation with a Soft Touch
• SynTouch Inc.
• Biomimetic Design of BioTac
• Download Episode (16.2 MB)

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
• Bioinspired robotics #3: Wearables, with Conor Walsh
• Robots Podcast #216: ExoAtlet: Exoskeleton for Rehabilitation, with Ekaterina Bereziy
• Exoskeletons: From helping people walk to controlling robots in space
• Are skintight suits the future of robotic exoskeletons?

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

The Arm

The design I used is based on industrial pallet-packing robots, and at its core has three degrees of freedom, or ‘axes’ on which it can move. Think left to right, in and out and up and down; it basically means that the arm can move in three different ways. The GIFs below show the different types of movement: the entire arm can swivel on its base, the “main arm” translates the elbow in and out from the centre, and the “actuator” drives the forearm, which in turn translates the end-effector up and down. A 3-DOF arm like this has the handy property of having a unique solution for each possible position of its end-effector, which made the IK task approachable to someone like me, who’s never done this type of thing before.

Swing

Main

Actuator

Like many well-studied problems, IK has generic solutions, but these are numerically-based and computationally complex. I wanted a simple and efficient solution, so I decided to approach the control of my arm analytically, using geometric principals and logic. The result of this work was a graphical Python application, which connected to the arm via serial and allowed the user to move the goal position of the end-effector by clicking and dragging, or by sending commands from another application running on the same computer (this IK application made up a key part of my project, but that’s a topic for another time).

Here’s a quick look at what I ended up with:

Heads up: The code in this post is written in Python with Numpy, and there’s maths. I won’t pretend that any of the ideas presented here are optimal (or good, for that matter), but hopefully you’ll be able to scrounge something useful!

Let’s go already

To start with, let’s consider the “swing” action of the arm, as it rotates on its base. Since the main arm, elbow and forearm are all constrained to a single plane (look at the GIFs above: only the swing rotation affects the left-right position of the wrist), all we really have to do here is line up that plane – a line from our top-down perspective – with the goal point, ignoring the goal’s vertical position. If this sounds easy, you’re right: it’s plain old trigonometry. Unfortunately for me, a quirk of my design left the arm’s operation plane just off-centre of the axis of rotation, which made the solution much less obvious.

Using the naive trigonometric approach here results in pointing off to the left of the actual goal (right if the shoulder sits on the other side), and the gap is big enough that this is a real problem. I took a shot at solving this geometrically, but quickly decided it wasn’t worth my time figuring it out. In the end, I resorted to an extremely simple numerical technique called a binary search. I’ve used this in the past for other tough geometric problems, like finding an intersection between a Bezier curve and a circle.

In a binary search (also known as the “Bisection Method”), you essentially come up with arbitrary upper and lower bounds for your variable – in this case, theta – and then examine the midpoint to find whether it’s larger or smaller than your target. If it’s larger, then the midpoint becomes your new upper bound; if it’s smaller, the midpoint is your new lower bound. Repeat until you’ve bounded your variable with sufficient accuracy. A binary search is conceptually simple and computationally cheap, and converges fairly quickly: you halve your search space each iteration. I found 3-5 iterations got me within 0.3 degrees of the true value, well within the limits of what my servo motors could practically target.

On to the solution! What we’re actually searching for is the point at which the vector representing our arm is lined up with the vector from the arm’s shoulder joint to the goal: where the angle between these two vectors is zero. Normally, when taking the angle between two vectors (call them A and B) you get the absolute angle out, with no directionality. However, with this search method it’s important to have a signed distinction between “too high” and “too low”. To get around this, I wrote a simple function to find a signed angle between two vectors, with negative showing that A points counter-clockwise to B, and positive showing that A points clockwise from B:

This works by taking a perpendicular vector from B (pointing clockwise), and dot-ing it with A to see how much they line up (using the dot product for
vector projection).

If A points clockwise of B then np.dot(A, perp) will be positive, and vice-versa.

Note that the normalize() function simply sets a vector’s length to 1, while keeping it pointed in the same direction. Mathematically, it’s just doing: A=|A|^-1 (np.clip() just keeps the dot product between 1 and -1).

With that, we can now perform our binary search and find a decent approximation to the target angle. We need a reasonable starting point, so let’s just use the angle from the origin to the goal (the naive approach I mentioned above). Numpy provides a very useful function for finding the angle np.arctan2(y, x) which resolves the arc-tangent of in the correct quadrant.

Now, it’s important to check which side the shoulder sits on: this lets us decide whether to use start_theta as an upper or lower bound. This is as simple as testing the signed angle between the shoulder offset vector (from the origin) and a vector pointing straight forwards. At the same time, we can also establish the other bound by taking the absolute value of this angle and adding or subtracting it from start_theta, since this angle to the shoulder is always going to be greater than the error of start_theta.

With our initial bounds established, the actual search loop is pretty straightforward. Note the use of an iteration counter: it’s only there in case something goes wrong (e.g. if we input an unsolvable configuration), since in normal operation the loop exits once the desired accuracy is reached. We iteratively update self.swing, which will hold the new target swing angle at the end of the search.

Note that the self.shoulder(angle) function just generates the top-down (x, z) position of the shoulder joint for a given swing angle. Besides the swing angle, there’s one other important result from this stage of IK: the radial distance, from the shoulder joint to the end-effector. The calculation is simple enough:

goal_radial = np.linalg.norm(self.goaltd – self.shoulder(self.swing))

Round in circles

We know which way to point: now, let’s consider the movement of the arm in its ‘plane of operation’, which covers the goal end-effector position and the line extending vertically (+y) from the shoulder joint. The main arm and forearm both move across this plane. The trick here is that we’ll still be using a 2D coordinate system, basically a local frame of reference, where our y value remains the same but the x value is the radial distance of the point we’re interested in. For the goal, we computed this radial distance above in the top-down IK, so we can go ahead and construct goalpl – the ‘planar’ goal – which lies at: goalpl=(goal_radial/goal_y).

Side-on, the main arm and forearm form an upright triangle between the shoulder joint and goalpl with the elbow joint sitting at the peak. It’s important to note that the arm’s sections are different lengths, with the forearm being a bit longer. Now, in this plane we’re solving given the end effector’s position, and the origin is static. That just leaves the position of the elbow joint to solve for, which we can do geometrically, since it’s at a fixed distance from both the origin and effector. If we draw two circles, one fixed about the origin with radius = main arm length, and one centred on the end-effector with radius = forearm length, then the elbow must be at one of the two possible intersections between these circles:

Let’s look at a simple Python class which deals with this intersection calculation. There’s a few possible cases for circle-circle intersection, most of which invalidate our solver: if the circles do not contact, either by being separated or in the case that one contains another, then there is no valid solution; if the circles are coincident (impossible for this design, since the radii differ) then there are infinite possible solutions; however, when the circle borders intersect, we can find two distinct intersection points.

For any case we’re interested in, it’s the top intersection that matters. This means we can just pick the one with the largest y value, and call it a day:

And – that’s it! We have the swing angle, and a valid position for the elbow joint to match the shoulder and goal end-effector positions. That’s all we need to define a valid pose for the arm. If you need to, you can reconstruct the positions in 3D space:

Wrapping up

If you’ve made it this far, I hope you’ve found this interesting and/or enlightening. Want to see how it all fits together? I’ll be uploading the full IK application to GitHub here, along with all the design files for the robotic arm. If you’re feeling ambitious, you should be able to 3D-print and build your own to play around with

Other notes:

• I didn’t design the arm completely from scratch: check out the original “LiteArm” design on Thingiverse.
• The IK application uses PyGame, a no-frills SDL wrapper for Python, to open and draw to the graphical window. I’ll probably switch to a meatier library for further development, but this did the job of basic “drawn-to-screen” stuff nicely.

Written by Alistair Wick for the University of Bristol Engineering Maths blog. The original post, and future follow-ups, are available on Alistair’s website.

If you liked this article, you may also enjoy these:

• TrotBot tackles rough terrain
• Sticky business: Five adhesives tested for 3D printing
• Meet RoboGabby: She’s on a mission to teach more girls about robotics
• Adjust how you learn and quickly pick up robotics programming
• 3-D printing hydraulically-powered robots, no assembly required

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

In this episode, Audrow Nash interviews Domenico Prattichizzo, Professor of Robotics at the University of Siena and Senior Scientist at the Istituto Italiano di Tecnologia in Genova in Italy, about a device for assisting people who have lost the use of one of their hands, for example by a stroke. The device is an extra finger that functions to press an object into the paralyzed hand so that it can be grasped.

A video of the device being used for various tasks is below:

https://www.youtube.com/watch?v=spAXDXLCsKw&list=PLfYZRHo5ca5asEeY8b66xJ1ZuX3SFlCpR&index=1

Domenico Prattichizzo

Since 2015, Prattichizzo has been a Full Professor at the University of Siena. From 2002 to 2015, he was an Associate Professor of Robotics at the University of Siena. Since 2009, Prattichizzo has been a Scientific Consultant at Istituto Italiano di Tecnoloogia, in Genova, Italy. In 1994, he was a Visiting Scientist at the MIT AI Lab. Prattichizzo  received his M.S. degree in Electronics Engineering and the Ph.D. degree in Robotics and Automation from the University of Pisa in 1991 and 1995, respectively.

Links

• The Sixth Finger project page
• Download mp3 (15.3 MB)
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS

In this episode, Abate De Mey interviews Edward Neff, founder of SMAC Corporation. Mr. Neff discusses how breakthroughs in his company have allowed them to develop linear actuators compact enough to be used to actuate robotic fingers. Companies like Apple and Samsung push for the development of robotic fingers to perform lifelike tests on their phones.

The robotic fingers developed using SMAC’s linear servo motors simulate a button press on a screen by employing a strong downwards force, as well as small sideways forces tangential to the screen. This mimics a realistic way that people interact with their touchscreens. The robotic fingers can also gently bump into surfaces, pull, and push objects. Other applications for these fingers include electronic circuit board assembly, and screwing bottle caps onto bottles.

Edward Neff

Edward Neff grew up in Birmingham, Michigan (USA) and is a graduate of the University of Michigan. He has a career history of managing automation companies in Belgium and Japan. Mr. Neff is the founder of SMAC Corporation in Carlsbad, California. SMAC is a leading manufacturer of precision programmable electric actuators. The company is planning a 2018 IPO. Mr Neff has over 40 patents in robotics and serves as a Director in BioTech and Medical Device companies.

Links

• SMAC Website
• Robotic Finger using a SmartPhone (YouTube)
• Robotic Finger at the Tokyo Robot Show (YouTube)
• Robotic Finger VS Human (YouTube)
• Smart Assembly Video (YouTube)
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS

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.

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

• Virtual race: Competing in Brain Computer Interface at Cybathlon
• Brain Computer Interface used to control the movement and actions of an android robot
• Can a brain-computer interface convert your thoughts to text?

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

In this episode, Audrow Nash interviews Hasier Larrea, Founder and CEO of Ori Systems and MSc candidate at the MIT Media Lab, about robotics used to transform personal spaces. Larrea discusses how the world is urbanizing and how new space paradigms are needed to accommodate this shift. He proposes robotic furniture that allows for what is not being used to be hidden, such as a desk or a bed. Larrea discusses the robotic systems, how these systems will be integrated into existing infrastructure, and the future or Ori Systems.

Below is Larrea’s TEDxCambridge talk:

Hasier Larrea
Hasier Larrea is an engineer and designer at the MIT Media Lab. He holds a Bachelors and Masters Degree in Mechanical Engineering. Hasier leads the Architectural Robotics research area at the Media Lab, which focuses on creating a new generation of hyper efficient and responsive urban spaces. His team is developing the robotic tools that allow traditional architectural and furniture elements to transform and intelligently connect to the world around us. His work on the CityHome and CityOffice has been featured in The New York Times, Time, Fast Company, Wired, and TechCrunch among others. His creative and entrepreneurial interests help translate the Lab’s envisioning, inventing, and designing approach into manifesting the ideal smart city from researchers’ minds to the streets of tomorrow.

Links

• Download mp3 (15.4 MB)
• Ori Systems’s homepage
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS

By Jurjen Slump.

Students of Delft University of Technology have developed a new add-on for a 3D printer that can cast silicones inside a 3D printed shell during the printing process. This new, and cheap, technique can be used to create new soft-robotic products that were previously impossible to make. The team presented their findings yesterday, at the science fair that marked the end of the minor Advanced Prototyping of the faculty Industrial Design Engineering.

The technique they developed is called UltiCast. “It is a combination of moulding and 3D printing”, explains Max Nobel, student Industrial Design Engineering. “The machine makes a plastic shell, which is filled with 2-components silicones at the same time.” The shell is made of a plastic (PVA) which dissolves in water. The team uses a Ultimaker 3D printer, which they completely rebuilt.

UltiCasting will allow more complex shapes that could not be printed on a ‘traditional’ direct drive 3D printer. If the mould quality is high, the surface quality of the flexible silicon will also be very high. This is very hard to achieve in traditional fused deposition modelling (FDM) printing, because every layer of hot filament deforms the previous layers of filament.

Soft robotics
The new technique proves very interesting for soft robotics. With UltiCasting it becomes possible to ‘print’ a soft actuator in a mould, thereby elimination the manual casting process and doing so speeding up the process of creating soft actuators. “You get a lot of freedom to personalise the behaviour of robots with geometry and materials”, tells team member Rob Scharff, a PhD student in soft robotics.

“You get a lot of freedom to personalise the behaviour of robots with geometry and materials”

It is mechanical intelligence. “You make sure that the robot behaves exactly as you want to.” For example, you can make a robot hand with flexible fingers and a thumb that has enough strength to pick up a vegetable. “With 3D printing, you can fabricate a custom-made gripper.” Besides, it has the potential to mix hard materials – as another team proved – making it possible to print a skeleton inside silicone. “You can use various materials during printing. The results are much more complex materials.”

The new application is cheap as well: the setup the students made cost about €3000, says Scharff. Other complex multi-material 3D printing systems cost around €300,000. “That’s a big difference.”

Medical sector
In the field of soft robotics, cheap 3D printing can prove valuable for several applications. This could be the case in the medical sector, especially with aided movement. “Reducing the cost of treatment and support utilities is a goal that inspires us all. Soft robotics could, in the long term, be a big player in this progress”, the team states.

“Reducing the cost of treatment and support utilities is a goal that inspires us all”

Therefore, they developed a 3D printed glove with soft robotics actuators to help move your fingers. This glove could be used to assist people with arthritis, local paralysis, limited hand function or as supporting tool in rehabilitation. The goal of the team is not to create a finished product, however, but rather to give a proof of concept showing the possibilities of 3D printing for soft robotics.

Digital fabrication

“3D printing offers a variety of applications, being soft robotics one of them”, tells coordinator Jouke Verlinden. “It allows you to make things that are impossible with traditional robotics. Soft robotics is a research theme in which we invest a lot.”

At the science fair, other student- projects were on display as well. At first sight, some of them don’t seem to be related with robotics. Others are. But don’t be mistaken! “It is all part of digital fabrication. Project Michelangelo is clearly a robot, but the Hololens at the museum has a lot to do with robotics as well. It is all about image recognition, and in the basis, they work with the same algorithms as the ones that are being used for robotic visioning.”

This article was originally posted on RoboValley.

If you liked this article, you may also want to read these other articles on 3D printing:

• Sticky business: Five adhesives tested for 3D printing
• Foundry tool: Multi-material designing for 3-D printing
• 3-D printing hydraulically-powered robots, no assembly required
• AI, deep learning and 3D printing produce ‘The Next Rembrandt’
• Rising Media acquires RoboUniverse and Inside 3D Printing shows

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

In this episode, Audrow Nash interviews several researchers presenting their work at the Robotics Science and Systems (RSS) 2016 conference in Ann Arbor, Michigan.

Audrow speaks with Gangyuan Jing from Cornell University about modular robotics; Rico Jonschkowski from Technical University of Berlin about lessons from last year’s Amazon picking challenge, which his team won; Matthew Gombolay from MIT about software to help nurses coordinate; and Dorsa Sadigh from UC Berkeley about developing human-like behavior for autonomous cars.

Links:

• Download mp3 (9.7 MB)
• Link to RSS 2016 homepage
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS

In this episode, Ron Vanderkley interviews Jürgen “Juxi” Leitner, a researcher at the ARC Centre of Excellence in Robots Vision in the Queensland University of Technology in Australia. Leitner speaks about a system being developed for the Google Lunar XPrize, called LunaRoo.

The Google Lunar challenge calls for privately funded spaceflight teams to land a robotic spacecraft on the Moon, travel 500 meters, and transmit back high-definition video and images. LunaRoo is a hopping robot designed to leap up to 20 meters in the Moon’s lower gravity. While in the air (well, not really), LunaRoo will use computer vision to track the movement of a rover, that is trying to drive 500 meters, navigate the Moon’s surface.

Below is a video showing some prototypes of LunaRoo.

Jürgen Leitner

Jürgen “Juxi“ Leitner is a researcher at the ARC Centre of Excellence in Robotic Vision. Before coming to Australia, he worked and studied at various research institutes in Europe and Japan. From 2010-2014, he worked in a robotics lab at the Dalle Molle Institute for AI (IDSIA) in Lugano, Switzerland. Prior to IDSIA, he worked at the European Space Agency‘s Advanced Concepts Team for a year.

Links:

• Download mp3 (8.3 MB)
• LunaRoo Proposal
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS

In this episode, Audrow Nash interviews Ekaterina Bereziy, Founder and CEO of ExoAtlet, about exoskeletons for the disabled and for rehabilitation.

Transcript below.

The ExoAtlet Exoskeleton assists people with full or partial lower body paralysis, as well as people with muscle weakness to perform a walking gait. This has been shown to improve blood pressure, decrease muscles and bone degeneration, and prevent urological diseases and disorders. In addition to walking, the ExoAtlet exoskeleton can be used to go up and down stairs. Lastly, ExoAtlet is experimenting with using electrodes to stimulate the contraction of people’s muscles as a way of improving their fitness.

ExoAtlet is one of many robotics companies working with Skolkovo, an innovation cluster based in Moscow, Russia. Skolkovo provides these companies with resources and expertise to encourage their success.

Below is the video of a man walking down the aisle to be married in an ExoAtlet Exoskeleton.

Ekaterina Bereziy

Ekaterina Bereziy is the Founder and CEO of ExoAtlet. She studied Mechanics and Mathematics at Lomonosov Moscow State University and has a Master’s in Business Administration from the Russian Presidential Academy of National Economy and Public Administration.

Links:

• Download mp3 (15.9 MB)
• Link ExoAtlet’s webpage
• Link to Skolkovo’s Robotics webpage
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS

This transcript has been edited for clarity.

Audrow: Hi, welcome to Robots Podcast, can you introduce yourself?

Ekaterina: My name is Ekaterina Berez. I am the co-founder of ExoAtlet, an exoskeleton for rehabilitation.

Audrow: Can you tell me a bit about ExoAtlet?

Ekaterina: ExoAtlet started at 2014. We developed our project for 2 years, but that is not the only time that we had together. The team joined with this type of development 15 years ago. ExoAtlet is the result of our investigations from 15 years. Now, we have the first version of the exoskeleton that provides the rehabilitation for people with their injuries. The next generation of ExoAtlet will provide the possibility to work for people after strokes.

Audrow: You mentioned the team has been working for 15 years. Where has this taken place?

Ekaterina: In the Damascus State University laboratory, a mechanical and robotic laboratory. We developed a lot of different kind of robots, and they started up the World Festival of Robotics for students and for PhDs. They worked for different kind of system controls for wheels robots and for walking robots. The exoskeleton was one of the ideas that they had for this for years. As the technical solutions have been providing good for making the kind of provides so the ideas to develop the exoskeleton for this exact idea (like rehabilitation) has been implemented and we started the company and conducting it like a commercial product.

Audrow: What stage of development are you currently at? Do you have users?

Ekaterina: We finished the certification process in the Russian market, so it means that we have the certified product that we start to sell 2 months ago. It’s the first product that we developed, but we have the second one that is on R&D stage. It will be more complicated or with the more complicated system control that provides the initiation of the movement. When the person can initiate by his own leg, the movement of the ExoAtlet. It provides the help only to make a step. The person controls it by his own muscles. It’s a great idea, and I mean that it’s a great implementation of an idea. The solution is very interesting. We will implement the control of the power of engines. It means that we will provide MDs to control-

Audrow: What is NMD?

Ekaterina: MD, just medical doctors. We will provide for medical doctors through their implication that we will help them to decrease their assistance of the ExoAtlet for the patients who can work, who can already make steps by themselves. To provide them less help. Then to make them work during the training.

Audrow: Almost like an exercise or rehabilitation. Now, about Skolkovo, how you are related to them?

Ekaterina: Skolkovo Foundation is the government fund that provides help for startups. We started to the member of Skolkovo in 2014 as we started up the company. We are working on this Skolkovo robotic sensor now in the territory of this center.

Audrow: That territory is basically a campus that startups work at?

Ekaterina: Yeah, it looks like a campus and the robotics startups have facilities to make engineering product types, and work with equipment to try and prove some concepts. Also, Skolkovo has their IP center that helps us a lot with our intellectual property and to issue patents. Skolkovo provides a lot of services for startups.

Now they have good infrastructure for the kind of startups who has a great idea, but not a lot of stuff to have goods or to have the specialist for each kind of idea, for implementation. I mean, for example, the PR support is one of the most important things for startups, because it’s necessary to get feedback from the market, and to involve the development process from the market, people who are close for example, in order to implement this idea in real life.

The PR solutions are really cool. And for us it was helpful. Some key medical doctors work with us now, and who made very important clinical trials for us. We have a connection with them, because of the PR support of Skolkovo team. Also, we need funding. It’s investors who can provide their investments for developing the product. Skolkovo has their own Skolkovo fund that is government money. It’s a grant money that they can provide the startups to support some ideas that are very innovative. Maybe too much innovative for the venture capitalist. Skolkovo provides the grant money for the first stage of development to prove this kind of concepts, and next step is to invest. It’s great support.

Audrow: Would you describe the ExoAtlet exoskeleton?

Ekaterina: The first version of ExoAtlet is the exoskeleton for rehabilitation people with the spinal cord injury. We make clinical trials with different kinds of level of spinal cord injury even with very high level. It works. Now, we have 2 models. There is one for hospitals, and for working for medical doctors with the patients, with the different kind of patients. I mean with the height of 160 centimeters.

Audrow: Okay, so about 5’2 to 6’2.

Ekaterina: The weight is about 100 kilos, maybe up to 120 kilos of the patient for hospitals to use. Another model is for personal using at home. The ExoAtlet is adjustable, it’s easy to fix the exact lenses of the part of legs.

Audrow: What does it look like, before we talk about adjusting legs.

Ekaterina: It’s just like the external skeleton. External metal parts that fixes to the legs and to the upper part of the body. With the cells that you can put in your shoes and the person can put it on in a sitting position.

Audrow: It has rigid components that go along with the legs on the outside of the leg?

Ekaterina: Yes, exactly.

Audrow: Then a foot insert that goes into the sole or goes with the sole of your shoe. There is a component that the user wears that’s sitting about at their lower back?

Ekaterina: Yes.

Audrow: They are strapped in by various straps that you pull? Kind of like snowboard boot straps.

Ekaterina: Yes, it looks like the snowboard boot straps. It’s very easy to strap it and fix it.

Audrow: You mentioned adjustable.

Ekaterina: Yes. It’s adjustable. We have a lot of adjustments because all people have different measurements.

Audrow: How does this exoskeleton compare to the exoskeletons of similar companies?

Ekaterina: Most of the skeleton for rehabilitation tried to solve the same problem. It’s to give an easy way to stand up, to start the walking movement, and provide walking steps to speed up the rehabilitation process. The differences in engineering solution, in some plugins, and in that way the exoskeleton works. I mean some of them, for example, have the initiation of the movement. It means that the patient with the not completely damaged spinal cord can initiate the moment. Or, for example, some of the exoskeletons can’t help with standing up to a totally disabled person. It means that they don’t have good parts, or good motors to help the person to stand.

They can make movements and make steps when the person is already in vertical position. Most of the exoskeleton needs crutches. It’s because the patient using the exoskeleton needs to keep their balance. The only way to keep their balance is to have the third points that you can get with a crutch.

Audrow: In our conversation earlier you mentioned you can use your exoskeleton on stairs?

Ekaterina: Yes. Normally in the rehabilitation process medical doctors don’t use stairs, but if we see or if we talk about the people of the stroke, for them they are going up and down stairs. It’s one of the exercises that improves and speeds up the rehabilitation process. Also for the person using it we need to go up and down the stairs because we have a lot of stairs in our surroundings. Normally to go out of the apartment you need to go down the stairs. If we talk about the personal use for people who, for example, has a totally damaged spinal cord, it means that it’s rather difficult to have a hope to reconstruct to start working by themselves.

It means that this kind of exoskeleton will provide them help in walking. Of course, we need to go up and down the stairs and stairs are different with the different heights of the steps, and it means that you should adjust the height of the step according to the stairs. It’s a very important thing.

Audrow: Is this something that the user needs to adjust or is there any sensing onboard that tells how high the steps are by itself, and then adjusts how large of the step it will have to take?

Ekaterina: Now it has sensors, but it works with the stairs, but sometimes you need just to step towards the obstacle. It means there are different kinds of obstacles, and sometimes you need to set up yourself like a pilot, the exact height to make a step. The situations are different. We have a variety of possible adjustments.

Audrow: The step parts and those types of things. To step higher or lower, further, shorter.

Ekaterina: Yeah. Of course, different people have different ways of walking. Some of us have very high steps. For example, normally for us to make high steps, but others make very short and low steps. We can adjust it, and not with the program is plug in the computer and do it once and for years. Now you can adjust exact minutes just by changing parameters in the application.

Audrow: Now, what kind of actuators are on the exoskeleton? It has electric motors?

Ekaterina: Yes.

Audrow: Where are they? Tell me a bit about them.

Ekaterina: They are in the upper parts of the leg.

Audrow: They are at the hip and the knee, correct?

Ekaterina: The knee yes, but they’re just a little bit higher. It works, yes. It moves with the knee joint and the hip joint, so 4 of them, because we have 4 joints. It works with batteries that are set up also onboard. It’s about 6 hours that you can walk.

Audrow: That is a long time. About 6 hours of regular use?

Ekaterina: Yes. Just walking on the normal surfaces. If of course if you will stand up and sit down for 6 hours batteries needs more taxing. You need to charge it.

Audrow: Yes. For sure. What kind of sensors does the exoskeleton have?

Ekaterina: It’s a lot of sensors that helps to get diverse composition.

Audrow: Skill accelerometers and gyroscopes to do some sort of status on this?

Ekaterina: Yeah. We have all of them, and we have some sensors for initiation to get information that it will be initiation of movement by their leg.

Audrow: Would that force sensors?

Ekaterina: It’s force sensors and mind sensors.

Audrow: The mind sensors are very interesting, but the force sensors, where are they in the exoskeleton? Is it on the feet?

Ekaterina: It’s on the feet, yes. It’s on the strips.

Audrow: On the strips; what do you mean?

Ekaterina: I mean that it’s onboard of the ExoAtlet.

Audrow: It’s on the component like along the legs? Sensing the amount twerk at the motor, at each joint basically? So what do you use that information for?

Ekaterina: For system control. To make more comfortable for a person, because when you wear anything that provides some movement you need to be comfortable. It’s one of the great problem how to provide comfort for person that can’t do specific moments. It’s huge that should fit well to make the comfortable moments.

Audrow: You also use the 4 sensors, so the ones in the feet in this kind of thing or the ones in the feet to help the user to walk by themselves, correct? You determine where the weight is loaded and then you decided if you can step or not? Is this the approach?

Ekaterina: It works like this. We have mind stimulation system also.

Audrow: I want to get to that after, but just to see if I understand the force sensors it’s basically if I have my weight on my right leg then I know I can move my left leg kind of thing. The force sensor would determine that I have my weight on my right leg, so then my left leg moves. Is this how it works?

Ekaterina: One of the solutions can be like this. We have a little bit different solution. It does matter. This kind of technical details they are very our specifics. This kind of idea can work. It means that you need to make the system control, but will provide the most natural pattern of walking. From bio-mechanical point of view you can do it like a normal person. This is the goal. There are different kinds of system controls that use their data from sensors, from their voice sensors, from their food on these trips, on the different points of the leg, because it means that the angles and so on. You can get the exact information of the movement that is in the second, and provide by the drives the next phase of movement.

Audrow: Electromyogram sensors sense the electric potential on the skin, and this potential increases when things like a muscle flexes. If I flex my muscle we can detect this with an electromyogram, a myogram sensor on it. You are using this for control of the exoskeleton as well?

Ekaterina: No, we use the myostimulation systems. It’s a multiple control system. It’s for stimulating, for additional stimulation of muscles. For example, who have the people who have damaged spinal, completely damaged spinal cord, we can provide them walking, but we can’t make their muscles work during this process, because their spinal cord is completely damaged, and there is no signal. It means that we can stimulate their legs muscles, and it’s like the stimulation of the normal walking process of muscles during the walking.

Audrow: Are you electrocuting the muscle in order to stimulate it?

Ekaterina: Yeah. We put on the skin the electrodes, and we will provide their electrical nodes to the muscles through the skin. It makes muscles work.

Audrow: It’s like when you are shocked. When you get shocked somehow, and all of your muscles contract. It’s like that kind of thing, but you are using those deliberately to help the user to walk?

Ekaterina: Yeah. It doesn’t help to the walking process, but it helps muscles to reconstruct their function, because we provide the signal, the electro stimulation signal to exact muscle that must work on this phase of walking.

Audrow: How much voltage are you applying to the, I mean is it allowed? Does it hurt?

Ekaterina: No. It doesn’t hurt. It can be just from the very low that you as a normal person would never feel, but the muscle will be on the strong physical condition. It’s adjustable. It’s there. It’s one of the very complicated parts of physical therapy. It’s normally implemented during physical therapy programs. Now medical doctors use it in the position when the person laid on the sofa for example, and they do this kind of mind stimulation without movements. We implemented to the exoskeleton, and combined it with the steps. We use the information of the angles from the joints and provide to the system, the control mind stimulation and elector signals.

Audrow: Has this approach been proven to be beneficial to do the steps in combination with the electrical stimulation? Is it proven or have you found that the results of doing these 2 things is better than the result of doing either separate?

Ekaterina: Now we have a big investigation. We have the clinical trials that the results of it will be comparing between the ExoAtlet with mind stimulation system. It will be interesting result.

Audrow: What was the second one?

Ekaterina: Local Mob. It’s their rehabilitation. They are very big and very famous rehabilitation equipment. It’s very expensive, so it costs about some hundred thousands dollars. Normally it’s on the rehabilitation centers, because it’s for the big one. It’s with the treadmill and their exoskeleton implemented in this system. It’s proved, and they were a lot of researches and clinical trials that proved this kind of robotic mechanical therapy. In Russian market we have about 75 such kind of machines. Now we have the clinical trials. The idea is to compare their results of 2 weeks of training for patients who can walk a little bit, who can do some steps.

It’s not the people with the completely damaged spinal cord, but it’s the spinal cord damaged injury people who feel their legs, and who can do some steps. We will measure the differences of improvements.

Audrow: I see, and use that to determine the validity of the method. That makes sense. What are the future steps for ExoAtlet?

Ekaterina: We will develop the next version. We are working on it. We are on R&D process, the ExoAtlet for people of the stroke. Then the cerebral palsy is one of the biggest problems that is around the world. We will provide to different exoskeletons to the market.

Audrow: What would you think would be a timeline for this?

Ekaterina: We plan to make a presentation of the ExoAtlet for people of the stroke next summer. It will be summer of 2017. For children with the cerebral palsy it will be the next generation maybe in 2 or 3 years.

Audrow: Do you have any stories or anecdotes of people using your exoskeletons?

Ekaterina: We have one exoskeleton that has been implemented in hospital in the north city of Russia. There is one patient who walks with it almost every day and use it almost every day. One of the really interesting stories that it was his own idea. It wasn’t like a PR idea. He decided to marry, and for him it was very important to stand, but not to sit during this process. He entered the church with his future wife wearing the ExoAtlet and using the ExoAtlet. He made steps with this kind of sound … It’s really so cool. That Saturday morning was very interesting, because he came to his wife, and he provided her his hand and she supported him a little bit, but it was really impressive.

Audrow: Thank you!

In this episode, Audrow Nash interviews Hugh Herr, Director of the Biomechatronics Group at MIT. Herr talks about the accident that led to the amputation of both of his legs below the knee and how this shaped his rock climbing and academic career. Herr also discusses orthoses and exoskeletons developed by his research group, as well as the future of bionic technology.

Transcript below.

Among topics discussed is the lower-body exoskeleton below. This exoskeleton is the first lower-body exoskeleton to decreases the user’s energy expenditure when walking (measured by oxygen consumption). It works by assisting the calf muscle and using the body’s joints, rather than heavy mechanical joints. Hugh Herr says, “Subjects noticed that their legs felt heavier and awkward when they took the exoskeleton off.”

Below is a TED talk Hugh Herr gave in 2014. In this talk, Herr describes disability as a failure of technology, he discusses the design of his own prostheses and his lab’s research, and there is a dance performance by Christian Lightner and Adrianne Haslet-Davis, who lost her lower left leg in the Boston Marathon bombings.

Hugh Herr

His research program seeks to advance technologies that promise to accelerate the merging of body and machine, including device architectures that resemble the body’s musculoskeletal design, actuator technologies that behave like muscle, and control methodologies that exploit principles of biological movement. His methods encompass a diverse set of scientific and technological disciplines, from the science of biomechanics and biological movement control to the design of biomedical devices for the treatment of human physical disability.

His research accomplishments in science and technology have already made a significant impact on physically challenged people. The Transfemoral Quasipassive Knee Prosthesis has been commercialized by Össur Inc., and is now benefiting amputees throughhttp://biomech.media.mit.edu/people-hugh-herr/out the world.  In 2006, he founded the company iWalk Inc. to commercialize the Powered Ankle-Foot Prosthesis and other bionic leg devices.  Professor Herr’s work impacts a number of academic communities. He has given numerous invited and plenary lectures at international conferences and colloquia, including the IVth World Congress of Biomechanics, the International Conference on Advanced Prosthetics, the National Assembly of Physical Medicine and Rehabilitation, World Economic Forum, Google Zeitgeist, Digital Life Design, and the TEDMED Conference. He is Associate Editor for the Journal of NeuroEngineering and Rehabilitation, and has served as a reviewer for the Journal of Experimental Biology, the International Journal of Robotics Research, IEEE Transactions on Biomedical Engineering, and the Proceedings of the Royal Society: Biological Sciences. He has been invited to participate in joint funding proposals from other universities and corporations, and has served on research review panels including the National Institute of Health, the National Institute on Disability and Rehabilitation, and the Department of Veterans Affairs. In 2007, He was presented with the 13th Annual Heinz Award for Technology, the Economy and Employment. His work has been featured by various national and international media, including Scientific American Frontiers, Technology Review, National Geographic, the History Channel, and CNN.

Links:

• Download mp3 (18.3 MB)
• Link to the Biomechatronics Group’s webpage
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS

The transcript is edited for clarity.

Hugh Herr:
My name is Hugh Herr and I’m a professor at MIT. I co-direct the Center for Extreme Bionics. I also am the founder of a company called BionX Medical Technologies.

Audrow Nash:
Can you tell me a bit about how you have lost your legs from the knee down?

Hugh Herr:
I was an avid mountain climber. I started mountain climbing when I was seven years old. When I was in my early teenage years I was considered a child prodigy in climbing. When I was seventeen I was out with my climbing partner at the time. Our goal was to climb Mt. Washington in the winter by ascending Huntington’s Ravine, which is about an eight hundred foot, very steep ice wall. We ascended very quickly the eight hundred foot wall, and then decided to continue towards the summit. The weather conditions got worse and worse. Even though we went just five minutes above the head-wall of Huntington Ravine, we were not able to retrace our tracks and we descended the mountain.

By the time we got to tree line it was clear that we had gone off course, but we were stuck at that point. We really didn’t have the rational choice of retracing our tracks and going towards the summit. At that point, wind speeds were so high that one could not even stand. We were forced to go down this ravine system, it’s called the Great Gulf Region. It’s the wilderness side of Mt. Washington. It was there that we spent several days in most extreme bushwhacking that one can imagine. The average depth of snow was to the waist. Sometimes it was to the chest. We were just simply trapped in a white maze.

We dug in when we were not able to move any more out of exhaustion by creating kind of these snow caves. During the day we would walk. We would probably make two miles of progress in a complete kind of marathon effort in the deep snow. We made it within a few miles of the roadway and then couldn’t walk anymore because of severe frostbite. We were later discovered, or found by a person out snowshoeing for the day, and we were plucked from the mountain via helicopter and then treated for severe frostbite and hypothermia.

Audrow Nash:
Then, so from there because of the severe frostbite you had both of your legs amputated from the knee down?

Hugh Herr:
That’s right. After a few months of effort by my medical team to “save” my biological limbs, they were amputated. We were on the mountain late January. My legs were amputated mid-March.

Audrow Nash:
Afterwards, you wanted to return to rock climbing. You were told otherwise, though.

Hugh Herr:
Yeah. I didn’t know what I would be able to do with my new body, and my father said, “If you want to climb, you should climb.” I really had no clear examples of what life would be using prostheses. My limbs are amputated. I went through about a month or two of healing, and then I was fitted with my first pair of artificial limbs. I was shocked at their lack of technological sophistication. They were actually made of plaster of Paris and it was recommended that I not walk without canes or crutches because the limbs might actually break. They were kind of these crazy trainer limbs. The first weekend I went home from the rehabilitation center they didn’t allow me to take my legs because they knew what I was capable of. The next weekend they were stupid enough to allow me to take my legs and I went climbing with my brother, Tony.

Audrow Nash:
How did you begin to adjust your prosthetic limbs for climbing and various purposes?

Hugh Herr:
I quickly abandoned the notion that the limbs needed to look human, and I quickly focused on function. I thought to myself what would be optimal designs for the vertical world of rock and ice climbing. What emerged was a series of different feet for rock and ice surfaces, so feet that could stand on small rock edges the width of a coin, feet that could wedge into rock fissures even where the human foot could not penetrate, feet that could penetrate ice walls. I made my legs exceedingly lightweight to increase my strength to weight ratio. Through design, I was able to etch out a few advantages. There were disadvantages, but I gained enough advantages that it was only twelve months after my limbs were amputated that I was climbing better with artificial limbs than I’d ever achieved with normal biological limbs before the accident.

Audrow Nash:
How did you transition into academia from this, and how did you know that that is the way you should pursue, or continue?

Hugh Herr:
To be honest, the men in my family, my father and brothers and grandfathers, were in the business of construction. Given how uncomfortable artificial limbs were at the time, I couldn’t imagine being on a construction site for the rest of my life. I also couldn’t imagine being on the construction site for the rest of my life. I decided to go to college, which I’d never plan to do. My intent was to be the best climber in the world before the accident. I went to college and I signed up for very basic math courses and computer science courses. I developed an extraordinary passion for the topics and couldn’t stop studying. I just absolutely loved the material. It’s interesting because science and a passion for science and math actually replaced somewhat my passion for climbing mountains.

Audrow Nash:
Then you began to apply what you were learning to your own prostheses with what you were learning? When did you start doing that?

Hugh Herr:
I did have the goal of continuing to design prostheses. The experience of designing new climbing legs and succeeding so wildly beyond everyone’s expectation, including my own, was very inspirational for me. I realized the power of technology to heal, to rehabilitate, even to extend human capability beyond innate capabilities. That was also a clear motivation in my acquisition of knowledge and going back to school. My first patent was obtained, issued very close to the time when I was graduating from undergraduate school in physics. It was on the mechanical interface between the residuum and the artificial limb. The idea was to use fluid bladders in certain configurations. I later proposed to my future MIT advisor that I would build this interface and have actuation pumps and pressure sensing and continually modulate the pressure field around the residuum.

Audrow Nash:
On the spot where the prosthetic limb interfaces with the residual limb is what you’re talking about?

Hugh Herr:
Yes.

Audrow Nash:
So you would use some sort of fluid to make a vacuum so that it would stay very well attached?

Hugh Herr:
Yes, a vacuum offering suspension, but also offering loading support.

Audrow Nash:
Loading support.

Hugh Herr:
A pressure field that would be very comfortable around the end of the limb, enabling a person to walk without discomfort.

Audrow Nash:
Can you tell me a bit about the prostheses that you’re wearing now?

Hugh Herr:
I’m wearing two bionic legs. Both legs have three microprocessors and twelve sensors. The microprocessors are brains, if you will. The devices control a muscle-like actuator that moves and stiffens and powers my synthetic ankles.

Audrow Nash:
What sensors are on board?

Hugh Herr:
The sensors measure position, speed, acceleration, force and temperature, as well as joint position and speed.

Audrow Nash:
What sensing are you doing, contact with the ground?

Hugh Herr:
No. The device has tendon-like series springs, and also a parallel elasticity. The torque sensing is of the torque that the series elastic actuator sees and the parallel spring sees.

Audrow Nash:
I see.

Hugh Herr:
Either one can produce net torque on a joint.

Audrow Nash:
Can you describe how they look a little bit?

Hugh Herr:
They look interesting. There’s two black batteries that sit on top, and there’s black anodized metal shields coming around the ankle. Then inside those shields are all the electronics and then the motor system. Then attached to that is a synthetic foot made of again, black carbon composite.

Audrow Nash:
What kind of grip on the bottom?

Hugh Herr:
I wear the carbon foot, and glued to that carbon foot is a typical rubber that you’d find on the bottom of a shoe.

Audrow Nash:
What kind of battery life?

Hugh Herr:
We get a few thousand fast walking steps. For me and my lifestyle, that’s fine to get through the day. If one wants to walk say, ten thousand steps, one would just take a spare battery or two. The batteries are modular. You just snap them in like a power tool.

Audrow Nash:
I see. How does this compare to other prostheses?

Hugh Herr:
All other foot/ankle prostheses in the world are human powered, meaning the energy of the human attached to them drives the movement. The legs that I’m wearing are bionic. They inject mechanical energy into a person’s stride in a way that’s similar to what the muscles used to do that were lost on amputation.

Audrow Nash:
Can you talk a bit about how they are fit to your residual limbs?

Hugh Herr:
At MIT I have a team of people thinking about fit. What is the nature of comfort? What are the mathematics of comfort? What is the science of comfort? We build mathematical descriptions of the tissues of the ends of the residuum and theories of how the shape of the structure that comes around the residuum and the stiffness of that shape to optimize comfort. Basically, the end of my limb goes into a cup-like device that supports my weight, and so the shape of that cup is critically important to comfort. We mathematically derive that shape, and then we 3D print the structure.

Audrow Nash:
You’re identifying where there are hard and soft spots in the residual limb so that you can match them or match them with their complement, so soft to hard, hard to soft so that it’s more comfortable?

Hugh Herr:
Yeah. Then what’s very complex is the shape, the equilibrium shape of the synthetic skin, if you will. That’s based on tissue, how compliant the tissues are, that shape. Mathematically we understand the compliance of the tissues in all the regions.

Audrow Nash:
What do you mean, compliance of the tissue?

Hugh Herr:
How soft or how stiff they are.

Audrow Nash:
Okay. What about stretch of say, the skin? Does that apply as well?

Hugh Herr:
In a different way. I wear a second skin silicone liner, so the design of that second skin relates to the skin’s strain field, or the amount of stretch in the skin.

Audrow Nash:
I see. Are they attached by this vacuum principle or how would you attach them to the residual limb?

Hugh Herr:
There’s a number of ways to attach. You can have a pin and a lock at the base of the limb. You can have suction. You can have a sleeve that spans from the biological leg across the socket. There’s a number of ways to hold it on.

Audrow Nash:
What have been some challenges in designing this?

Hugh Herr:
The interface. If you ask a thousand persons with limb amputation what’s the number one problem they want solved, probably all thousand would say the mechanical interface, the mechanical attachment. Why? Because it’s uncomfortable. It’s a very, very important problem. It’s a very complex problem because no one understands what comfort is for any device, or there’d be a shoe or a prosthesis. We’re developing that science, and it’s very exciting. We want to be able to produce sockets that are comfortable. We want to produce them fast at low cost.

Audrow Nash:
What are a means of doing this? You 3D print the part. How do you model the residual limb?

Hugh Herr:
We take an MRI image of the limb, and that tells us where the shape of the bones and where the skin is and where the muscles are. We then use robotic palpation tools to compress the tissues and measure displacement speeds and forces, which tell us the fundamental constants of the tissues. We then build a continuum mechanical model. If you have a mathematical description of the biological segment, you can then apply pressure fields. The model will tell you how the tissues deform. Then we define particular tissue deformations that are healthy and comfortable.

Audrow Nash:
Is the goal is to make this cheap and replicable for the general person?

Hugh Herr:
Yeah. My vision of the future is that each human individual will have a digital representation of their body. When they need any type of a bionic device, that digital body will be used to compute an optimal personalized bionic device for the human.

Audrow Nash:
This can refer to clothing as well.

Hugh Herr:
Correct. Clothing, shoes, bras, bike seats, bionic limbs, exoskeletons, neural implants that go inside the body. Everything will be personalized.

Audrow Nash:
Everything is optimized, and this relies heavily on 3D printing or additive manufacturing?

Hugh Herr:
3D printing is a tool. It’s not the dominant science. That is to say, one could build these interfaces with molding processes and not using digital fabrication. Digital fabrication is sometimes nice because it reduces the fabrication frequency, but it’s not necessary. It’s not the secret sauce.

Audrow Nash:
What’s the role of industry and academia in producing these prostheses?

Hugh Herr:
As stated, I’m a professor. The typical model of a professor in the US is they receive some type of grant monies and they conduct research with a team comprising both staff and students. There are inventions. Patents are filed. Patents are owned by the university. The university then licenses the intellectual property to founders of a company. Those founders can be the professors and students that invented, and often that’s the model, the most successful model of translation. In my case that’s exactly what happened. There was patents generated. I’m the inventor. My students are the inventors. A company’s established and then there’s a contractual relationship between the company and the university, licensing the IP within a certain domain of use. That’s what was done with my company, BionX Medical Technologies.

Audrow Nash:
What role does the company play in developing the technology?

Hugh Herr:
At university we do science. We do research that’s publishing. We test hypotheses. We do things that have never been done before, never been tested before. At the company, basically crude prototypes that we build at the university are commercialized. What that means is getting them light enough, most important strong enough, durable enough, manufacturable, at the right price point. Then all the sales and clinical services necessary to distribute that product globally.

Audrow Nash:
What does it look like when someone puts on the BiOM ankle for the first time?

Hugh Herr:
The design in the ankle is biomimetic, inspired by nature, by the human body. The algorithms that are running on these small computers on the ankles control the motor as if the motor were made of muscles and tendon and the whole structure was bone and whatnot. The whole thing moves as if it was made of flesh and bone, even though it’s made of synthetics. Because of that, when it’s fit to a human, the human is used to those dynamics, so there’s little to no training required. Often in minutes the person is saying things like they have their life back, they have their leg back, and they’re either crying out of happiness or laughing out of happiness. It’s fun to go to these fittings, actually.

Audrow Nash:
I’d like to move into talking about exoskeletons. First, would you define what an exoskeleton is and then tell us how it relates to your work with prostheses.

Hugh Herr:
My definition of an exoskeleton is a device that attaches to the body, intimately to the body, that augments physicality. What I mean by augments, it enables a human being to do something that’s beyond natural capability for an innate, healthy body. The word orthoses is a medical term for a robot that attaches to the body that enables a person with a disability or some condition to move more naturally. Exoskeletons are wearable robots that augment.

Audrow Nash:
Can you tell me a bit about metabolic cost as a parameter for designing exoskeletons?

Hugh Herr:
One goal in exoskeletal design is to augment a human by pedal locomotion. An excellent metric of evaluation is the amount of food energy a person requires when using the exoskeleton versus not using the exoskeleton. Why? My view is that if the exoskeleton increases energy levels of the human, the human won’t want to use it. It won’t have sufficient value. It’s a very important metric of evaluation.

Audrow Nash:
Basically they’ll discard it and not use it if the metabolic cost is higher than the task, or higher than it would be to accomplish otherwise?

Hugh Herr:
You know, unless they want to get exercise. If they want to actually be augmented and enhance physicality, no they won’t use it.

Audrow Nash:
How do you measure this?

Hugh Herr:
Typically for aerobic exercise it’s measured by monitoring how much oxygen a person uses when they breathe and how much carbon dioxide is expelled. With those two rates one can compute the calories burned per unit of time.

Audrow Nash:
Would you describe the exoskeleton used to decrease metabolic costs for walking?

Hugh Herr:
The first exoskeleton that was envisioned and published in the world that I’m aware of was in the nineteenth century, over a hundred years ago. It was a Russian inventor called Jagen. His dream was to augment the Russian Army, Russian soldiers. It wasn’t until 2014 that someone succeeded in building an exoskeleton that augments walking and running. My group was the first to accomplish that goal. It’s an exoskeleton that spans the foot and comes up about mid-calf, just below the knee. Fundamentally it’s an artificial calf muscle. The calf muscle is the most important muscle in bi-pedal walking. It supplies about eighty percent of the power to walk. It’s where humans are most inefficient, so the exoskeleton adds an artificial calf muscle and injects energy into the gait like a calf muscle to reduce the metabolic cost of the biological calf.

Audrow Nash:
What does it look like and what kind of actuators are you using?

Hugh Herr:
The actuators are electric based. It looks very interesting. It’s like, this crazy structure, this crazy shoe that goes higher than is normal and has these springy struts that go up on the side of the leg, and then a motor that sits on kind of a shin guard that pulls on that strut and powers movement.

Audrow Nash:
Yes, and so it’s using some sort of tether and you’re pulling that, and that’s how you flex the calf to augment walking?

Hugh Herr:
Flex the ankle, yes.

Audrow Nash:
You’re using the body as its own joint for this. Correct?

Hugh Herr:
It’s very important that exoskeletons are very, very, very light weight. Whenever you add weight to the legs, metabolic cost increases. In an effort to reduce the weight or the mass of the device, we actually use the body’s joints as the bearing instead of putting a synthetic bearing in the device.

Audrow Nash:
What kind of sensing is done with the device?

Hugh Herr:
All the electronics are up high by design so you can walk through a deep, deep puddle of water and not affect the system. We measure positions and speeds and accelerations, essentially. Then from those data we can compute torques, forces, given a model of the structure.

Audrow Nash:
You can infer where the person is in their gait and then determine if you should pull to assist the gap?

Hugh Herr:
Essentially.

Audrow Nash:
Why did you choose to revolve around the ankle?

Hugh Herr:
Again because the calf muscle is the most important muscle in walking.

Audrow Nash:
Are there other advantages to it as well, being that there’s not so much squishy …

Hugh Herr:
Yeah. The shin bone is where the leg is most stiff, most rigid, and it’s a tremendous plate of bone to apply forces on from an exoskeleton.

Audrow Nash:
Can you only use this for walking? Could someone use it for running or other activities?

Hugh Herr:
The device is very versatile. It’s powered, it has smart computation, so yes you can walk, run up and down hills, whatever you want to do. It’s as versatile as the human leg itself.

Audrow Nash:
What kind of efficiency gains are we getting?

Hugh Herr:
There’s a number of peer-reviewed manuscripts. Again, we were the first in 2014 to augment the human in a peer review publication. That first publication, the human subjects were wearing a twenty-three kilogram backpack and we augmented by eight percent. We later conducted a separate study on unloaded humans, just regular walking, and augmented by an average ten percent. The variation is huge, so you have N subjects. Some subjects are as high as almost thirty percent, some are low, and everything in between.

Audrow Nash:
What do you believe creates that variation?

Hugh Herr:
It’s a really interesting question. No one really knows, but everyone in the field has observed this phenomenon, that when you put a single exoskeletal design on N people, you get huge variation. My belief is you take ten people and you could distribute them in terms of athletic capability. Some people have a very smart spinal cord and they can exploit any tool or device very, very quickly. Others are clueless. That’s a possibility where training may be effective to mitigate that variance.

Audrow Nash:
Now, people that use it, what do they say it feels like?

Hugh Herr:
The human body is extraordinary, so a person begins to use it, they very quickly … Initially they feel the energy that it provides, but very quickly the human body gets used to it. Then you take it off and you kind of stumble, and your own biological normal legs feel heavy, awkward and slow. This tells us that wearing bionic structures will just be commonplace in the future, and innate, normal bodies will just be boring.

Audrow Nash:
Now, what kind of upper limit do you think we can get on efficiency with this device?

Hugh Herr:
With the foot/ankle device it’s not known. I would say at least twenty-five percent, but it may be even higher, which is very, very exciting.

Audrow Nash:
Then will you benefit, you think, from augmenting other muscles or other movements?

Hugh Herr:
We don’t know. What’s intriguing is our recent paper on the device. What’s intriguing is when you add exoskeletal torque and power at the ankle, not only do you reduce the power requirements of the biological ankle, but also you reduce the power requirements of the knee and hip. Even though the exoskeleton doesn’t span the knee and hip we nonetheless, reduce muscular effort at those joints tremendously. It begs the question do you really need an exoskeleton that spans the whole leg, or is the foot/ankle sufficient?

Audrow Nash:
Why would you think that it decreases the expenditure at those muscles?

Hugh Herr:
It’s just the dynamics of the system. The body, when you give it power, it uses that power distributively across the entire leg in an optimal way. It’s a new gait. It’s a different gait … than normal.

Audrow Nash:
How does it look compared to normal?

Hugh Herr:
It looks normal to an untrained eye. It doesn’t look like a bizarre walk. It is a distinctly new gait. You give the human energy from a bionic structure and the body quickly figures out how to ultimately use that energy to minimize its own metabolic food energy. That’s what humans are really good at. We’re inherently lazy and we minimize energy really, really well. We’re terrible at detecting high stress levels and probabilities of injury. We injure our knee and we’re surprised. We injure our hip and we’re surprised. An incredible value of exoskeletons is to give the human something that they’re bad at. One class of exoskeletons in the future will be exoskeletons that enable us to do athletic performances without a threat of injury.

Audrow Nash:
How would those work?

Hugh Herr:
I don’t know, but they would have to detect high levels of stress and fatigue in biological structures and then tell the human to stop or to move in a different way. Imagine a world where our top athletes never injure. Imagine the impact to human athletic performance. Performance would just go through the roof, because often what mitigates top performance is injury and recovering from injury and not training harder because you might injure, yada, yada, yada.

Audrow Nash:
What other future applications of exoskeletons do you see?

Hugh Herr:
Another form of augmentation is maintaining one’s inherent physicality independent of age. Imagine a world when you run your best marathon time when you’re sixty, and you’ve been running marathons since the age of fifteen. Why? Because you’re a more skilled athlete. As you age, technology eliminates the impact of age related degeneration by neural implants, maybe by regenerative medicine, maybe by exoskeletons or various forms of augmentation. That’s a very palpable form of augmentation. Most people ethically would be fine with that and most people would be excited about maintaining quality of life as they age.

Audrow Nash:
What are some bottlenecks in exoskeletons? What technological bottlenecks are there?

Hugh Herr:
I’m not sure there are technological bottlenecks. I think that largely the components are sufficient. It’s really a problem with design and architecture. The human machine interaction is something that’s unknown, so putting a bionic structure on a human and adapting its control in real time in a kind of human machine optimization is critically important to the future of this area of design. Imagine putting on a device and you just starting to walk and run and it adjusts its behavior to optimize its performance with you in a collaborative effort.

Audrow Nash:
Is this one of your research interests moving forward?

Hugh Herr:
Absolutely.

Audrow Nash:
What are some of your near-term research goals?

Hugh Herr:
I want to do three things first and foremost. One is I want to advance better muscle-like actuators. Perhaps actuators that are better than biological ones, kind of the engine of BionX. I want to understand electrical interface between the peripheral human nervous system and devices, so how to talk to nerve endings, essentially. I also want to understand how to attach machines to the body mechanically in a comfortable way. Those three innovations will really define bionics in this century. If you solve all three, you’re more or less done. You’ve solved bionics, limb bionics that is.

Audrow Nash:
Thanks for being on Robots Podcast.

Hugh Herr:
Thank you.

In this episode, Audrow Nash interviews Fredrik Gustafsson, Professor in Sensor Informatics at Department of Electrical Engineering in Linköping University, about an initiative to reduce poaching in a rhino sanctuary in Ngulia, Kenya. Gustafsson discusses how he first became involved in this project, how he has worked with the rangers to develop solutions, and the future of this work.

Fredrik Gustafsson

Fredrik Gustafsson is professor in Sensor Informatics at the Department of Electrical Engineering, Linköping University, since 2005. He received the M.Sc. degree in electrical engineering 1988 and the Ph.D. degree in Automatic Control, 1992, both from Linköping University. His research interests are in stochastic signal processing, adaptive filtering and change detection, with applications to communication, vehicular, airborne, and audio systems. His work in the sensor fusion area involves design and implementation of nonlinear filtering algorithms for localization, navigation and tracking of all kind of platforms, including cars, aircraft, spacecraft, UAV’s, surface and underwater vessels, cell phones and film cameras for augmented reality. He is a co-founder of the companies NIRA Dynamics (automotive safety, including tire pressure monitoring systems found in more than 20 million, here), Softube (plug-ins for music studios and software solutions for Marshall and Fender), and Senionlab (leading manufacturer of indoor navigation solutions for sm, here).

Links:

• Download mp3 (16.9 MB)
• Project Ngulia website
• A PDF overview of Project Ngulia
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS

This is the second of two episodes where Audrow Nash interviews several companies at the International Conference for Robotics and Automation (ICRA). ICRA is the IEEE Robotics and Automation Society’s biggest conference and one of the leading international forums for robotics researchers to present their work. The 2016 conference was May 16-21 in Stockholm, Sweden.

Interviews include the following:

Greg Burman, Engineer at iniLabs, explains a novel, high frequency camera that senses differences in illumination at each pixel.

Dave Rollinson, Co-Founder of HEBI robotics, talks about on a modular system for constructing robots.

Tony Prescott, Director of Research at Consequential Robotics, discusses work on a companion robot.

Xavier Carpentier, Phoenix Technologies, on motion tracker used on the International Space Station.

Links:

• Download mp3 (17.4 MB)
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS

Most of D’Andrea’s work shown at this latest demonstration is dedicated to pushing the boundary of what can be achieved with autonomous flight. One key ingredient is localization: To function autonomously, robots need to know where they are in space. Previously his team has relied on an external high-precision motion capture system available in his Flying Machine Arena at ETH Zurich for positioning. In his previous TED talk on robot athletes for example, you can clearly see the reflective markers required by the motion capture system. This meant that most algorithms were difficult to demonstrate outside the lab. It also meant that the system had a single point of failure (SPOF) in the centralized server of the mocap system — a highly problematic point for any safety critical system.

In another world first, Raff and his team are now showing a newly developed, doubly redundant localization technology from Verity Studios, a spin-off from his lab, which gives flying machines, and robots in general, new levels of autonomy. For the live demonstrations, all flying machines use on-board sensors to determine where they are in space and on-board computation to determine what their actions should be. There are no external cameras. The only remote commands the flying robots receive are high level ones, such as “take-off” or “land”.

The nature of this performance was also unprecedented in that it did not use safety nets to separate the audience from the action, and in that it even saw dozens of vehicles fly directly above the audience.

The performance included demonstrations of various safety systems for flying machines, including a large, high-performance quadcopter that is fully redundant and that utilizes a state-of-the-art failsafe algorithm (see previous Robohub article) among its many other onboard and offboard safety features.

Following extensive technical discussions on the various failure modes of the positioning system and of each flying machine as well as on the systems’ numerous redundancies and safety features, the organizers of TED had full faith in the systems’ safety and reliability. So much so that they decided to indemnify the venue against any lawsuits to overcome its ban on drones.

Here is the video:

And here is a more in-depth breakdown of what you are seeing, based on a transcript of the presentation and information from Raff D’Andrea’s team:

Intro video – Institute for Dynamic Systems and Control (IDSC), ETH Zurich

The video shows some of the previous work of D’Andrea’s group on aerial construction: A 6-meter tall tower built out of 1500 foam bricks by four autonomous quadcopters over a three day period in front of a live audience and a rope bridge built by three autonomous quadcopters in the Flying Machine Arena.

Live demo 1: Tail-Sitter – IDSC, ETH Zurich

The Tail-sitter. Photo: Bret Hartman / TED

“This is a so-called tail-sitter. It’s an aircraft that tries to have its cake and eat it: Like other fixed wing aircraft, it is efficient in forward flight, much more so than helicopters and variations thereof. Unlike most other fixed wing aircraft, however, it can hover, which has huge advantages for take-off, landing, and general versatility. There is no free lunch, unfortunately, and one of the challenges with this type of aircraft is its susceptibility to disturbances, such as wind-gusts, while hovering. We are developing control architectures and algorithms that address this limitation. [Pushes the vehicle, then grabs it and throws it] The idea is to allow the aircraft to autonomously recover no matter what situation it finds itself in. [Second throw] And through practice, improve its performance over time. [Third throw, violently spinning the vehicle]”

Demo 2 (video of demo the night before): Monospinner – IDSC, ETH Zurich

The Monospinner. Photo: Marla Aufmuth / TED

“When doing research we often ask ourselves abstract, fundamental questions that try to get at the heart of a matter. One such question: What is the minimum number of moving parts necessary for controlled flight? This line of exploration may have practical ramifications; take helicopters, for example, which are affectionately known as “machines with over 1000 moving parts all conspiring to cause bodily harm”. Turns out that decades ago skilled pilots were flying RC airplanes with only 2 moving parts: a propeller and a tail rudder. We recently discovered that it could be done with only 1.”

“This is the monospinner, the world’s mechanically simplest, controllable flying machine, invented just a few months ago. It has only one moving part, a propeller. There are no flaps or hinges, no control surfaces or valves, no other actuators. Just one propeller. Even though it is mechanically simple, there is a lot going on in its electronic brain to keep it stable and move through space in a controllable fashion. Even so, it does not yet have the recovery algorithms of the tail-sitter, which is why I have to throw it just right.”

Live demo 3: Omnicopter – IDSC, ETH Zurich:

The Omnicopter. Photo: Bret Hartman / TED

“If the monospinner is an exercise in frugality, this machine here, the omnicopter, with its 8 propellers, is an exercise in excess. What can you do with this surplus? The thing to notice is that it is highly symmetric, and as a result it is ambivalent to orientation. This gives it an unprecedented capability: It can move anywhere in space, independently of where it is facing, and even of how it is rotating. It has its own complexities, mainly due to the complex, interacting flows of its 8 propellers. Some of this can be modeled, while the rest can be learned on the fly. [Omnicopter takes off, manoeuvres around the stage, lands]”

Live demo 4: Fully redundant multicopter – Verity Studios

Fully redundant quadcopter. Photo: Ryan Lash / TED

“If flying machines are going to find their way into our daily lives, they will need to become extremely safe. This machine here is a prototype being developed by Verity Studios. It actually consists of two separate, two propeller flying machines, each capable of controlled flight. One wants to rotate clockwise, the other one counter clockwise. When all systems are operational, it behaves like a high-performance, 4 propeller quadrocopter. [Quadcopter takes off and flies an arc across the stage] If anything fails, however – propeller, motor, electronics, even a battery pack – it is still able to fly, albeit in a degraded way [One half of the quad is de-activated, the quadrocopter starts to spin and fall, the failsafe mode kicks in and the quadrocopter recovers, flies to the back of the stage, and performs a controlled landing at the takeoff spot].”

Live demo 5: Synthetic swarm, 33 small flying machines – Verity Studios

Photo: Bret Hartman / TED

“This last demonstration is an exploration of synthetic swarms. The large number of coordinated autonomous entities offers a radically new palette for aesthetic expression. We have taken commercially available micro-quadrocopters, each weighing less than a slice of bread, and equipped them with our localization technology and custom algorithms. Because each unit knows where it is in space and is self-controlled, there really is no limit to their number. [Swarm performance]”

Raffaello D’Andrea wrapped up with: “Hopefully these demonstrations will motivate you to dream up new revolutionary roles for flying machines. For example, the ultra-safe flying machine [points to the fully redundant multicopter] has aspirations to become a flying lampshade on Broadway. The reality is that it is difficult to predict the impact of nascent technology. And for folks like us, the real reward is the journey, and act of creation. It is also a continual reminder of how wonderful and magical the universe we live in is; that it permits clever, creative creatures to sculpt it in such spectacular ways. The fact that this technology has huge commercial potential is just icing on the cake.”

More photos for your viewing pleasure:

Photo: Bret Hartman / Ryan Lash / TED

Photo: Bret Hartman / Ryan Lash / TED

Photo: Bret Hartman / Ryan Lash / TED

Photo: Steven Rosenbaum / Waywire

Transcript below.

In this interview, Audrow Nash talks to two teams from Mobile Microrobotics Challenge at the 2015 International Conference for Robotics and Automation (ICRA).

The first team was from the University of Hawaii, Manoa. We spoke with Leanne King about the Autonomous Mobility & Accuracy Challenge. In this competition, teams try to move a small robot accurately around a shape. The robot can be a small piece of metal or even a bubble generated by a laser. Leanne’s team won the Mobility & Accuracy Challenge.

The second team was from West University of Timișoara (UVT) in Romania. We spoke to Ioan Alexandru Ivan about the Microassembly Challenge, where teams tried to move small triangles against walls and other fixtures using a micro-robot. The Ivan’s team placed second in the Microassembly Challenge.

Links:

• Download mp3 (8.9 MB)
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS
• ICRA 2015 Mobile Microrobotics Challenge

Transcript:

Microrobotics Mobility and Accuracy Challenge: Interview 1

Audrow Nash:  I’m at ICRA at the …

Edward Nerad:  … Mobile Microrobotics Challenge.

Audrow Nash:  And I am speaking to a team from …

Leanne King:  … University of Hawaii, Manoa.

Audrow Nash:  Would you introduce yourself?

Leanne King:  My name is Leanne, I’m a senior Electrical Engineering student at Manoa. I’m joined by Edward and Caralyn. Edward just graduated and Caralyn is also is a senior.

Audrow Nash:  Would you tell me a bit about the Challenge, the Mobility Challenge?

Leanne King:  The Mobility Challenge was where you had to autonomously traverse three different shapes: a rectangle a triangle and a circle as quickly and accurately as possible. Teams were judged based on time, how big a robot you had and accuracy; how many landmarks you hit on the field.

Audrow Nash:  First, what are these robots?

Leanne King:  Microrobotics could be anything really. Most teams use magnetic dust and electromagnetics to control it. Our team uses bubbles. We generate our bubbles using lasers.

Audrow Nash:  You generate the bubble using a laser?

Leanne King:  Yes.

Audrow Nash:  How does that work?

Leanne King: When you heat oil to a certain point, it creates differences in the surface tension. The thermal changes create surface tension in certain areas at the laser point that then generates a bubble.

Audrow Nash:  Differences in surface tension?

Leanne King:  Yeah, so then we can control that bubble using the same principle. The bubble wants to go to the laser.

Audrow Nash: Changing the surface tension of the area around it moves the bubble?

Leanne King:  Yes.

Audrow Nash:  Interesting. And so you direct the laser around these shapes and that’s how you control where the bubble goes for these challenges?

Leanne King:  Yes, we pick certain points, like critical points. For example, a triangle has three vertices, we pick those three points and then the computer finds a solution for those lines and the laser just steps through them.

Audrow Nash: Is there a vision system? How are you looking at what this bubble does?

Leanne King:  We have a camera looking thorough a microscope objective lens and that’s really it. We connected it to a computer and that’s how you see …

Audrow Nash:  And you select the points manually, where you want it to go?

Leanne King:  Yes, we pick our points. We were working on a visual system, using a camera, but it’s a little less reliable because our fields get dirty. So we decided to do it manually.

Audrow Nash:  What are some of the major challenges with this?

Leanne King:  Of our personal major challenges, our laser strength is very critical. If we don’t have a strong enough laser, or enough of a focused laser, the bubble won’t generate or we won’t be able to move it efficiently. The scale of the competition, because it’s on a micro scale …

Audrow Nash:  Can you relate the scale to something?

Leanne King:  Hair.

Audrow Nash:  Like, is it 100^th of the thickness of a hair?

Leanne King:  It’s like the thickness of a hair.

Audrow Nash:  The thickness of a hair.

Leanne King:  Yeah, the robots can be no bigger than 500 micrometers, and fields, I think, are three centimeters long. The entire course is 3 centimeters. It’s pretty small.

Audrow Nash:  What are some future applications of this kind of research?

Leanne King:  We’ve been doing some research into what this can be used for. Right now this is kind of a proof of concept thing that we can do something so small and move objects around with other small objects. Some research is already being done in the medical field where you can move particles in packets of medicine through a body with micro-robots. That will give us better precision and quicker recovery time or healing processes, and things like that. It has a lot of medical applications so far.

Audrow Nash:  Are there applications outside of the medical field?

Leanne King:  If you were able to create a system that can be put onto, for example, the BP oil spill, it can move those micro-particles and clean up the spill without humans needing to be in the water. If you move the micro-particles quickly, it will be able to clean it a little bit more efficiently.

Audrow Nash:  How do you feel about the competition so far?

Leanne King:  We’re enjoying it, we’re enjoying ourselves. Yesterday we did very well in the autonomous portion of the contest; we were one of only two teams to complete the challenge. That secures us first or second place, depending on how well we did compared to the other team. Today, we had some problems moving triangles in the mobility challenge, but we know what happened, what went wrong and how to fix it. So we’ll be back next year.

Audrow Nash:  Thank you.

Leanne King:  No, thank you.

Microrobotics Assembly Challenge: Interview 2

Audrow Nash: Would you tell me who you are?

Ioan Alexandru Ivan: I’m Alex Ioan, I’m an assistant professor at the University of Timișoara, which is located next to the capital city of Bucharest, Romania.

Audrow Nash: Would you tell me a bit about the Microassembly Challenge?

Ioan Alexandru Ivan: The Microassembly Challenge consists of pushing very small triangular objects, roughly the size of a human hair, into a very narrow channel of less than a millimeter in diameter. To be precise, it’s 750 microns, 0.75 of a millimeter. The winning team is the one that can insert the most triangular objects into this channel in two minutes.

There are several actuation principles involved, namely electromagnetics – these microrobotic devices are not tethered. This means that you cannot wire them; they should be operated wirelessly.

Audrow Nash: Yes, and so you control them with magnetic forces?

Ioan Alexandru Ivan: Yeah, with magnetic forces.

Audrow Nash: Can you tell me a bit about your setup that uses magnetic forces?

Ioan Alexandru Ivan: We use a set of four coils as you can see here. We have an arena, in the middle region, and inside we have the microrobotic agent that is a neodymium micro magnet of around 220 micrometers, each side.

Audrow Nash: What I’m looking at is a series of… there are four coils here?

Ioan Alexandru Ivan: Yeah.

Audrow Nash: And the coils, their cylinders in the top part are facing the neodymium magnet, correct? That’s in the center of all four coils, it kind of looks like a plus sign.

Ioan Alexandru Ivan: Well, we will generate magnetic gradient and this will attract the neodymium orient and the neodymium magnet towards the magnetic gradient field. So, with a joystick, we are able to drive these four coils and have very smooth movement.

Audrow Nash:  What does the neodymium magnet do?

Ioan Alexandru Ivan:  The neodymium magnet is the magnetic agent. It will respond to the magnetic field generated by the coils.

Audrow Nash:  So, the neodymium magnet – is that what you’re moving?

Ioan Alexandru Ivan:  Yes.

Audrow Nash:  The coils that are in the plus shape, you put a current through them and this generates a magnetic field that moves the neodymium magnet – that’s the small microrobot that you’re moving. And it’s moving on a X-Y plane.

Ioan Alexandru Ivan:  Exactly, X-Y and rotation.

Audrow Nash:  And rotation.

Ioan Alexandru Ivan:  And rotation: X-Y-R. They’re also doing 5D [indicating another group] so they also do Z, they have a much more complex setup.

Audrow Nash:  So one of the groups behind us does depth as well.

Ioan Alexandru Ivan:  Yeah, definitely you should ask them questions. However, we focused on integration, as you can see we have an all-inclusive device, all integrated.

Audrow Nash:  Yes, it’s very small.

Ioan Alexandru Ivan:  We have the smallest of the most compact ones and it’s very low cost. It’s based on open source technology. Here, you can see the Raspberry Pi …

Audrow Nash:  So you have a Raspberry Pi with a touch screen?

Ioan Alexandru Ivan:  Yeah, we developed a sort of shield for this one. We would like, eventually, to advertise it on Kickstarter or something like that.

Audrow Nash:  That would be cool.

Ioan Alexandru Ivan:  So we intend to further develop this product. It can help a lot for teaching, for instance. You can teach actuation principles, you can teach image processing, you can teach control systems.

Audrow Nash: It’s a nice little package for it.

Ioan Alexandru Ivan: Yes.

Audrow Nash: I agree. So, going back to the competition, you’re moving this neodymium magnet and it is pushing around these little triangles to try to put them together or put them against the wall. How did the competition go?

Ioan Alexandru Ivan: Quite well. Many competing teams were able to insert triangles and we also managed several. Right now, we’re having a small issue with one of the rules because they …

Audrow Nash: If the triangles go on to the borders.

Ioan Alexandru Ivan:  If it goes out of bounds. In bounds, the ruling is a little bit fuzzy.

Audrow Nash:  It’s not clear, I see.

Ioan Alexandru Ivan:  But we still managed to insert around five or six triangles in the two- minute window.

Audrow Nash:  And so, what are some of the applications of this technology?

Ioan Alexandru Ivan:  Well, the main application could be biomedical. For instance, this type of task simulates pushing objects or medicine, drugs, into blood vessels or noninvasive surgery. One day, robotic agents with some cancer treatment drugs will be driven through the body to the tumor, and they will locally deliver chemo static medicines. I think this field will really grow; microrobotics develops more and more as the technology advances towards nanotechnology, micro-technology. This has huge potential for the future.

Audrow Nash:  What about microassembly, actually building very small robots or something else, does this technology encompass that as well?

Ioan Alexandru Ivan: Yeah, you can build things.

Audrow Nash:  What can we do with these small things?

Ioan Alexandru Ivan: We could build 3D micro systems, 3D MEMS (Micro-Electro-Mechanical Systems), besides the medical applications. Presently, MEMS are based on planar technology but, in the future, for further integration, some parts of the MEMS could be assembled in 3D to build, for instance, more integrated actuators, more lab-on-a-chip integrated devices that are not possible today. This could also be a way forward for more integrated applications.

Audrow Nash:  Would this be similar to how they are now layering transistors for more space, rather than just moving along one plane?

Ioan Alexandru Ivan:  Yeah, I’m not entirely familiar with that technology.

Audrow Nash:  But that doesn’t require microrobots.

Ioan Alexandru Ivan:  Well, I don’t know. Probably one day we’ll have extremely small devices based on this technology. They could be, for instance, taken by microrobotic manipulators and placed inside very small systems. The idea is to embed intelligence but we’re quite far from that stage.

Audrow Nash:  Well, not actually intelligence within the robot but in how you control it, with an external magnetic field or a laser for example, correct?

Ioan Alexandru Ivan:  Yeah, for the moment we cannot embed intelligence in a tiny robot under a millimeter. But these incremental steps will help, in the future, to develop such systems. It’s groundwork for future applications. The more we integrate, the more we are able to manipulate accurately and this will allow us to make the next generation of robots more integrated and more intelligent.

Audrow Nash:  Thank you.

Transcript included.

In this episode, Ron Vanderkley speaks with Dr. Lei Cui from Curtin University about his team’s work on 3D printable hand orthosis for rehabilitation, a  task-oriented 4-DOF robotic device for upper-limb rehabilitation and a 3-DOF platform providing multi-directional perturbations for research into balance rehabilitation. They also discuss a high-speed untethered robotic fish for river monitoring and an amphibious robot for monitoring the Swan-Canning River System.

A 3D Printable Parametric Hand Exoskeleton for Finger Rehabilitation

ComBot: a Compact Robot for Upper-Limb Rehabilitation

A 3-DOF Robotic Platform for Research into Multi-Directional Stance Perturbations

Curtin Robotic Fish

AmBot: A Bio-Inspired Amphibious Robot for Monitoring the Swan-Canning Estuary System

Lei Cui

Dr. Lei Cui completed his PhD degree in Mechanical Engineering in July 2010 from the Centre for Robotics Research, King’s College London (UK). He continued his employment as Postdoctoral Research Associate after graduation. In February 2011 he moved to the Robotics Institute, Carnegie Mellon University(US) and worked as Postdoctoral Fellow until July 2012. He joined the Department of Mechanical Engineering, Curtin University (AU) and was appointed Lecturer in Mechatronics, which he currently holds.

Links:

• Download mp3 (11.0 MB)
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS

Transcript

Ron Vanderkley: Dr. Lei, we can start by asking you to introduce yourself to the listeners.

Dr. Lei Cui:  Sure. My name is Lei Cui, I’m a lecturer in Mechatronics. I’m with the department of Mechanical Engineering at Curtin University. I joined Curtin about two-and-a- half years ago. Before that I got my PhD from King’s College, London, and I did my second Post-Doc at Carnegie Mellon University.

Ron Vanderkley: Let’s talk about the research that you’re doing at Curtin University. I know there are two facets to it. We can start with the first one: robot rehabilitation.

Dr. Lei Cui: I worked on robot rehabilitation when I was doing my PhD. After I joined Curtin, I decided it was a good time to pick this research up again, because the Mechanical Engineering Department is now shifting its research focus to rehabilitation and medical engineering. In the last two and a half years I’ve developed a 3D printable hand exoskeleton for rehabilitation, in collaboration with a colleague. We patented a finger design just three months ago. The unique feature of this hand exoskeleton is that each finger has three active joints, and, so, three active outputs, but with only one actuator – there’s one input. It’s the first design of its kind in the world.

It’s a joint linkage, I don’t know if you’re familiar with the concept of linkage? We applied the same linkage to all five fingers, so each can be operated by one linear actuator, but has three output joints corresponding to the three joints in each finger. Another feature is the whole assembly can be printed out using a 3D printer. No assembly work needs to be done, it’s for mass customization.

Ron Vanderkley: That’s a general 3D printer, printing in any kind of polymer plastic?

Dr. Lei Cui: No, we need a high precision printer. We need high resolution.

Ron Vanderkley: It’s not a hobbyist or …

Dr. Lei Cui: No, it’s not, any low level 3D printer cannot do that.

Ron Vanderkley: You told us that it’s at the point of being patented or patented already, are you collaborating with any other organization?

Dr. Lei Cui: Yes, we’re now working with a company in the US, from MIT. They’re also developing this hand exoskeleton, but our design is much better than theirs so we’ve been talking about how they could buy our design, or we could just print some collaboration frames so we can work with them.

Ron Vanderkley: You also spoke about the second project that you’re currently working on?

Dr. Lei Cui: Another project is an upper limb, for arm rehabilitation. On this project I have been working with Kerry Allison, a professor at Curtin from the Health Science School. Mainly, we’ve concentrated on task-oriented training. When patients do training they don’t focus on each individual joint in terms of rehabilitation. Usually, they focus on the task. For example, they want to move the hand from point A to point B: they normally look at the target first then move the hand from A to B. They don’t focus on each individual joint.

So that’s the idea behind our design. The ideal training would be for the user, or the patient, to look at the target, move to it and it would have false feedback to test the patient’s performance.

Ron Vanderkley: So it’s a variable device?

Dr. Lei Cui: No, it’s not variable, it’s a hand infector type. Not an exoskeleton type.

Ron Vanderkley: The primary use for that is to teach stroke victims?

Dr. Lei Cui: Yes, mainly for stroke patients.

Ron Vanderkley: What stage are you at with that process?

Dr. Lei Cui: We have developed a prototype and, currently, I’ve got one PhD student working on this project, but he’s only at the first stage. Now, we are combining force torque sensing and EMG sensing to do a patient intention estimation, it’s the first step. Previous work has used a predefined trajectory, meaning that the subjects first perform the task and they record the predefined trajectory; so they’ve try to force or assist the patient to follow the same trajectory. Our idea was to do a patient intention estimation first and combine EMG and force sensing. So, we don’t rely on the predefined trajectory, we want to generate it on the fly. This is the first step.

The second step would be to apply adaptive impedance control to assist training, as needed.

Ron Vanderkley: Thirdly, you are working on a three directional platform, is that correct?

Dr. Lei Cui: Yes, a three dimensional platform. That’s also a collaboration between Professor Gary Alison and myself. Basically, it’s his idea; he’s been working on this project for many years. What he wanted was a platform that could be multidirectional for the patients. This platform destabilizes the user. So, the user will be standing on top of the platform, it will move and the user has to move his or her foot to regain stability. That’s the part we need to control well.

Ron Vanderkley: What’s the end goal of it? Why are you creating it?

Dr. Lei Cui: Professor Alison’s research focuses mainly on how to prevent old people from falling. To study human posture and balance, you need to provide a well-controlled multidirectional platform for patients. That’s why it’s pretty large: 600mm in diameter. A person with an average weight of 100kg can stand on top of it comfortably. Also, they can shift a foot and regain their balance; we’ve provided enough space for that, and also enough speed and acceleration.

Ron Vanderkley: The other area of expertise you’re looking at is Biomimicry, could you tell us where you are going with that?

Dr. Lei Cui: Biomimicry is a fascinating research field; it’s also quite a hot topic. If we take the normal approach, using mathematical modeling, it’s very hard to realize some functions, that’s why we take inspiration from nature. Because nature – fish and other animals – has evolved over millions and millions of years. I’m focusing on applying Biomimicry design to monitoring.

The first project I worked on was called Amphibious Robot. We took inspiration from amphibious centipedes because they have multiple feet. These feet provide buoyancy and actuation, our design was decided with this in mind. It’s a track driven vehicle, but at each point of the two tracks, we’ve fixed ballpoint blocks so that, when the robot is in water, they provide buoyancy and actuation. When it’s on land, it can also effectively drive the robot.

Ron Vanderkley: At what stage are you at with that?

Dr. Lei Cui: We have finished the first stage, that’s the mechanical design and the empirical study; the paper’s been accepted by the ASME Journal of Mechanical Design. Now, some of my students are designing autonomous navigation systems, using an android form that also provides sensors, an accelerometer, gyros and a video camera.

Another project is the Robotic Fish.

Ron Vanderkley: Yes, we’ve heard a lot about the fish.

Dr. Lei Cui: In fact, this robotic fish is quite fast. It’s the fastest robotic fish in the world in terms of body length, per second and we’ve achieved three body lengths per second. A review paper in 2012 held the previous record: 1.6 body lengths per second.

Ron Vanderkley: So, it’s a first here in Western Australia?

Dr. Lei Cui: Yes, definitely, we just haven’t published anything yet. We want to wait until we collect enough data and it’s accepted by a good journal.

Ron Vanderkley: The propulsion of the fish…Biomimicry, is that using actuators?

Dr. Lei Cui: Yes. In fact, those kind of fish are in [the local] Swan Canning River, they’re called Bream. It’s quite a popular fish in this river. We effectively morphed this fish. If you put our robotic fish in the river, it would be very difficult to discern which is the real fish. The appearance is the same and the size.

Ron Vanderkley: Is there a plan to monitor the schools of fish?

Dr. Lei Cui: Yes. Our idea is that our fish could mix with biological fish seamlessly, because they could be made to look real. We hope, in the future, to use it to monitor the fish population in the Swan Canning River, for example.

Ron Vanderkley: Would you be able to mount sensors on them to take salinity tests, for example?

Dr. Lei Cui: Yes. Now we’re studying the actuation of the fish; we hope to further improve the speed, that’s the first task. The second task is to develop electrical field sensing technology for the fish, because they are un-tethered, small in size and battery powered. Currently, the batteries last for about 45 minutes but we need to make the fish autonomous. We’ve taken inspiration from some fish using electrical fields to sense their environments. I’ve got one PhD student and several undergrad students developing this form of bio-sensing, using electrical field sensing.

[0:17:53]

Ron Vanderkley: That’s very fascinating and impressive. I haven’t read anything on that for quite a while.

Dr. Lei Cui: Thank you.

Ron Vanderkley: We’d be very interested to find out how that’s developing, once you get going on it.

Now, the question that we ask everybody is this: Where do you think robotics will be, say, in 10 or 20 years from now? What kind of direction do you think we are heading in?

Dr. Lei Cui: I think robotics covers a lot of areas: artificial intelligence, mechanical design, sensing technologies; many of them are a combination of these. In my opinion, currently, sensing technology is holding back development in robotics. I would assume that, in the next 10 or 20 years, more people will be focusing on sensing. In terms of artificial intelligence, IBM has built machines to beat world chess champions. Artificial intelligence to do a single task is, I think, quite mature, but to multitask? Developing robotic animals that behave like the real thing still has a long way to go, it needs 50 -100 years, I’d guess.

Ron Vanderkley: Obviously, we’ve seen lots of examples, but predefined scripts give the audience the idea that some robots are more intelligent than they are?

Dr. Lei Cui: I’ll give just one example. While I was working at CMU, on the DARPA sponsored ARM [Autonomous Robotic Manipulation] project, one of my tasks was on the robot arm to hold a key, insert it into a lock and then unlock the door. If you watch the video, it seems quite intelligent. It appears as if it can feel what it’s doing, but my approach was to use force torque sensing on the wrist of the robot and then to insert the key into the hole. It looks intelligent but, in fact, as you’ve said, it’s defined by algorithms.

Ron Vanderkley: To me, that always confuses people and detracts from the research, that should be the focus. It presents the wrong impression. Similarly, movies like Terminator, also paint the wrong picture.

Dr. Lei Cui: Yes, definitely. I think it would take 200 years, at least, for Terminator to be a threat.

Ron Vanderkley: So, I’ll wrap it up by saying thank you very much for taking the time to do the podcast, and we hope to chat with you again in the future.

Dr. Lei Cui: Sure, thank you.

All audio interviews are transcribed and edited for clarity with great care, however, we cannot assume responsibility for their accuracy.

In this episode, Audrow Nash speaks with Helen Greiner, CEO and founder of CyPhy Works and co-founder of iRobot, about CyPhy Work’s LVL 1 photography drone. The LVL 1 drone has six propellers that are angled up and rotated slightly, which allows the drone to fly without tilting; flying without tilting is significant because, Helen says, it makes the drone more intuitive to control, as well as removing the need for a costly and high-maintenance camera stabilizing gimbal system. 

CyPhy Works has an KickStarter campaign for the LVL 1 drone that ends June 18th, 2015. The Kickstarter video and a link to the campaign are below.

Helen Griener

Helen Greiner co-founded iRobot in 1990 and served as President until 2004 and Chairman until 2008. During her tenure, Ms. Greiner guided iRobot into its position as a global leader with the release of the Roomba, the PackBotand SUGV military robots. She built a culture of practical innovation and delivery that led to the deployment of 6,000 PackBots. In addition, Ms. Greiner headed up iRobot’s financing projects, raising $35M in venture capital for a $75M initial public offering. Greiner holds a bachelor’s degree in mechanical engineering and a master’s degree in computer science, both from the Massachusetts Institute of Technology.

Links

• Download mp3 (10.7 MB)
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• Link to LVL 1 Kickstarter campaign

Transcript

Audrow Nash: Hi, welcome to Robots Podcast. Can you introduce yourself?

Helen Greiner: My name is Helen Greiner and I’m the CEO and founder of CyPhy Works, the drone company.

Audrow Nash: Can you tell me about your career?

Helen Greiner: I saw a competition on the Discovery Channel of students building robots out of a kit of parts at MIT, and I decided I want to go there because those students looked like they were having a heck of a lot of fun building robots. I went to MIT, I interned at the jet propulsion laboratory, and then I did my masters also at MIT in Electrical Engineering and Computer Science. I was accepted to the PhD program and dropped out of it and started iRobot Corporation.

Audrow Nash: Now you are at CyPhy Works. Can you tell me a bit about the industrial drones that you make there?

Helen Greiner: We started at CyPhy Works with a very unique technology: a microfilament tether, which supplies power and communications, and [carries] video from the drones. With that, the drones have long, almost infinite times of flight because power is being supplied from the ground. And we’ve solved some of the communications challenges like being hacked or spoofed. We’ve built a drone that just goes up and stays up at 500 feet, 24/7.

Audrow Nash: You are releasing a drone you’re calling LVL 1. What are the primary applications that you expect the LVL 1 drone is going to be used for?

Helen Greiner: We’ve designed the LVL 1 as a consumer drone, so you can take it out and very practically look in your gutters for leaves, or, if a storm came through, look to see if a shingle blew off your house, or you can look at what’s going on in your neighborhood. And it’s also for fun. You can take it to that beach barbeque and get stunning photographs from a unique perspective. You can take videos of your kids out kayaking; you can take videos of your child’s soccer game.

There are just so many wonderful applications for drone photography, and I think it’s really taken the place of video cameras. Phones have taken a lot of the functionality, but there’s still a need to get great shots, and drones go beyond video cameras because you can get shots from up high. So you have unique perspectives, but you can also do rolling shots. Like if your child is on a bicycle or in a kayak, the drone can follow along with them. It’s better than a video camera in many respects because it can follow the action.

Audrow Nash: And it’s a consumer drone. Can you tell me about why you’ve made that transition from industrial and military to consumer?

Helen Greiner: We haven’t really transitioned to the consumer. We have a lot of very inventive engineers and drone scientists at the company and one of them, Kenneth Sebesta, came up with what we call “Level-Up Technology”. Most multi-rotors tilt into the direction of motion, and that means every sensor on them, including the camera, has to tilt with them. Companies have spent a lot of time building these fancy gimbal systems to compensate for that tilt.

What we’ve done is we’ve built a multi-rotor (a hex rotor) that just flies level. The way we’ve done that is by putting what we call a dihedral, which is the angle between the body of the drone and the rotors, we put an angle on that and we also give the strut a twist. A dihedral and a twist is how we get the level behavior. And of course it has to have a lot of fancy math and control code running on it.

Audrow Nash: How did you decide initially to make this drone?

Helen Greiner: Our drone scientist was working to make the Parc system, our larger drone, fly better in wind, and he was experimenting with twists on the struts. He was looking at the math behind it and noticed that if you put a dihedral as well as a twist you can get level flight or tilted flight if you want.

We quickly prototyped it, and our dronies started to fly it … and they loved it because it was such a better flight experience. We thought, “We’ve got to do something with this because this would be the right thing for a consumer drone.”

I really do believe this design is going to be the right design for consumers because it makes flying so much simpler. It’s very intuitive because it’s level flight. We pull the camera into the body, which means you’re just controlling the camera (rather than controlling a drone and then controlling a camera, which is kind of like patting your head and rubbing your stomach).

There are a lot of tech folks who like to do that, but it’s so much simpler if you just fly the camera. With the Level-Up Technology we actually have a very rapid yaw, which we don’t have on a regular quadrotor.

Audrow Nash: So it can rotate very quickly …

Helen Greiner: Yes, very quickly … and so now we can actually use that as your motion for the camera. It’s all come together as a very solid compact design, very rugged. We’ve talked to hobby shops, and they said that it’s never the drone that breaks; it’s always the camera gimbal. That’s because the gimbal is exposed and it takes the brunt of any hard landing; it’s also got wires flexing many times, and hobby shops spend a lot of time fixing people’s gimble system, and charging them for it. I think the LVL 1 drone will be a much more reliable drone.

Audrow Nash: This drone is significantly cheaper than most of comparable drones. Has that been accomplished largely by removing the gimbal system for holding the camera?

Helen Greiner: Yes, largely by removing the gimbal system, but we’re also making use of the smart phone as a controller. A smart phone makes a great controller because you’ve got a wonderful display touch screen, you have a Wi-Fi link already onboard, and it’s got a GPS in it. With the smart phone, many people already have all the elements you need for a drone controller.

Audrow Nash: Why six propellers?

Helen Greiner: The level of behavior does require six, and that’s because of the math that’s running inside it. Six also gives you a lot of other great features. For example, with the level of behavior we have, if one or some combination of two rotors breaks, the drone can still fly, which is very exciting.

Audrow Nash: If two of these propellers break it can still continue to fly, or is it just one?

Helen Greiner: Definitely one, and some combinations of two. [Greiner points to two propellers on the same side] If these two go out, you’re dead.

Audrow Nash: How is it controlled?

Helen Greiner: The primary mode of control will be the touch screen interface. It’s very easy to set an altitude and be able to drag the drone. You’re controlling the drone just like you would look at a picture in a map. People have already learned the workflow of that. You have a picture or a video coming back from the drone and you’re just controlling it, in a first person view within that screen. When you fly this, when you control the yaw, you’re just dragging across the screen, and it really feels like you’re tightly coupled to the robot. It’s very cool to feel it.

Audrow Nash: What about the follow-me behavior? How does that work?

Helen Greiner: We’re building on top of an open source project called Tau Labs, and Tau Labs already incorporates a follow-me behavior in the control code. We will have that when we release this, but user feedback so far on all the drones that have follow-me behavior is that it’s not that useful yet, and it probably won’t be that useful until you can have sense-and-avoid to make sure that it’s safe. Right now it’s really useful if you’re in the desert, on the ocean, or where there’s a wide open space. But we’d never want to use follow-me behavior today in your city park.

Audrow Nash: Speaking of sense-and-avoid behavior, does this have the ability to do any of that?

Helen Greiner: It doesn’t have sense-and-avoid yet.

Audrow Nash: Are there ultrasonic sensors, or anything for it to determine what’s around it?

Helen Greiner: No, not at the moment.

Audrow Nash: You have a new thing called “geo fencing” – can you tell me about that?

Helen Greiner: People who haven’t flown a drone before are a little intimidated for the first flight, and they read on the internet about how during someone’s first flight they run it into a tree or it goes off into the neighbor’s yard. We’ve created a very simple interface that let’s you walk a boundary and the robot will stay in that boundary and not go out of it so it can’t get in any trouble as you’re taking those first flights. But it can be also useful for the sophisticated user that wants to practice aerodynamic flight, so the drone doesn’t get away from you. It’s based on the Tau Labs code.

There are all kinds of features to make any polytope … any kind of a shape that you would like … and keep the robot in that. But that’s not what our users want. We’ve developed an intuitive interface, where you walk the boundary and it stays in that boundary. You don’t have to do any correspondence between a map on your cell phone and what’s happening in the physical world. You’re constructing the boundary in the physical world.

Audrow Nash: No punching in GPS coordinates?

Helen Greiner: No punching in GPS coordinates.

Audrow Nash: But this does rely on GPS to create the geo fence, correct?

Helen Greiner: It does, yes.

Audrow Nash: Is that challenging given that GPS can be fairly inaccurate unless you buy an expensive one?

Helen Greiner: You wouldn’t want to make your geo fence in an extremely confined area. We’re only as good as the GPS accuracy, but the GPS accuracy locally at a certain point in time is usually pretty good. It can vary over days and when satellites move from place to place, but the local time of GPS is usually reasonably accurate.

Audrow Nash: Can you use an RC transmitter to control this?

Helen Greiner: You can. We’ve put payload ports on the bottom of the robot, so you can take off the stumpy little feet and there are connectors and power that allow you to connect a radio receiver.

When you want to do acrobatics, you do need to turn off Level-Up mode because it’s going to keep you flying level and that’s not going to allow for fun loops and flips. But one of the great things about the Level-Up Technology is that you can have it on to get that great video, and then you can turn it off if you want to have large accelerations.

Audrow Nash: Can you talk a bit about using open source technology in a consumer product?

Helen Greiner: There’s a lot of work that’s already being done by several communities in making autopilots that work well. The cofounder of Tau Labs is also the inventor of the Level-Up technology, so it was a no-brainer for us to use the Tau Labs open source code.

Audrow Nash: Will you be doing software updates to the LVL 1 drone?

Helen Greiner: Yes, we plan to make additional features along the way.

Audrow Nash: Congratulations on the successful Kickstarter campaign. You’re now more than double what you wanted …

Helen Greiner: It’s been wonderful. We’ve established a great community that we think of as our community of support and a lot of the basic design is done. But there’s a myriad of design decisions along the way and we’re getting a lot of feedback on what’s important to our customers and what’s not. It was a great thing to do a crowd funding campaign.

Audrow Nash: You’re pushing into some stretch goals. What are you thinking to implement now that you’ve received a good bit more than you originally asked?

Helen Greiner: Our first stretch goal is a camera control pack. The camera control pack will allow things like time-lapse photography and burst mode photography.

Audrow Nash: For a drone with limited battery life, I’m curious how time lapse would work …

Helen Greiner: It just gives you more high-resolution images at certain time intervals. Like if the sun is setting you get really high resolution, we like to say a beautiful sunset happens one minute at a time. Also on the camera control pack there’s RAW photography mode and features to let you control exposure and aperture and other things that really appeal to enthusiastic photography buffs, and they’ve never had a drone where you can get access to the data coming from the camera before.

Audrow Nash: What have been some of the major obstacles with the LVL 1 drone?

Helen Greiner: I think the major obstacle, because we are a small company, has been resource allocation. We didn’t stop what we were doing; we haven’t “pivoted” as they say. We are continuing to go strong in the industrial realm but we also wanted to get this consumer product, so we made the decision to prototype it quickly. We had a very enthusiastic team put in their sweat equity and really push it through from the grassroots. And we got it up on Kickstarter where it’s just going so well, and we’ve had such an enthusiastic response from the community.

Audrow Nash: Wrapping up, what advice do you have for those who want to do a startup?

Helen Greiner: If you want to do a startup, just go for it. You do want to have a solid idea first, and you can develop that idea when you’re in school or when you’re working at another job. But once you have that idea and it’s something that you’re passionate about, I’d say go for it, because the worst case scenario is that it doesn’t work out.  And in this country … in the United States … it’s a badge of honor if you’ve tried it, even if it doesn’t work out. You can always go on to the next thing, which might be another startup or it might be going back to school. It could be anything … but at least you know you have tried it. And if it works out you will be living your dream.

Audrow Nash: Thank you.

Helen Greiner: Thank you.

All audio interviews are transcribed and edited for clarity with great care, however, we cannot assume responsibility for their accuracy.

The idea of SupraPeds is to increase locomotion stability by incorporating a pair of actuated smart staffs with vision and force sensing that transforms biped humanoids into multi-legged robots. Credit: Oussama Khatib, Stanford University; Shuyun Chung, Stanford University.

Anyone who has ever watched a humanoid robot move around in the real world — an “unstructured environment,” in research parlance — knows how hard it is for a machine to plan complex movements, balance on uneven surfaces or traverse levels.

Quadruped robots, like Boston Dynamic’s BigDog, seem to be more adept, but don’t have the ability to use their hands to open doors or pull people from rubble.

Humans have trouble balancing sometimes too — say when climbing a mountain or walking on ice. In many cases, people turn to a trusted and time-tested tool: the walking staff.

“Efforts to make robots emulate humans have ignored an important fact: whenever humans approach their limits, they augment their capabilities with a diverse variety of assistive tools,” said Shuyun Chung, a postdoc working with Oussama Khatib in the Stanford University Artificial Intelligence lab.

“Perhaps the most useful tool is the walking staff, which improves support, enables load redistribution and can also be used as a sensor to probe the stability of planned footsteps.”

Researchers at Stanford University have been developing a robotic platform they call SupraPed that uses “smart staffs,” inspired by walking sticks, to better balance and traverse terrain.

Working in disaster recovery situations, the SupraPed can use the staffs to explore the terrain around them, expand the range of movements that are possible and even communicate a sense of touch to a human at a remote site, who can use that data to plan the robot’s movements.

Their work is supported by a grant from the National Science Foundation through the National Robotics Initiative, an effort to develop the next generation of robots that work beside or cooperatively with people.

A diagram showing some of the technologies incorporated into SupraPeds’ actuated smart staffs. Credit: Oussama Khatib, Stanford University; Shuyun Chung, Stanford University.

The smart staff is no simple stick. It’s equipped with 3-D vision capabilities and tactile sensors that can assess the topography of the surface, the friction of the material, its ability to sustain weight and other information relevant to planning the robot’s next move.

Just as we might poke a rock to see how stable it is before we jump on it to cross a creek, SupraPed can use its smart staffs to test the ground before it takes a step.

But endowing SupraPed or other future robots with increased mobility isn’t simply a question of handing a robot a computerized stick. The Stanford team needed to create a suite of new algorithms and control mechanisms to allow the robot to incorporate and control the staff.

Among the challenges they had to overcome in the research was that of balancing a changing center of mass during an activity. This is difficult in rough, irregular terrain and more so with a system that uses input from four independent surfaces to balance and move.

The Stanford research team developed new mathematical formulations of the relationship between internal forces and movement control, allowing them to surmount existing problems and provide improved stability for the SupraPed.

Before developing a physical prototype of their system, the researchers first ran SupraPed through a series of simulated tests using the Simulation and Active Interface framework developed in their lab. The simulations placed SupraPed in series of challenging terrains–outcroppings of rubble–and tested how the robotic platform reacted when it encountered obstacles.

In each simulation, the robot plants its smart staffs, testing its firmness just as an expert hiker might, and swings its foot forward, moving from one balanced position to the next.

Using the algorithms developed by the Stanford team, it almost never falls.

The team presented their work at the IEEE International Conference on Robotics and Automation. The first prototype of smart staff has been designed and fabricated, and is being tested in the lab environment. The researchers hope that one day, the telescopic staff easily can be integrated with any human-size humanoid robot.

“Although research on rescue robots working in hazardous environments has made tremendous progress over the past decade,” Chung said, “we believe that SupraPeds will bring a whole new level of mobility and manipulation for humanoid robots.”

The SupraPeds also herald a future where robots, like humans, use tools to augment their abilities.

Investigators
Oussama Khatib
Shuyun Chung

Related Institutions/Organizations
Stanford University

Related Programs
National Robotics Initiative 

To manipulate objects, robots are often required to estimate their position and orientation in space. The robot will behave differently if it’s grasping a glass that is standing up, or one that has been tipped over. On the other hand, it shouldn’t make a difference if the robot is gripping two different glasses with similar poses. The challenge is to have robots learn how to grasp new objects, based on previous experience.

To this end, the latest paper in Autonomous Robots proposes a Sparse Pose Manifolds (SPM) method. As shown in the figure above, different objects viewed from the same perspective should share identical poses. All the objects facing right are in the same “pose-bucket”, which is different from the bucket for objects facing left, or forward. For each pose, the robot knows how to behave to guide the gripper to grasp the object. To grip an unknown object, the robot estimates what “bucket” the object falls into.

The videos below shows how this method can efficiently guide a robotic gripper to grasp an unknown object and illustrates the performance of the pose estimation module.

A paper in Nature Communications earlier this year reports on “bio-bots”. These tiny machines inspired by sperm, are a hybrid combination of live heart cells and a synthetic polymer body.

The new bots, developed by researchers from the University of Illinois and Arizona State University, are the first swimming micro-machines that mimic the flagellar movement of sperm to traverse the viscous fluids of biological environments. This means they can propel themselves onward, fired by the contractile power of heart cells. The bio-bots are just 2 millimeters long and basically consist of a head and a wire tail with the some live cells to make it functional. The small cluster of heart cells are grown at the intersection of the head and the tail. They are synchronized to beat together, creating a wave motion in the tail that can push the bot forward at a speed of up to 10 micrometers per second.

The bio-bot’s creators have also designed a similar micro-bot with two tails that can swim even faster; up to 81 micrometers per second. According to the team’s lead investigator, Taher Saif, this type of robotic phenomena is the first of its kind, and the team is not quite sure how the cells communicate to synchronize their movement. Saif explained that the additional tail allows the bio-bots to navigate. He predicts that future applications could include: chemical detection, light sensing or they could be programmed to reach a specific target for medical or environmental purposes.

While there is still much work to be done, this program marks the innovative first step towards a new realm of bio-hybrid machines. Saif teamed up with fellow University of Illinois researchers Brian J. Williams and Sandeep V. Anand, as well as Arizona State University’s Jagannathan Rajagopalan. Their work was completed as part of Emergent Behaviors in Integrated Cellular Systems (EBICS), a National Science Foundation-funded Science and Technology Center.

If you liked this article, you may also be interested in:

• Cell origami
• Micro-robotics and medicine: Interview with Toshio Fukuda
• Minimally-invasive eye surgery on the horizon as magnetically-guided microbots move toward clinical trials
• Crowdsourcing new strategies for cancer treatment: Towards swarming nanobots
• Medical robotic systems market to reach $13.6 billion by 2018

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

Empire Robotics, a Boston-based start-up, is beginning to sell their VERSABALL kits, a new-tech jamming gripper enabling adaptive gripping operations with a single inexpensive tool.

Filled with a granular material, in one mode it is squishy enough to envelop an object. Then, when a vacuum is created inside the ball, the granules get pulled together, solidifying around the object. The unique design enables the gripper to pick up a wide range of different objects, which can weigh up to 20 pounds. Early work was done in partnership with iRobot of Bedford, and funded by the Pentagon’s R&D arm, DARPA. Cornell holds the patent.

Grabit, a Silicon Valley new-tech gripper start-up, is working with customers on development projects to perfect their new electroadhesion technology. Electroadhesion is an electrically controllable adhesion technology that requires ultra-low power consumption. The technology was developed and patented SRI International. Grabit’s products include grippers and fixtures, case and box handling grippers, and smart conveyors for manufacturing and logistics.

Grippers – the hands of robots – are the physical interface between the robot arm and the work piece. Up until these two start-ups, there used to only be four types of grippers: vacuum, pneumatic, hydraulic and servo-electric (or, for the more technical reader, impactive, ingressive, astrictive or contigutive).

Often combined with clamping, gripper manufacturers abound, but Schunk, a family-run German company, has been at the forefront of the industry. They will soon be bringing to market a 5-fingered hand with sensors on each finger’s pads, for intricate work.

2022 update: A new, one-wheeled version of the Cubli has been developed. The one-wheel Cubli is a three-dimensional pendulum system that can balance on its pivot using a single reaction wheel, as it is shown in this article.

2014 update: My colleagues at the Institute for Dynamic Systems and Control at ETH Zurich have created a small robotic cube that can autonomously jump up and balance on any one of its corners .

This latest version of the Cubli (2014) can jumping up, balance, and even “walk”. This new version is self contained with respect to power and uses three slightly modified bicycle brakes instead of the metal barriers used in the previous version. We are currently developing learning algorithms that allow the Cubli to automatically learn and adjust the necessary parameters if a jump fails due to the deterioration of the brakes and changes in inertia, weight, or slope of the surface.

This robot started with a simple idea:

Can we build a 15cm sided cube that can jump up, balance on its corner, and walk across our desk using off-the-shelf motors, batteries, and electronic components?

There are multiple ways to keep a cube in its balance, but jumping up requires a sudden release of energy. Intuitively momentum wheels seemed like a good idea to store enough energy while still keeping the cube compact and self-contained.

Furthermore, the same momentum wheels can be used to implement a reaction-torque based control algorithm for balancing by exploiting the reaction torques on the cube’s body when the wheels are accelerated or decelerated.

Can this work?

The first step in creating the robot, therefore, was to look at the physics to figure out if a jump-up based on momentum wheels was possible. The image below shows some of the math to figure out the Moment of Inertia (MOI) of the wheel and full cube.

This mathematical analysis allowed a quantitative understanding of the system which allowed to inform design choices, such as the trade offs of using three momentum wheels vs. a design with a momentum wheel mounted on each of the six inner faces of the cube.

Another outcome of this analysis was a good understanding of the required velocities of the momentum wheel to allow the cube to jump up, and the torques required to keep the cube in balance. Both factors were critical for the next steps: Determining the required hardware specs.

Specs and hardware design
Given the required velocities and torques determined above, it was clear that the momentum wheel’s motor and gearbox would be a major challenge for creating the robot. Using the mathematical model allowed to systematically tackle this problem by allowing a quantitative analysis of the trade-offs between higher velocities (i.e., more energy for jump-up) and higher torques (i.e., better stability when balancing).

This mathematics-driven hardware design resulted in detailed specs for the robot’s core hardware components (momentum wheels, motors, gears, and batteries) and allowed a CAD design of the entire system.

Part of this step was the design of a special brake to suddenly stop a momentum wheel to transfer its energy to the entire cube and cause it to jump up.

The photo to the left shows an early design of this brake, consisting of a screw mounted on the momentum wheel, a servo motor (shown in black) to move a metal plate (in blue) into the screw’s path (in light brown), and a mounting bracket (in light brown) to transfer the momentum wheel’s energy to the cube structure. The current design uses a combination of hardened metal parts and rubber to reduce peak forces.

2D prototype
To validate the mechanics and electronics of the jump-up and balancing strategy and prove feasibility of the overall concept a one-dimensional version was built:

The results obtained with this 2D version of the cube were published in an IROS 2012 paper.

The final robot
Following successful tests with the 2D version, a full robot was built. The result is Cubli, a small cube-shaped robot, named after the Swiss-German diminutive for “cube”.

As you can see in the video, Cubli can balance robustly.

However, first jump-up tests showed that the stress resulting from a sudden braking of the momentum wheel led to mechanical deformations of the momentum wheels and aluminium frame. This made repeated jump-ups of the whole Cubli impossible without part replacement. It was therefore decided to tweak the structure and breaking mechanism to reduce the mechanical stress caused by the jump-up.

In addition to balancing, my colleagues are now investigating the use of controlled manoeuvres of jumping up, balancing, and falling over to make the Cubli walk across a surface.

Note 1: This post is part of our Swiss Robots Series. If you’d like to submit a robot to this series, or to a series for another country, please get in touch at info[@]robohub.org.

Note 2: This work was done at the Institute for Dynamic Systems and Control, ETH Zurich, Switzerland and was funded in part by the Swiss National Science Foundation (SNSF), grant number 146717.

Note 3: If you have questions, post them below and we’ll post answers.

Some more photos:

Thanks Gajan!

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