Transcript below

In this episode, Audrow Nash interviews several companies from the International Conferences on Robotics and Automation (ICRA) showcase. The companies span the following applications: mobile robots for military and commercial uses, warehouse solutions, robotic arms and manipulators, and robotic systems to assist surgery.

Companies are listed in the order of their interview with a picture of their booth at ICRA 2015. All photos were taken by Robohub and can be found here.

American Robot Company (AMBOT) specializes in advanced electronics, unmanned systems, autonomous vehicle solutions and intelligent platforms for research, commercial and military robotic applications.

Fetch Robotics provides warehouse solutions.

Intuitive Surgical designs and builds the da Vinci Surgical System for robotically assisted minimally invasive surgery.

Kinova designs and manufactures robotics platforms and components that are simple and safe under two business units: Assistive Robotics for  people with disabilities and Service Robotics for humans and robots working in the same environments.

Applied Dexterity seeks to advance the field of robotically assisted surgery by creating a research surgical robot that can serve as a standard platform allowing researchers to share their software and improvements.

Interviewees

Jens Hurley is a Senior Roboticist at the American Robot Company.

Michael Ferguson is a Co-Founder and the Chief Technical Officer (CTO) of Fetch Robotics. Previously, Ferguson was the founder and CTO of Vanadium lab, developing low-cost education and hobby robot controllers, and a Software Engineer at Willow Garage.

Simon DiMaio is a Senior Manager of Research and Advanced Systems Development at Intuitive Surgical. Previously, DiMaio was an Instructor of Radiology and Postdoctoral Research Fellow at Harvard Medical School.

François Boucher is the Vice President of Business Development at Kinova Inc. in Boisbriand, Canada. He received his MBA and a Bachelor degree in Physics Engineering from Laval University. After his graduation, he worked as the General Manager of a technology transfer and investment company before joining Kinova in its early days to develop the service robotics market. In 2014 and 2015, Kinova was listed as one of the 50 most influential public & private companies in the global robotics industry by the Robotics Business Review and one of the fastest-growing company by Profit 500 in Canada.

Andrew Lewis is a Roboticist at Applied Dexterity. Previously, Lewis was a mechanical engineering graduate student in the BioRobotics lab at the University of Washington. His work in the BioRobotics Lab focused on the development of surgical robots, dynamically evaluated gravity compensation, and electromyography controlled iRobot Roombas. Lewis earned his BS in Robotics Engineering from Worcester Polytechnic Institute in 2011. His interests include mechanical design, systems development, and ethics in robotics engineering.

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Transcript

Audrow Nash: Hi, can you introduce yourself.

Jens Hurley: My name is Jens Hurley. I’m a senior roboticist at AMBOT.

Audrow Nash: Can you tell me what you have set up at this booth?

Jens Hurley: Our booth is based on research mobility platforms. We’re doing a lot of ground robotics with a lot of autonomous function built into them.

Audrow Nash:This one right here, can you describe it a bit and then talk about its applications?

Jens Hurley: Absolutely, this guy is a ruggedized outdoor, high-speed, mobility platform. It weighs about 525 pounds. It can carry about 550 pounds, so it’s got a one-to-one payload ratio. It’s got four-wheel, steer independent suspension and four-wheel drive so it’s highly manipulative which makes it great for research applications. It also served in the military for a light and load application and medical evac. It has high-powered lithium ion batteries and a 8-10 hour runtime over difficult terrain.

Audrow Nash: How large is it and how tall?

Jens Hurley: Overall, I believe it’s about 52 inches long, about 42 inches wide and its ground clearance is about eight inches. The whole vehicle itself is about 19 inches from the ground.

Audrow Nash: It’s a large remote operated vehicle?

Jens Hurley: It can be. This particular one is set up for remote, a man in the loop. We also offer systems with autonomous functionality, like soldier following or vision based systems.

Audrow Nash: I see, and so it has these large carbon fibre capsules on its side. What do those do?

Jens Hurley: Those are actually our battery packs. They are quickly removable.

Audrow Nash: Why electric?

Jens Hurley: Silent operation, clean, quiet, overall a very nice function to have. A lot of applications in military require silent functionality.

Audrow Nash: What’s the max distance it could travel? Do you have an idea?

Jens Hurley: No, I couldn’t tell you the max distance. Runtime dependent about seven miles per hour for 10 hours, moving continuously.

Audrow Nash: 70 miles, okay. This vehicle looks like it has a very heavy suspension system.

Jens Hurley: It does.

Audrow Nash: Can you talk a bit about that?

Jens Hurley: Absolutely, it was developed to carry a high payload, heavy weight, under almost any conditions, so it’s got six inches of independent suspension at each corner which makes it contact with the ground. It’s very rugged, very durable.

Audrow Nash: How fast does it go?

Jens Hurley: This particular model has a top speed of about 40 miles per hour.

Audrow Nash: At those speeds why is good suspension valuable?

Jens Hurley: It’s key. If the payload’s too high – high centre of gravity – it’s just not a good thing to have at high speeds. The active suspension keeps the payload low, very stable, it’s physics.

Audrow Nash: Yes. Are there governments interested in this or are there other applications?

Jens Hurley: This particular robot here was developed for a military function, basically, to lighten the load of a soldier. The European space agency is using it for teleop, and universities such like John Hopkins and the Applied Physics Laboratory, are using it.

Audrow Nash: What do you mean by teleop?

Jens Hurley: It’s basically a man in the loop. So they’ll be operating from miles away to perform some sort of action.

Audrow Nash: Okay, so teleop and research applications. What about agriculture?

Jens Hurley: It’s something we’re looking into. There’s been a lot of interest here in agricultural purposes.

Audrow Nash: I see. What’s the purpose of the buttons?

Jens Hurley: It’s pretty standard that every robot comes with an emergency shut off: the red buttons. The rest of these are there for easy setup and control when you get it out of the package. A lot of the customers, when they get it up and going, move the onboard control system because they’re going to be controlling it from a distance.

Audrow Nash: I don’t really see how you put a payload on it, so describe it a little bit more. The wheels are a little bit taller than the body and the battery packs are a little higher than the body. Do you mount some sort of platform on it and then use the payload that way?

Jens Hurley: It depends on the application. A lot of payloads are being used just with manipulative arms and torsos and they do fit on the current payload space. If it’s something larger, like a palette size, we do offer mounting. There are hard mounting points located at each corner of the vehicle where we mount racks to elevate it up and off.

Audrow Nash: What about repair in the field?

Jens Hurley: Depending on what exactly has just broken down, it’s a very simple platform. Everything is open architecture, so it’s easy to get into, easy to repair. It’s a very straightforward, simple machine.

Audrow Nash: Are there standards in creating this? Do you use, for instance, one single size screw for accessing almost everything, or is there one tool to take apart most of it? Is there any modularity built in, so that you can quickly replace components?

Jens Hurley: Definitely. Most of the stuff is done in two metric sizes, M6 and M5, so it’s pretty much built together with the same hardware.

Audrow Nash: Thank you.

Jens Hurley: No problem.

Audrow Nash: Hi, can you introduce yourself.

Michael Ferguson: Yes, I’m Michael Ferguson. I’m the chief technology officer at Fetch Robotics.

Audrow Nash: Can you tell me about what you have set up at your booth?

Michael Ferguson: This week we are showing our new robot platform, Fetch. We’re letting people get up close and see the robot in person. Most people here at this show are particularly interested in the research edition of Fetch.

Audrow Nash: Can you tell me a bit about the robot and describe it.

Michael Ferguson: Sure. So, Fetch is a mobile manipulation platform, it has a mobile base that can drive around with a scanning laser in it to give it a view of the world and keep it localized. It has a torso that can rise up and down and attached to that we have a seven degrees of freedom arm. The gripper is a modularity point. Currently, we have a simple two-finger pincher on here.

Audrow Nash: What do you mean by modularity point, can you swap it?

Michael Ferguson: You can swap it, yeah. We have a standard specification on this, an ISO standard.

Audrow Nash: International Standards Organization?

Michael Ferguson: Yes. There are a number of vendors here at the show with really cool grippers that have, you know, sensors and all sorts of other stuff in them. And many of those will fit on our robot.

And then, lastly, the robot has a pan and tilt head, so that you can look in different directions, that’s carrying an RGB-D sensor. Colour and depth data are in 3D so you can see the whole world around you.

Audrow Nash: It’s a Microsoft Kinect or very similar?

Michael Ferguson: It’s a prime sense sensor, yeah, similar.

Audrow Nash: The robot has a wider base and that’s how it moves around. Then it has the centre part, that has the robotic arm and then above that is the vision, the depth sensor. What’s at the very base?

Michael Ferguson: It’s a scanning laser range finder.

Audrow Nash: Laser range finder? Is it only on a plane?

Michael Ferguson: It is on a plane, yeah. That’s actually manufactured by SIC.

Audrow Nash: Why seven degrees of freedom?

Michael Ferguson: Seven degrees of freedom give you really good dextrous manipulation. If you’re just picking things up off a table top, you probably don’t need a really dextrous arm, but if you want to reach into things, if you’ve got a complex cluttered environment, you really need the extra degrees of freedom there.

Audrow Nash: I see, and so it only has one arm and the arm is at the centre of the robot. Why this configuration? Is this so you can reach things?

Michael Ferguson: Yeah, there are two aspects to it. One is that two arms would make this robot significantly larger, heavier and bulkier. You want to navigate around human environments. We have skinny doors and things like that. The other is that very few applications really require two arms and, in many cases, if you did need a second arm, you just have a second robot come up and help and they can coordinate with each other. As far as the centre, really that’s for stability. There’s a very nice little tuck configuration for the arm that makes the platform extremely stable as the robot’s driving around – so that you can move quickly in the environment and not have to worry about your robot falling over.

Audrow Nash: So will this, primarily, be used for warehouse solutions to pick up products and put them into boxes to be shipped off somewhere? Is that what this has been designed for?

Michael Ferguson: We’re really designing to go into the logistics space, warehouse automation, pick and pack for items. There are videos on our website where you can see the robots doing those sorts of tasks.

Audrow Nash: If I’m not mistaken, the whole robot can rise up. Its spine kind of grows longer and that allows it to reach higher?

Michael Ferguson: Yes.

Audrow Nash: Okay, and how tall can it be?

Michael Ferguson: It gets up to about 1.7 meters or so. The exact specs are on our website. When you extend the arm all the way up you can still have some dextrous manipulability at about six foot.

Audrow Nash: You also mentioned that this could be a research platform. Can you tell me a bit about that?

Michael Ferguson: Yeah, we need a stable robot platform built on a operating system or ROS. We’re now making available our research edition, which has the robot hardware plus the open source side of navigation and some manipulation software, so that university labs can get a real jump-start. They don’t have to build robots by bringing pieces together. They have something that’s designed for mobile manipulation from the get go.

Audrow Nash: Thank you.

Michael Ferguson: Well you’re welcome.

Audrow Nash: Hi, can you introduce yourself.

Simon DiMaio: Sure, my name is Simon DiMaio. I work in our R&D group at Intuitive Surgical.

Audrow Nash: Now, can you tell me a bit about what you have set up at this booth?

Simon DiMaio: Sure, so this is called the Da Vinci Surgical system. It’s a system for minimally invasive surgery, for assisting surgeons to perform in a minimally invasive way. It does that by allowing the surgeons to control instruments inside the patient’s body from what we call a surgeon console. They can get very dextrous manipulation of surgical instruments while minimizing the invasiveness of the procedure for the patients. They fit these instruments through very small incisions in the patient’s abdomen, but still allow the surgeon to have control and dexterity.

To see the field in 3D, we have a stereo endoscope that allows the surgeon to see the inside, with very high quality HD images to control surgical instruments, like graspers and forceps and cutting instruments and electric quarter instruments, in a very natural way, almost as if they were inside the patient.

Audrow Nash: Now can you describe the Da Vinci a little bit.

Simon DiMaio: Absolutely, so there are really two main components in the Da Vinci. There’s the part that’s at the patient and this is where there are robotic manipulators for instruments that are inserted inside the patient. And that is a cart. It’s a mobile cart that carries four robotic arms. Usually, one of those arms carries the camera and the surgeon can control the pointing direction and the view that the camera sees. Then there are three additional arms that are usually used to manipulate instruments. Those can be for cutting, grasping, suturing and things like that.

Then, the other main piece is where the surgeon sits. They sit in an ergonomic console. You can think of it as kind of like a surgeon’s cockpit where they see the images from inside the patient. They see information from the system and they can interact and control by moving some really fancy joysticks, to control the movement of the instruments. They can interact with tissue from the other side of the room. Usually, the console is in the same room, connected to the rest of the system through a cable, but the surgeon controls the system from it.

Audrow Nash: The patient and cart, it kind of looks like a large hang man.

Simon DiMaio: Yeah.

Audrow Nash: It’s high, high above with the arms coming down.

Simon DiMaio: Correct.

Audrow Nash: It has four arms and each has a large tube that is inserted into the patient and that’s where the manipulators are and the camera.

Simon DiMaio: That’s right and that tube is called a trocar or a cannula and that’s the point of entry into the patient’s body. In order to create space for the surgeon to work in, the abdomen, or whichever cavity within the anatomy the surgeon is working on, would be inflated using carbon dioxide gas to create kind of a bubble. Those tubes that you referred to, the trocars or cannulas, have a seal on them, so that the carbon dioxide is trapped inside and maintained and so the instruments pass in and out of the body through that cannula, or trocar, through a seal.

Audrow Nash: I see, and so if they want different attachments, do they remove the inserted part from the arm and then swap in a new one?

Simon DiMaio: That’s correct. Yeah. They’re surgical articulated instruments that have wrists on them to mimic what the surgeon would want to do inside the body. Then there are a variety of end effectors or jaws that the instruments have. Some of them are for grasping tissue, like bowel, liver or kidney. Some of them are for cutting. Some of them are for suturing, so for handling needles. Those instruments are interchangeable on the system so they can be removed and replaced with different instruments depending on the task.

So, if the beginning of the procedure is a dissection, where you’re trying to expose tissues of interest, then you’ve got to remove some tissue and suture things back together. In each of those stages you would potentially use a different combination of instruments. So the system allows you to swap instruments during the procedure very quickly.

Audrow Nash: Is that the purpose of the fourth arm?

Simon DiMaio: The fourth arm could be managing retraction, for example, so it could be holding some tissue out of the way. You might be working underneath a liver or you might be working in an area where there is bowel and you might need to hold the bowel out of the way. It could be what’s called a suction irrigation instrument, that flushes the field with saline and then vacuums it out to manage blood and to keep the field dry and visibility good.
There are a variety of different things that you could use the fourth arm for. It could be used to manipulate an ultrasound probe. If the surgeon is looking at ultrasound images of the tissue and trying to see beyond visible surfaces in the endoscopic image, they might insert an ultrasound probe and manipulate that with the fourth arm. So it’s kind of an auxiliary arm that’s used for many different things.

Audrow Nash: I see.

Simon DiMaio: It can also be controlled from a second console so you can connect two consoles to the robot and have two surgeons.

Audrow Nash: When is that useful? Why?

Simon DiMaio: Training in one instance, if you have a proctor that’s training a mentor, a mentor that’s training a mentee, the proctor might be on one console and the trainee might be on the other. They can virtually swap instruments back and forth. So, a mentor might demonstrate a technique and then give control of the instruments to the trainee who can replicate those motions.

Another instance is if you have two experienced surgeons and they’re collaborating on a complex case, so that you can have all of the arms operating at the same time.

Audrow Nash: I see. How do you manage collisions between the arms? Does that happen?

Simon DiMaio: It does happen, the arm is working in very close proximity. Part of the design was to make the arms as skinny as possible so that they can work closer together. They’re pretty robust, so they can collide and they shouldn’t break.

Audrow Nash: Do they collide during surgery?

Simon DiMaio: They can collide during surgery and usually that happens when the surgeon’s trying to get the instruments very close together through a very narrow entryway. Some of the forces that occur during collisions are fed back into the console, so surgeons can feel some of them on the outside of the robot.

Audrow Nash: So there is some haptics.

Simon DiMaio: So there is some haptics. They don’t feel the very delicate interactions with tissue, but they can feel external collisions between the arms or perhaps with some rigid object. They get some feedback and they stop and investigate. There’s usually an assistant or nursing staff at the patient’s side that can warn them. But it happens quite often, and instruments work very closely together, it’s almost like if we were to stand side by side and our elbows were to knock against each other. It’s not a big deal, you keep going and that’s the nature of working in a confined space.

Audrow Nash: Now, can you tell me about the demo that you have set up at the booth?

Simon DiMaio: So we have a full Da Vinci system here and people are able to test-drive it. We’ve got a mannequin that has a simple plastic environment people can interact with. We’ve got some elastic bands so that they can get a sense of the intuitiveness of the motion of the arms. We also have a virtual reality-based simulator that’s used for training, basically a skills simulator that surgeons can use to learn how to tie knots and suture. They learn how to control the camera and the various instruments, and so are able to repeat the same skills and drills over and over again to develop muscle memory and familiarity with the system in a way that doesn’t require them to operate on patients or animals.

It’s a convenient way for people to use virtual reality models to hone their skills or to warm up before a procedure. So there are some surgeons that use the simulator like an athlete would do before a game. They might warm up and they might do some practice.

Audrow Nash: Interesting. Is there a certification procedure for doctors that are using the Da Vinci system in operations?

Simon DiMaio: There is a training path. I’m less familiar with that side of things but there is some training. I don’t believe that Intuitive directly certifies surgeons for using the system at their hospitals. That’s the responsibility of the hospital. But we provide some training, but we don’t train them on how to perform surgery, that’s something that they learn in med school, but we teach them how to use the tool and then hospitals decide how to certify.

Audrow Nash: What kind of feedback have you received from surgeons on the Da Vinci?

Simon DiMaio: Well, a variety of feedback. Obviously, there’s lots of interest in what they can do with it and what they can offer their patients. It’s a way of allowing surgeons to offer their patients an option for minimally invasive procedure where sometimes that’s not available. We get feedback related to the kinds of procedures that they can perform and the value of those procedures.

We also get feedback on the ergonomics of the system. For them, it’s being able to sit at a console in a fairly comfortable posture versus sometimes working in fairly awkward poses and configurations at the operating room table.
Obviously, there are lots of features that surgeons would like and so we get feedback on things that they’d like to see. It’s a work in progress, so we’re always interested in feedback; things that work well, things that they would like improved.

Audrow Nash: Thank you.

Simon DiMaio: You’re welcome.

Audrow Nash: Hi can you introduce yourself.

François Boucher: François Boucher, VP Business Development for Kinova Robotics.

Audrow Nash: Can you tell me what we have at the booth here?

François Boucher: Lighter, smooth, compact, efficient sets of service robotic arms. We have two different models, JACO and MICO with different ranges and size and weight.

Audrow Nash: First, what do you mean by service robotic arm?

François Boucher: Basically, it’s the opposite of industrial robotics. We’re not extremely fast and extremely precise, but we’re good at working around humans, in collaboration with them or with a human in the loop.

Audrow Nash: That means that they’re compliant to being touched, so if they bash into a human when it’s moving, it won’t hurt the human?

François Boucher: Yeah, exactly, but the arms are on top of all the features that we added: software and sensors. The arm itself weighs only five kilograms, including gripper control, so it won’t hurt anyone at that speed and that weight.

Audrow Nash: Very light, very cool. So how do you make them compliant? Is there a spring in them, like with Baxter?

François Boucher: No, it’s sensors in every actuator.

Audrow Nash: It generates a small electrical signal when you have force on the arm and it’s in the opposite direction?

François Boucher: Exactly.

Audrow Nash: How come you have a different approach? What’s the advantage to doing that over having like a spring in series with the actuator or pillow action?

François Boucher: Well, the fork gives us a safety feature but it also gives us a whole new range of applications, since the arm can now be speech controlled and not only just position controlled. With this whole new field of service robotics, that’s the kind of control that will be needed.

Audrow Nash: You mentioned two robots. What are they? Can you introduce me?

François Boucher: Yeah, it’s JACO and MICO. We started a company for assistive robotics, to mount robots on a wheelchair so that people can eat, drink, open doors, all by themselves. JACO was our first product five years ago.

Audrow Nash: How did you get its name?

François Boucher: It’s the name of the uncle of the founder of the company. He, himself, had muscular dystrophy. So, basically, you’re in a power wheelchair and you have limited motion in your arms. He was an inventor and he developed and built some sort of mechanical arm, with a cable from bicycle brakes and a motor from a windshield washer. It was our inspiration to start the company. His nickname was Jaco, so that’s why the robot’s named after him.

Audrow Nash: I see, and how did MICO get his name?

François Boucher: That’s an easy one. It’s a contraction of Mini and JACO so it turns out to be MICO.

Audrow Nash: Very cool. Can you tell me a bit about the grip and endefactors on these robotic arms?

François Boucher: Yeah, so for the assistive robotics purpose, we had to figure out how we could grip hundreds of different objects of different size and weight. We had to find a really versatile solution. We went with an under actuated gripper.

Audrow Nash: Tell me a bit about how it’s under actuated.

François Boucher: Basically, there’s one actuator per finger and, if you imagine, two tendons links with a spring.

Audrow Nash: I see. Okay.

François Boucher: So the great advantage with this gripper is that they are mechanically intelligent. We don’t have to worry much about which force we’re going to apply, how we’re going to grasp the object, since the flexible gripper adapts to its shape. So, with the precise force needed, we can grab an egg like we can grab a bottle of water.

Audrow Nash: You can grab an egg?

François Boucher: Yeah, exactly.

Audrow Nash: Surprising, that’s great. So, to grab an egg, do you have to tell it that it’s a soft object?

François Boucher: No, it’s really just a great mix of mechanical design and force imitation based on current. Sometimes people try to overdo things. We keep it simple and it works pretty well.

Audrow Nash: That’s interesting. Can you talk a bit more about that or is that IP?

François Boucher: It’s more experience than IP, I would say.

Audrow Nash: That’s kind of mind blowing for me. All right. So some of these don’t have any hands, robotic arms or graspers. What are you demonstrating there?

François Boucher: A lot of our arms are used in maintenance logistics, inspections or replacing a tool, a sensor or a camera. So the arm’s not only used for manipulation, but it can also be combined with a mobile mount platform to perform inspection and probing.

Audrow Nash: I see. Thank you.

Audrow Nash: Hi, could you introduce yourself.

Andrew Lewis: Sure, my name is Andrew Lewis and I’m a roboticist with Applied Dexterity and I’m here representing the Raven II and the Surgical Cockpit.

Audrow Nash: Tell me about the Raven II.

Andrew Lewis: The Raven II is an open source surgical robotics research platform. It was developed here at the University of Washington and then spun out into our start-up company, Applied Dexterity. We’ve helped to ship it around the world. Now there are about 18 or 19 labs around the world using Raven, or something similar.

Audrow Nash: Now, what stands out to me is open source surgical. Can you explain that a bit?

Andrew Lewis: Sure. So all the software is open source. It’s up on GitHub if you want to fork it and play around with it; throw it in your own version of Raven, if you’re that brave. The tooler face is also open source. We have a team, one of the original Raven teams that developed a Da Vinci adapter, and now we can use clinical Da Vinci tools on our robot and have access to their 150 plus tools without having to make them all ourselves.

Audrow Nash: Is most of the value you get from open sourcing this technology in low level parts of the software?

Andrew Lewis: Right. We’ve done all the low level parts and we’ve opened it all up so that researchers can look at anything they want. If they want to implement their own impedance control, or some other control techniques, they’re welcome to dive deep into the PID loops and what have you. You can just use some automation software, with some python scripts in ROS and, since all the source code is available to you, if something’s not working quite right you can change it yourself, or you shoot me an email and I’ll tell you how to change it, or change it for you.

Then, with the common platform, all of the schools or labs that have this robot can really lean on each other for advice on how to approach a problem.
If they’re having technical difficulties, they can check the forums and see that “oh yeah Harvard dealt with it a few years ago and they just unplugged this other thing.”

Audrow Nash: Is it largely research communities that are benefitting from this being open source?
Andrew Lewis: Yeah, so far we’ve distributed just to research labs to help us get in touch with hospitals about using this for research or training. But that would probably be a tighter, ready to roll, don’t-need-to-play-with-the-code version.

Audrow Nash: When we spoke about this a little bit ago, you mentioned that it’s not FDA approved.

Andrew Lewis: Right.

Audrow Nash: Can you talk a bit about what that means in term of its application for surgical, is it just a robot that is used in surgery first of all?

Andrew Lewis: The original version, the Raven I was used for Cholecystectomy, about a decade ago I guess.

Audrow Nash: I don’t know what that is.

Andrew Lewis: It’s gall bladder removal in a pig, and it was successful and I think that’s been the only real life surgery that’s been done with the robot. Harvard has started doing some work with it on time delay and tele-operation.
Since it’s not FDA approved, it means that, when a lab is looking to do some research with the platform that’s ready to go, they’re not paying for that FDA approval. The Da Vinci or another clinical systems is going run into millions of dollars because they’ve had to put time and a lot of effort into getting it approved.

Audrow Nash: That’s the advantage of it, the cost is kept down because it’s not FDA approved.

Andrew Lewis: Right.

Audrow Nash: I see. What’s kind of the goal do you have, where is the company heading?

Andrew Lewis: Our slogan is: “pushing the boundaries of surgical robotics.” The idea is to be able to make this platform available to research labs that are really pushing the boundaries and to support them wherever we can. Then we can make a lot of great connections in all these great labs.

And maybe try to license back some of the technologies that are coming out of the research community. And certainly learn a lot by just being around everyone, so we keep developing either surgical or tele-operated or autonomous systems, and gain huge advantages by being familiar with all these research topics.

Audrow Nash: Thank you.

Andrew Lewis: Sure.

ICRA 2015 Organising Committee

Ada Lovelace was the world’s first computer programmer, and heralded symbolic logic by demonstrating future applications for the universal computing machine that Charles Babbage proposed. She was exceptional in her era for her mathematical brilliance, but though she imagined future applications for a multitude of technological innovations, women at that time were not encouraged to speak about or publish their work, so Lovelace’s genius was appended as ‘notes’ onto the work of others and not seen as a major contribution in its own right.

The fact that the contributions of women such as Lovelace have not been celebrated until recently gives us cause to remedy the situation. Now in its third year, our list of ‘25 Women in Robotics You Need to Know About’ is both a shoutout and a call to look at what all these women in robotics have achieved! 

This has been a year of extremes for women in robotics. We celebrated the news of a phenomenal ICRA — one of the premier academic robotics events worldwide, and this year brought to us by an all-female organising committee. But we also despaired that IROS, equally esteemed in academic circles, featured not a single woman delivering a keynote, plenary, or participating in many of the key discussions, such as the futurist forum or the citizen forum. The DARPA Robotics Challenge (DRC) Finals 2015 were similarly lacking; of the 444 robot builders representing 24 robot entrants, only 23 builders were women (though some of the most successful teams at the DRC had female team members). Given how multidisciplinary the field is, and how many different skills are required, we need to celebrate women who are achieving greatness in robotics until we are seeing more parity.

This list showcases women in a wide range of roles and career stages, from the early stage entrepreneur/academic to those who are at their peak. We’ve also featured women working in a wide range of fields: women who are inspiring young Africans, trailblazing the field of surgical robotics, and helping us beautify ourselves using technology.

The honourees on our list of 25 Women in Robotics You Need to Know About – 2015 have been chosen for the resilience, perseverance and sheer brilliance they had to demonstrate to get to the top of their particular tree. Whatever your field, we hope that you find a woman doing great things in robotics to inspire you!

Many thanks to Robohub’s contributors for all their great suggestions. Here are 25 women in robotics you should know about (in alphabetical order) for 2015:

Nancy Amato Professor at Texas A&M Nancy Amato is noted for her research on the algorithmic foundations of motion planning, computational biology, computational geometry and parallel computing, as well as her leadership in broadening participation in the industry. Her 1998 paper on probabilistic roadmap methods is one of the most important papers on this field. Among other awards, she was made an IEEE Fellow in 2010. She has been  a member of the Committee on the Status of Women in Computing Research since 2000. She was also the Program Chair at ICRA this year.

Spring Berman Assistant Professor of Mechanical and Aerospace Engineering at Arizona State University Spring Berman is developing a system that would allow swarms of robotic machines to perform reliably in situations where there is little prior data about the environment they are operating in, and where GPS and radio communication are limited and unreliable. She is the recipient of the 2014 DARPA Young Faculty Award and the National Science Foundation (NSF) Graduate Research Fellowship.

Kate Darling Research Specialist at MIT Media Lab Mistress of machines, robot ethics and intellectual property, Kate Darling is a Fellow at the Harvard Berkman Center for Internet & Society and an Affiliate at the Institute for Ethics and Emerging Technologies. She writes for Robohub and IEEE and is passionate about the near-term societal impact of robotic technology, rethinking copyright, and patent law.

Fei-Fei Li Director, Artificial Intelligence Lab and Vision Lab, Stanford University (SAIL) Fei-Fei Li is working to solve some of AI’s trickiest problems: image recognition, learning and language processing. Her work at SAIL has helped to create software capable of recognizing scenes in still photographs — and accurately describe them using natural language. It marks a significant step forward for AI research, and could lead to applications ranging from more intuitive image searches to robots able to make autonomous decisions in unfamiliar situations.

Propa Ghosh Medical Director of Robotic Surgical Services at Hunterdon Medical Center Propa Ghosh is the first female medical director of robotic surgery in the USA. She is an ambitious, forward-thinking physician with the ability to combine surgical expertise, compassionate 360 degree care, strong business acumen, and organizational skills to help develop and foster initiatives from the ground up. She is committed to excellence in medicine through the propagation of advanced surgical techniques, staying at the frontier of skill and knowledge.

Cecile Huet Project Officer at European Commission Cécile Huet is Deputy Head of the Robotics Unit at the European Commission and instrumental to the success of the new wave of robotics projects funded under the multi-billion dollar Horizon 2020 funding programme. Her focus is on robots that can help people and drive applications forward.

Ayorkor Korsah Assistant Computer Science Professor at Ashesi University College | Co-founder of the African Robotics Network Growing up in Nigeria and Ghana, Ayorko Korsah has been a technology evangelist for as long as she can remember. In the US, she discovered her love for computer science and ultimately gained a PhD in Robotics and Artificial Intelligence from Carnegie Mellon University. Korsah co-founded the African Robotics Network (AFRON) in Ghana in April 2012. Ayorkor teaches robotics and computer science at Ashesi University College, and   coordinates the Ashesi Innovation Experience, a summer enrichment program that engages high school students in leadership, design, robotics and more.

Danica Kragic Professor, Computer Science, Vice Dean, School of Computer Science and Communication, Royal Institute of Technology (KTH) Danica Kragic studies how robots interact with each other, how they sense things, and how they see and grasp objects. One of her goals is to build future systems that interact with humans and their surroundings in a natural way. A specialist in computer vision and robotics, Kragic coordinates several EU-projects in the area. She is director of the Center for Autonomous Systems and head of the Computer Vision and Active Perception Lab at KTH. She is also recipient of the European Research Council (ERC) Starting Grant and the IEEE Robotics and Automation Society Early Academic Career Award. She is a member of The Royal Swedish Academy of Sciences and Young Academy of Sweden.

Kavita Krishnaswamy Ph.D. Candidate at University of Maryland Baltimore County Born in Tamil Nadu, India, and raised in Columbia, MD, Kavita Krishnaswamy is both a Ford Foundation Predoctoral Fellow and National Science Foundation Graduate Research Fellow. She has worked at the National Science Foundation’s Engineering Research Center – Quality of Life – Technology Center (QoLT) at Carnegie Mellon, the University of Pittsburgh and IBM Business consulting services. As a professional researcher with a severe physical disability, Krishnaswamy is motivated by a powerful, innate force: achieving autonomy and independent daily living through the advancement of technology. She develops robotic systems to provide assistance and increase independence for people with disabilities, to support transferring, repositioning, and personal care, with a focus on accessible user interfaces.

Hanna Kurniawati Lecturer at University of Queensland Hanna Kurniawati’s research interests include robotics, motion planning, planning under uncertainty, computational geometry applications, machine learning, and randomized algorithm. Before joining UQ, she worked with Nicholas M. Patrikalakis as a Research Scientist at the Singapore-MIT Alliance for Research and Technology. Recently she helped to create the Interactive-Ball (I-Ball) — a robotic soccer ball to improve social ball game participation for people with visual impairment that uses motion sensors, high-efficiency speakers and a small UQ-developed controller that can be programmed to suit the player.

Stéphanie Lacour Assistant professor at the School of Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) Stéphanie Lacour’s has pioneered the development of semiconductor devices that can stretch and still retain their electronic properties. Her goal is the fabrication of artificial skin — human-sized integrated circuits that can conform to a robotic or human body and communicate directly with the nervous system. Her research interests lie at the intersection of engineering, materials science and biotechnology. Lacour holds the Bertarelli Foundation Chair in Neuroprosthetic Technology at the School of Engineering at EPFL. A MIT TR35 winner in 2006, she also claims a University Research Fellowship from the Royal Society (UK), a European Research Council ERC Starting Grant, the 2011 Zonta award and the 2014 World Economic Forum Young Scientist award.

Cecilia Laschi Professor at Scuola Superiore Sant’Anna, Italy Cecilia Laschi’s research interests are in the field of biorobotics and she is currently working on soft robotics, humanoid robotics and neurodevelopmental engineering. She has been, and currently is involved in many National and EU-funded projects, she was the coordinator of the ICT-FET OCTOPUS Integrating Project, leading to one of the first soft robots, and the European Coordination Action on Soft Robotics, RoboSoft. She is a member of the IEEE, of the Engineering in Medicine and Biology Society, and of the Robotics & Automation Society, where she served as elected AdCom member, and is Co-Chair of the TC on Soft Robotics.

Hagar Marzouk Engineering student at Nile University Hagar Marzouk is leader of the “Walross” team in the Middle East and North Africa (MENA) and first female leader of a team in the Egyptian underwater competition, ROV, 2014. She took her team to first place in Minesweepers 2014, both at the national and international competitions.

Barbara Mazzolai Deputy Director at Center IIT Barbara Mazzolai is the recipient of the 2010 “Marisa Bellisario” Award for her scientific and management activities in the EU DustBot project for improving the urban hygiene and the quality of life of citizens. In 2013 she was also awarded the Medal of the Senate of the Italian Republic for her scientific activities in biomimetics and biorobotics. Her current scientific activity focuses on bio-inspired soft robotics. She has project managed many European projects, including PLANTOID, and is Associate Editor of Micro-Bio Robotics, Frontiers in Bionics and Biomimetics and a member of the Editorial Board of Soft Robotics and Applied Bionics and Biomechanics. She is also a member of the IEEE, the Engineering in Medicine and Biology Society and the Robotics and Automation Society.

AJung Moon PhD candidate at the University of British Columbia. AJung Moon is a Vanier Scholar and studies human-robot interaction and robot ethics. Currently she is a visiting student at the Learning Algorithms and Systems Laboratory (LASA) at the Ecole Polytechnique Fédérale de Lausanne (EPFL). She is a co-founder of the Open Roboethics initiative, an international roboethics think tank that investigates ways in which stakeholders of robotics technologies can work together to influence how robots should shape our future.

Yuki Nakagawa Founder and CEO at RT Corporation Fascinated by robots as a young child, Yuki Nakagawa has been involved in Japanese humanoid and home robotics for almost her entire career. Her life’s mission is to improve and enhance people’s quality of life through the use of robot technologies. Nakagawa’s company developed RIC90, which can wear an “animal suit” to achieve physical contact with people and can change forms. To date, RIC90 has hugged 3000 or more people and shared smiles all over the world. The company believes in the mantra: “No Physical Contact, No Service Robot” and wants to provide intelligence and sophisticated behaviour/motion animal-like robots that can work in close proximity to human beings.

Samantha Payne Co Founder at OpenBionics | Digital Marketer | Journalist Samantha Payne is co-Founder of Open Bionics, an award-winning startup developing low-cost effective and good looking bionic hands for amputees or those born without a hand. The company is now part of the Disney Accelerator, powered by Techstars. Payne, recently shortlisted for the ‘Entrepreneur of the Year’ at the Women and Business awards, focuses on building Open Bionics’ public profile, business model, network, and raising rounds. Open Bionics has won multiple awards for innovation, most recently the prestigious James Dyson Award and the RBA50.

Angelika Peer Professor at Bristol Robotics Lab Angelika Peer’s research interests are in human-robot interaction (HRI) with special focus on telepresence and teleaction systems, brain and body computer interfaces, physical/haptic HRI and human motor control. Her research combines methods originating from control, robotics, mechatronics, computer science, neuroscience, and psychology. She has won numerous awards and is chair of the RAS Technical Committee on Telerobotics, a Member of the RAS Technical Committee on Haptics, Secretary of the Eurohaptics Society, and a member of the program committee of several international conferences in the fields of robotics, HRI and multimodal human-system interaction.

Louise Poubel Software Engineer at Open Source Robotics Foundation When Louise Poubel was a little girl, she had as much access to computers as her brother, but her father refused to give her a remote-control car even though he gave her brother one. Now she is convinced that we should “give the girls the toys” — it’s important to let kids know it’s ok for them to pursue anything they like. Poubel, who grew up in Brazil, got her engineering degree in Japan and masters degrees in Poland and France; she is a truly global citizen. She now works on the robotics simulator Gazebo, a graphical user interface (GUI), at the Open Source Robotics Foundation, with the aim of knocking down complexity and price — barriers that she believes stand between robots and people.

Wendy Roberts CEO at future Skies | CEO at Five Elements In 2001, Wendy Roberts founded Future Skies, which primarily develops software for the US Department of Defense. In 2012, she created Five Elements Robotics to help further US personal robotics design and manufacturing. The company’s Budgee, a friendly robot that follows you around and carries your stuff, won two Gold Stevie Awards in 2015: Technical Innovation of the Year at the American Business Awards, and New Product Award for Consumer Electronics. Roberts is currently CEO for both Future Skies and Five Elements Robotics. Five Elements is the only commercial US firm manufacturing personal service robots domestically. Based on her work with the military, Roberts has made it part of Five Elements’ mission to serve the disabled and the aging.

Noriko Takiguchi Journalist and author Noriko Takiguchi covers the business and technology of robotics for various newspapers and magazines in Japan, including Newsweek Japan, Diamond Online, Nikkei BP, Toyo Keizai, Newspicks, President magazine, Harvard Business Review Japanese version and AXIS. Her most recent book on the culture of Silicon Valley — Why is Garbage Not Sorted in Silicon Valley? (President,Inc., 2008) — is a collection of her weekly columns on the Nikkei BP website. She has also translated into Japanese books such as Bringing Design to Software and Engineer Imagines. Watch for her at robotics conferences and trade shows around the world.

Pree Walia CEO & Co-Founder at Preemadonna Entrepreneur, risk-taker, beauty junkie, and business school graduate, Pree Walia has combined a political career with leading teams at early stage hardware startups, most recently starting her own venture at the intersection of beauty and tech. Walia is co inventor of the Nailbot — a nail decorating robot that lets you paint custom designs on your fingernails — and winner of the 2015 Robot Launch international startup competition. She supports causes that empower women and young girls.

Jing Xiao Professor at University of North Carolina at Charlotte Jing Xiao’s research spans robotics, haptics, and intelligent systems. She is Site Director of the US National Science Foundation Industry/University Cooperative Research Center on Robots and Sensors for Human Wellbeing. She has over 130 publications in major robotics conferences, journals and books, and holds one patent. She is an IEEE Fellow and has been elected twice as an AdCom Member of the IEEE Robotics and Automation Society.

Yao Zhang Founder and CEO at Roboterra Yao Zhang’s company, Roboterra, makes tool kits that allow students to tap their imagination and creativity to design and build their own robots. Zhang earned her Ph.D. in Education Economics at Columbia University before working for McKinsey and Co. and the Soros Foundation. She has been working in the fields of education and technology for more than a decade, and won a “Best 10 Global Citizen Service Award” from the US State Department in recognition of her outstanding work in educational innovation.

ICRA 2015 Organising Committee – In 2015, the IEEE International Conference on Robotics and Automation (ICRA) — one of the world’s largest robotics conferences — had an organising committee made up entirely of women. In order to broaden the field, early career women who may be less well-known outside of their specialties were selected to serve alongside senior women on the 2015 committee. This showed that there are women experienced in every aspect of conference organisation that could be part of future committees. ICRA may well have set a precedent; leading annual Computer Vision event, CVPR, is planning to follow suit next year with a female only committee.

Then and now

The celebration wouldn’t be complete if we did not share some of the recent achievements by women from our 2013 and 2014 honour rolls:

• Cynthia Breazeal has since launched Jibo with a massively successful crowdfunding campaign
• Helen Greiner has launched and successfully crowdfunded the LVL 1 Drone and keynoted at ICRA 2015
• Radhika Nagpal was named in 2014 among Nature’s 10 (scientists and engineers who made a difference).
• Lynn Parker chaired ICRA 2015 and was appointed as Division Director for the Information and Intelligent Systems (IIS) Division in the Computer and Information Science and Engineering (CISE) Directorate at the National Science Foundation (since January 2015).
• Melonee Wise launched Fetch Robotics
• Ekaterina Bereziy’s ExoAtlet started clinical trials.
• Ayanna Howard  launched Zyrobotics and was a finalist in Robot Launch

Know of any great women in robotics who should be on this list next year? Check the lists from our previous years (2013 and 2014), and feel free to leave your nominations in the comments section below, or email us at info [at] robohub.org.

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

• 25 women in robotics you need to know about (2013)
• 25 women in robotics you need to know about (2014)
• Five women robotics experts from Silicon Valley share their stories
• Steffi Paepcke on Robots, women and design: Opportunities abound

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

In this new lecture series, controls expert Brian Douglas walks you through key concepts in control system theory. Focused on making control theory accessible and intuitive, this series is for anyone who wants to relate control concepts to robotic applications in the real world. 

Presumably you are a human, an as such you have become really good at manipulating your environment by using your hands. You have these incredible actuators that allow you gently and nimbly grab an object, hold it tight, and then move it to a new location. So why wouldn’t you use your hands to grasp everything, and why wouldn’t you build a humanoid robot with hands so it could be as capable as you?

Well, this type of manipulation is called prehensile manipulation — or gasping manipulation — and it’s reasonable to expect a robot to be able to interact with the environment in this manner. However, you may not realize that as a human you are also performing a huge amount of non-prehensile manipulation everyday, both with and without your hands, such as pushing a swinging door open at the store, typing on your keyboard, or walking on two legs.  

A robot that is expected to do well in our human built environment would need to be good at non-prehensile manipulation as well as the prehensile sort, and unfortunately it doesn’t always have a straightforward solution. The difficulty of getting a robot to move like a human was demonstrated at the DARPA Robotics Challenge (DRC), where the robot contestants were expected to perform activities like driving a car, opening a door, and closing a rotating valve. The robots successfully completed a number of very impressive feats, but it was also obvious from the number of robots falling down that we have a long way to go in our efforts to build a human-like robot; not the least of which is building agile bi-pedal robots.

I was recently at the International Conference on Robotics and Automation (ICRA 2015) and I came across a very unassuming robot performing continuous non-prehensile manipulation of a ball around the edge of a butterfly-shaped link. Using a camera to sense the position of the ball, and a motor for actuation, the robot was able to continuously spin the butterfly shape while keeping the ball perched on top. Maksim Surov and Leonid Paramonov developed the algorithms that made this possible, and not only was their display mesmerizing, but their formalized approach to solving these types of problems could go to advancing our ability to generate continuous stability for an agile walking bipedal robot.

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:

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• 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, Audrow Nash interviews Sangbae Kim, from the Massachusetts Institute of Technology (MIT), at the International Conference of Robotics and Automation (ICRA) 2015. They speak about an electrically-powered quadruped called the Cheetah 2.

Sangbae Kim

Prof. Sangbae Kim is the director of the Biomimetic Robotics Laboratory and an Associate Professor of Mechanical Engineering at MIT. His research focuses on the bio-inspired robotic platform design by extracting principles from complex biological systems. Kim’s achievements on bio-inspired robot development include the world‘s first directional adhesive inspired from gecko lizards, and a climbing robot, Stickybot, that utilizes the directional adhesives to climb smooth surfaces featured in TIME’s best inventions in 2006. The MIT Cheetah achieves stable outdoor running at an efficiency of animals, employing biomechanical principles from studies of best runners in nature. This achievement was covered by more than 200 articles. He is a recipient of King-Sun Fu Memorial Best Transactions on Robotics Paper Award (2008), DARPA YFA(2013), and NSF CAREER (2014) award.

Links:

• Download mp3 (20.1 MB)
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• MIT Biomimetic Robotics Lab

Transcript

Audrow Nash: I’m at ICRA 2015 with Professor Kim from MIT. Professor Kim, would you introduce yourself?

Sangbae Kim: I’m an associate professor in Mechanical Engineering at MIT. I’ve been working on bio-inspired robots focusing on locomotion capability.

Audrow Nash: What is the goal and motivation of your research?

Sangbae Kim: Our motivation is to be able to develop a robot that can move around in a dangerous environment like Fukushima or a building on fire, so that we can send in a robot without worrying about putting a human in danger. Robotic technology stems from manufacturing applications, but in these applications robots don’t need to move in an unstructured environment; they are mostly moving on a rail or not moving around at all. We need a robot that can actually move around unstructured environments.

We’ve been focusing on locomotion capability because that’s currently a missing component in robotics technology. With the MIT Cheetah robot we developed a lot of component technology specifically for locomotion. Right now we’re moving a little beyond just locomotion or walking – we’re trying to combine this with vision so the robot can actually navigate by itself.

Audrow Nash: Let’s talk a bit about the Cheetah 2. Can you describe it for us?

Sangbae Kim: Cheetah 2 is the second generation of MIT Cheetah. We started five years ago, and we envisioned developing an electrically powered legged robot, because there was no electrically powered robot that could run or jump at that time. Most of the electrically powered robots out there are very slow. Boston Dynamics developed their hydraulic version; it’s vastly successful but very noisy and very inefficient. We did some investigation into the area and found huge potential. We started developing our all-electronic components … electric motors, electric driver amplifier, driver motor … and the locomotion algorithms about five years ago.

Cheetah 2 is the latest version of the Cheetah, and it has a custom motor designed by our MIT team.

Audrow Nash: Starting from the very general  … it looks like a cheetah. It’s a quadruped …

Sangbae Kim: Yes, it looks like a quadruped; we modeled it on a cheetah but it looks quite different from a cheetah. It’s really hard to be close to an animal. If you look at the many quadrupeds that run really well, their morphologies are not that different. We don’t actually have that exotic flexible cheetah spine.

Audrow Nash: It’s a rigid back?

Sangbae Kim: Yes, it’s a rigid back right now.

Audrow Nash: There’s one joint on the back?

Sangbae Kim: Cheetah 1 had this joint with a differential power drive, but we actually designed that and used it in Cheetah 2. So that mitigates the stiffness in the structure integrity. Cheetah 2 is a rather simpler design but is more capable. Cheetah 2 can run outside and it’s about 33 kilograms and about a meter long, about 80 centimeters high. It’s like Big Dog in size and it keeps on running up to 6 meters per second without a vision sensor. We believe that with the vision sensor it can go up to 10 meters per second.

Audrow Nash: What would the vision sensor do?

Sangbae Kim: It will detect the ground height more accurately. So far most running is done without having any vision, which means you’re robot is blind. Basically you close your eyes and you run as fast as possible, and it’s much more dangerous. It’s very important to have the contact angle of the leg be accurate, and as you get faster and faster that accuracy requirement becomes severe. With blind control we could get only 6 meters per second, but the mechanical capability of the robot is way beyond that. I think 10 meters per second, no problem.

Currently the Cheetah has vision sensors; we have a LIDAR that can detect an obstacle and it will adjust its step to jump over it properly, autonomously.

Audrow Nash: What are the differences between Cheetah 1 and Cheetah 2?

Sangbae Kim: There are some differences. Cheetah 1 mostly runs on a track with some support, whereas Cheetah 2 is fully 3D. Cheetah 1 is held in a plane so it can’t fall sideways but it can go only up and down and rotate. It’s more like a 2D robot. And its motor is less powerful because it’s a commercially available motor. It also has an exotic spine structure so it can actually use it’s spine in a galloping gait. There are some difference in the motors in the legs, and a big difference in the algorithms.

Audrow Nash: Can you talk a little bit about that?

Sangbae Kim: In Cheetah 1 we use very simple algorithm; we define a trajectory and then apply some sort of impedance, like vertical impedance, stiffness and damper. We just follow the trajectory based on the impedance, which is just a push and tracking gain.

Cheetah 2 is completely different; it does not rely too much on position tracking. As soon as the feet hit the ground, the ground stance control is not heavily dependent on position tracking, we are mostly prescribing force. We call that impulse planning.

In locomotion the fundamental requirement is that we have to fight against gravity. You don’t want to fall; you have to keep your body up. Then you’re constantly losing momentum by gravity. If you’re standing, gravity pulls you down so you need to generate the same amount of force to cancel that. If you’re running, you need to generate that same amount of impulse in a shorter amount of time because you’re staying on the ground a much shorter time than when you’re standing.

We calculate how much impulse I need to generate each step, depending on my speed, depending on my gait, and we actually prescribe that force profile to master our impulse on each step. That’s a basic control algorithm, so during the stance, force control is dominating whereas Cheetah 1 is still position tracking with this virtual spring control.

Audrow Nash: So for Cheetah 2 you’re planning how hard you’re pushing the foot into the ground … how much force you’re doing?

Sangbae Kim: Yes, we give some sort of force profile as a function of time.

Audrow Nash: And it’s time-dependent, the rate that it’s running?

Sangbae Kim: It’s time dependent, yes.

Audrow Nash: The DARPA Robotics Challenge involves humanoids. What are the advantages of using a quadruped over a biped?

Sangbae Kim: I’m a strong believer in quadrupeds. I think bipeds are just inherently less stable because we have a smaller polygon and the numbers of legs are less. In a quadruped you can stand with the one leg up, and you still have a tripod. The majority of the mammals in the world are quadrupeds. It’s hard to draw evolutionary reasons, but they have much more versatile capabilities.

Of course the human has amazing capabilities to go over rough terrain but they can become quadruped; if you’re going rock climbing and then you see those enthusiasts that are running off, and jumping off buildings …

Audrow Nash: The Parkour people?

Sangbae Kim: Yes, that’s actually quadruped locomotion, not biped. I think quadruped has a huge potential of becoming a major mode of locomotion. If you see the DARPA Robotic Challenge, I think you will see a lot of teams actually use four legs or some sort of wide support polygon to ensure there’s stability.

Audrow Nash: Can you talk a bit about the actuators on the Cheetah 2?

Sangbae Kim: That’s probably the one component we spent the most time on and probably why I started this project. In leg locomotion there’s a predominant model called a ‘spring-loaded inverted pendulum’, and it’s a very remarkable model that has enlightened a lot of researchers.

Audrow Nash: Can you describe it?

Sangbae Kim: It’s like a poker stick, basically. If you look at human running or animal running, it’s very complex; there are so many joints and a lot of pieces are moving in different ways …

Audrow Nash: Elastic tendons and everything  …

Sangbae Kim: Yes. Then these bio mechanic researchers in the late 70s and 80s found that, even though there are very complex and multi-body systems interacting with the ground, the bottom line is that your center of mass … your final trajectory during the stance … pretty much looks like a poker stick. It’s as if the entire leg becomes a spring; the system is a mass with a spring leg bouncing off the ground, and that describes pretty much the fundamental mechanics of most runners. It’s basically a reduced model of running, but it surprisingly covers many species and many type of running.

People started to realize, “Wow, so I don’t need to design these 20 muscles and 7 jointed legs to mimic human or animal running. You can just have a spring that takes care of everything.” This is theoretically true, but stability wasn’t very good, so a lot of people were trying to mimic this spring.

That’s when we started building the hypothesis that we’re not trying to control our leg to be like a spring; we’re probably controlling force. And that’s why we started developing this actuator, which has a very small gear ratio and a very specialized motor that can generate high torque per mass. We’re trying to maximize the force bandwidth; how quickly you can change force and at the same time maximize the transparency. Transparency means the mechanical impedance between the actuator force to the end effector …basically the electromagnetic component, which is an electric motor between the stator and the rotor. If you count all the masses and the frictions and inertia from there, all the way to the foot, that inertia and friction will mitigate the force transmission.

Audrow Nash: So some force is lost by the arm or the leg moving?

Sangbae Kim: Exactly. If you look at 99% of the robotic systems in the world, including manufacturing robots, that transparency is nearly zero. If somebody pushes the robotic arm, the motor cannot feel that, which means your motor cannot actually control force properly at the end effector. So what’s the solution? They add force sensors and then get the feedback. But that’s a very dangerous idea when the robot actually interacts with the ground because it’s non-collocated sensing.

Audrow Nash: Can you explain non-collocated sensing?

Sangbae Kim: Non-collocated sensing is when you’re trying to control the force at the end effector but your force is transmitted far away and there are dynamics between them, or there is delay between them. If you try to close the loop through this non-collocated sensing, there’s going to be inherent instability. It’s a very well known phenomenon. In the 90s, people showed how to develop better controllers to deal with contact instability by developing different control algorithms.

Audrow Nash: And so you make it elastic, in a sense, through the spring?

Sangbae Kim: We make it collocated. We don’t sense anything at the end effector; we sense through the actuator and then minimize the transmission. It turns out that is actually much closer to animals because from our muscle to our end effector, there’s almost no transmission and your Golgi tendon is combined with your muscle, which is a force sensor, and that can sense pretty much almost everything in the end effector. It’s not well proven, but think of when a human is running and balancing how much we are relying on the skin force sensor …

Audrow Nash: So it’s mostly muscles as sensors?

Sangbae Kim: Mostly muscle force for perception. Of course the skin sensor should detect the ground, in an “I know my foot is on the ground” kind of cue … but I don’t think the actual accurate measurement force should be done in the muscle. Imagine you have a different type of shoe on, or you tie your shoe laces a bit stronger today, and then you have to adjust your step … that probably doesn’t make sense right? In our case it’s a very practical reason. Non-collocated force sensing is not practical or feasible especially when we need high gain and high performance. Any force sensor in the foot will be destroyed in less than 30 minutes.

Locomotion is surprisingly violent. With the MIT Cheetah, peak force is about 500-600 Newtons. That’s happening 4 times in a second. This means you’re holding your robot and hammering the end effector at 4 hertz at 500Newtons, which is a 50 Kilogram force.

Audrow Nash: And how do you not strip your gears with all of this?

Sangbae Kim: That’s the key of our low-impedance transmission.

Audrow Nash: Low-impedance in what sense?

Sangbae Kim: Low inertia, low friction, where there’s no stiffness. The inertia and the friction is most of it.

If you’re going back to the conventional robotics there’s like 200:2 gear reduction, a special harmonic drive, and those cause enormous inertia. If you do that the robot is going to break somewhere. Those forces cannot really transmit. In our case your rotor can just give, so our electromagnetic interaction will be the only one that receives the force. We can even regenerate power, which is how we save energy when we’re running.

Audrow Nash: So a spring is in series with the motor …

Sangbae Kim: We don’t have a spring in our system.

Audrow Nash: But the tendon system that you have …

Sangbae Kim: No, those are harder than steel. There’s no single mechanical compliance in our system, except for the paw … the foot has little rubber pads to minimize the impact of vibration … but beyond that part, everything is rigid.

Audrow Nash: Where’s the energy storage that you’re talking about?

Sangbae Kim: All the energy can transfer to the motor side because there’s very little inertia, very little impedance. The motor will regenerate the energy and then restore it to the battery. It’s pretty much like electrical wheels. When they brake, when they do negative work and energy goes back to the battery. That’s the key element: maximizing transparency, which makes our force bandwidth maximum at the same time that it makes it inherently much more robust against impact, because the energy is restored rather than just breaking the gear.

Audrow Nash: So in our skeletal system, we do a little bit of energy storage because of the elastic component to our composition?

Sangbae Kim: That’s very different, yes.

Audrow Nash: But you are taking the force that’s generated back and instead of storing it in a tendon system it’s going back to the battery.

Sangbae Kim: Exactly.

Audrow Nash: Are there conversions lost in this?

Sangbae Kim: There are some conversion losses, yes.

Audrow Nash: But you’ve shown in your talk the efficiency of this system, and it’s remarkably efficient and comparable to biology.

Sangbae Kim: Our robots are far more efficient than most of the robots …

Audrow Nash: Could you place the efficiency of the Cheetah 2 compared to other animals and robotics systems?

Sangbae Kim: The cost of transport is a proper measurement of the efficiency in locomotion, and human is at about 0.3, which is pretty good. One thing I need to mention though is, this number is dependent on mass. For example, horses are better than humans, and rats or dogs are worse than humans because they’re lighter. So bigger animals tend to have a smaller number and cheetahs are like 0.4 and our robot is 0.5-0.45.

Audrow Nash: An actual cheetah is 0.4, and your cheetah is too?

Sangbae Kim: An actual cheetah is 0.4, and our cheetah is 0.45. It’s nearly same. The Honda ASIMO, which is one of the most remarkable robots, is at 2, so about 7 times worse.

Audrow Nash: How about Big Dog?

Sangbae Kim: Big Dog is at about 15.

Audrow Nash: With its hydraulic actuators, it’s at 15?

Sangbae Kim: Yes, it’s about 30 times worse than the actual animal. They’re heavier so they need to compare with their matching animal … I don’t have the exact number.

We’re nearly matching animal efficiency. I think this is because of our unique actuation system. The major cause for this efficiency is probably not regeneration, but light transmission. You have far fewer components and you lose very little through the transmission. That allows you to move fast.

As you mentioned, animals and humans should be better because they’re restoring energy; they should be more efficient. But that’s not really true because animals are more transparent in a sense. If I push you, and you relax your muscle, you can make your muscle impedance almost zero. Which means when you’re storing energy in the muscle tendon, your muscle should actually hold the force. Muscle is very good at holding force but it still needs to consume energy. Which is same for our system, but we can regenerate the energy whereas the muscle cannot.

Another thing is that the animal efficiency of 0.4 I mentioned is a measure through the metabolic energy, which is measured through the oxygen consumption and estimated energy you need to consume. This involves so many other processes, and then we’re comparing with our electricity consumption, which is technically not fair in my opinion because electricity is a refined source of fuel compared to fat or the sugar we consume. There’s a lot of inefficiency energy loss though this process. They break down the sugar to make ATP, and if you measure efficiency from ATP consumption to mechanical it’s probably a different story. I think that if you use an electric motor you should beat any animal by a factor of 2 or so.

I think our robot is not nearly optimized, and this is one data point we just built. We didn’t actually put any effort to make the robot more efficient. We paid attention to those principles when we designed it, so that’s probably one of the reasons it’s efficient, but we didn’t add any components or mechanisms optimized for algorithms to make it more efficient. I believe that we can increase efficiency by a factor of 2.

Audrow Nash: How?

Sangbae Kim: A couple of things like control optimization  …

Audrow Nash: What do you mean by that?

Sangbae Kim: You can actually optimize for your energy consumption. Our controllers are not optimized at all, so we haven’t studied that yet. In the MIT Cheetah, the major source of energy loss is caused by force generation, which means it’s just torque generation. This is very consistent with the biomechanics data … animals consuming 70% of energy in just producing force, which means that your energy is not going to be proportional to your mechanical work. Mechanical work is a bad metric in locomotion because we do a lot of negative work and then we don’t know how to actually deal with those negative work.

In MIT Cheetah it’s very easy because we know how much energy is actually restored and how much energy is burnt. We can measure every single bit of it. The one way to save energy is adding parallel springs, not series elastic. Series elastic doesn’t really save force generation, but parallel springs do. We actually have a paper about adding parallel springs.

Audrow Nash: Can you describe what a parallel spring would be?

Sangbae Kim: If you think about a desk lamp, it has springs on the joints so that it balances itself. It doesn’t have an actuator but it holds the position pretty well; you can move it around and still it can hold the position pretty well, because there’s a little friction at the joint. But those springs are actually compensating for the gravity.

We can do the same thing on the Cheetah leg because the majority of the energy consumption is caused by generating vertical force, fighting against the gravity. We add a spring in parallel to the actuator to help to generate the vertical gravitational force and that will reduce the requirements of torque generation in the motor. Then they will reduce residual heating dramatically.

Audrow Nash: And that will make the cheetah significantly more efficient.

Sangbae Kim: Significantly more efficient, yes.

Audrow Nash: Can you give me a comparison of electric motors versus muscles?

Sangbae Kim: That’s one of the most interesting things we learned through the MIT Cheetah project. We naively started with the idea that “Wow we might be able to build a robot that runs like an animal but we can also learn how animals work through the system.” That’s actually one of the inspiring aspects of this project.

But the more we study and we see the measurements, we realize how much the electric motor is different from our biological muscle. Now I’m thinking it’s kind of obvious, but there are huge differences. For example, quadrupeds have many different gaits, like the running gait, a canter gallop and a bound, and each gait has its own specific preferred speed.

When they run 3 meters per second they want to run in a trot, but when they’re running faster they want to switch to galloping. We don’t know exactly why, but we have some speculations and hypothesis.

Audrow Nash: These gaits, they’re different modes of running. So we walk and then jog and then run. But there’s not really something between walking and jogging for us. Is this what you mean by gaits? So in quadrupeds …  it would be walking and galloping and trotting.

Sangbae Kim: There are many gaits but the major ones are walking, trotting, cantering and galloping. And for each of these the foot pattern is different. There’s some data that shows that animals really don’t want to run certain gaits at certain speeds. We don’t know exactly why but they probably don’t want to run this way because the energy consumption is pretty high.

Our electric robot is actually very insensitive to gait and we realized the electric motors are much less picky in a way. For example, our torque generation and torque capability is independent from position. If you grab an electric motor, it doesn’t matter what position you are in, you can generate the same torque. You can generate the same torque according to speed as well: if you have a high enough voltage source, you can generate exactly the same torque at any speed. If you look at the muscles, the speed and then force and position, they highly couple each other. For example, your biceps can generate the biggest force if your elbow angle is 120 degrees, Then if your arms get stretched more and then become straight, your biceps muscle force becomes very small. When the biceps contract all the way to the shoulder length, your force generation can also drop significantly.

There’s also velocity-dependent force capability. When a muscle is contracted fast, your force generation capability drops very low and the muscle can generate a much higher force when it is expanding; when it’s doing negative work, it can generate much higher force. We don’t know exactly the energy consumption associated with that, but the maximum capability is heavily dependent on velocity and position. Which made us think that all this motion regeneration should be highly dependent on this muscle function. The electric motor doesn’t have that, so we start seeing this vast difference in energy consumption and capability. For example, when you run or you’re cycling the legs seems to have high energy consumption, and when you run fast at some point you feel like you cannot recycle your leg fast enough.

In the MIT Cheetah case, recycling leg energy consumption is very small, less than 10%. It’s probably because you can generate torque in any position, whereas when the leg is in the maximum stretched position, your force capability is very bad. We are not very good at moving back and forth due to that reason, so probably we need to include other parts of muscles and resonate using many different muscles. Whereas electric motors don’t need this many muscles; one motor is enough.

This relates to the spine. The cheetah uses its spine dramatically when its running fast and a lot of scientists are interested in that aspect. I don’t have any answer but my speculation is that it’s because the muscles have a very short range of operation. As I said on the biceps, you only have high force capabilities for 20-30 degrees, and for the rest of the range you don’t.

In order to generate high power movement like jumping or throwing a ball or swinging a bat, you need to include many muscles in a series fashion. I read a couple of papers showing that in a golf swing the maximum muscle activities are not happening at the same time, they are happening sequentially. Start from the ankle, to the leg and then fire, fire, fire and then going up, climbing up your body and your back muscle and then going to your arm. It’s sequentially activating, because probably one muscle cannot generate momentum for a long period of time because it has a fixed amount of good range of motion. Thinking about galloping, you need to swing your leg probably nearly as fast as 70 miles per hour to run 70 miles per hour, and one muscle cannot generate that kind of speed. So you probably need to include many muscles, and even more joints because more joints add speed. You can probably increase your speed by adding one more joint, which is where the spine comes in I think. In the electric motor case, it doesn’t really matter, because the speed is much cheaper in electric motors.

As I said, in muscles, your force drops if your speed increases, but in electric motor it’s not like that. You can actually generate the same amount of torque in high speed as well.

Audrow Nash: So what does this mean?

Sangbae Kim: The electric motor is actually way easier to use because everything is nearly linear and is less dependent on position and velocity. I think it’s actually more capable and a more powerful actuator than a human muscle. Although we are now still not capable … we’re still not as good as muscle in different aspects, for example force density. The muscles driving our fingers or jaw are not high power muscles but can generate really high force with a very small amount of mass. One finger can handle some people’s mass, but the muscles that are in charge of that finger are very minimal – it’s less than 100 grams or something. They are not high power but they are very high force and still very transparent. They can actually relax very quickly; we cannot generate that kind of actuator through an electric motor yet. So force density yes, but power density I think we actually exceed the human muscle.

Audrow Nash: Thank you.

Sangbae Kim: Thank you.

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

Tabula rasa learning

Learning Legged Swimming Gaits from Experience | Meger, David Paul;
Gamboa Higuera, Juan Camilo; Xu, Anqi; Giguere, Philippe; Dudek, Gregory
*Best Conference Paper Finalist*

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Look Ma, no inertial sensors!

Passive Stability of Vehicles without Angular Momentum Including Quadrotors and Ornithopters | Piccoli, Matthew; Yim, Mark

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It all starts with the basics

Towards Learning Hierarchical Skills for Multi-Phase Manipulation Tasks | Kroemer, Oliver; Daniel, Christian; Neumann, Gerhard; van Hoof, Herke; Peters, Jan
*Best Student Paper Finalist*

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Fast injection

An Automated Robotic System for High-Speed Microinjection of Caenorhabditis Elegans | Dong, Xianke;  Song, Pengfei; Liu, Xinyu
*Best Automation Paper Finalist*

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Help for vocal chords

Feed Forward Incision Control for Laser Microsurgery of Soft Tissue | Fichera, Loris; Pardo, Diego; Illiano, Placido; Caldwell, Darwin G.; Mattos, Leonardo;
*Best Medical Robotics Paper Finalist*

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Related research here and here.

No wrinkles, please

Accurate Garment Surface Analysis Using an Active Stereo Robot Head with Application to Dual-Arm Flattening | Sun, Li;  Aragon-Camarasa, Gerardo; Rogers, Simon; Siebert, Paul
*Best Robotic Manipulation Paper Finalist*

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Working together

Learning Multiple Collaborative Tasks with a Mixture of Interaction Primitives | Ewerton, Marco; Neumann, Gerhard; Lioutikov, Rudolf; Ben Amor, Heni; Peters, Jan; Maeda, Guilherme Jorge
*Best Service Robotics Paper Finalist*

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Taking it all in, in real time

Incremental Dense Semantic Stereo Fusion for Large-Scale Semantic Scene Reconstruction | Vineet, Vibhav (joint 1st author); Miksik, Ondrej (joint 1st author); Lidegaard, Morten; Niessner, Matthias; Golodetz, Stuart; Prisacariu, Victor; Kahler, Olaf; Murray, David; Izadi, Shahram; Perez, Patrick; Torr, Philip
*Best Robotic Vision Paper Finalist*

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Building the IKEAsy way

Multi-Robot Grasp Planning for Sequential Assembly Operations | Dogar, Mehmet Remzi;  Spielberg, Andrew; Baker, Stuart;  Rus, Daniela
*Best Robotic Manipulation Paper Finalist*

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Perception: It’s elementary

RoboSherlock: Unstructured Information Processing for Robot Perception | Beetz, Michael;  Balint-Benczedi, Ferenc;  Blodow, Nico;  Nyga, Daniel;  Wiedemeyer, Thiemo;  Marton, Zoltan-Csaba
*Best Service Robotics Paper Award Winner*

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Tiny guns

Toward Tissue Penetration by MRI-Powered Millirobots Using a Self-Assembled Gauss Gun | Becker, Aaron;  Felfoul, Ouajdi;  Dupont, Pierre
*Best Medical Robotics Paper Finalist*

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Working side by side

Control of Generalized Contact Motion and Force in Physical Human-Robot Interaction | Magrini, Emanuele Sapienza; Flacco, Fabrizio Sapienza; De Luca, Alessandro
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Need a helping hand?

The Design and Evaluation of a Cooperative Handheld Robot | Gregg-Smith, Austin; Mayol, Walterio
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Helping surgeons get a better view

A Miniaturised Robotic Probe for Real-Time Intraoperative Fusion of Ultrasound and Endomicroscopy | Dwyer, George; Giataganas, Petros; Pratt, Philip; Hughes, Michael;  Yang, Guang-Zhong
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Butterfly benchmark revisited

Case Study in Non-Prehensile Manipulation: Planning and Orbital Stabilization of One-Directional Rollings for the “Butterfly” Robot | Surov, Maksim; Shiriaev, Anton; Freidovich, Leonid; Gusev, Sergei; Paramonov, Leonid
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There was so much great research presented at ICRA in Seattle this year that it was tough to find the time to see it all. Thanks to IEEE RAS ICRA, who allowed us to film at the event, we are able to bring you highlights from some of this year’s exciting papers. We’ll be sharing lots more ICRAcam videos over the next couple of weeks, so watch this space! In Part One: Adhesion grippers, robots life cycles, car-towing bots, and motion planning in uncertainty.

Check out that grip!

Grasping without Squeezing: Shear Adhesion Gripper with Fibrillar Thin Film | Hawkes, Elliot Wright; Christensen, David; Han, Amy Kyungwon; Jiang, Hao; Cutkosky, Mark
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The cycle of life, robo-style

An Untethered Miniature Origami Robot that Self-folds, Walks, Swims, and Degrades | Miyashita, Shuhei; Guitron, Steven; Ludersdorfer, Marvin; Sung, Cynthia; Rus, Daniela
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Parking illegally? A robot could tow your car away

AVERT: An Autonomous Multi-Robot System for Vehicle Extraction and Transportation | Amanatiadis, Angelos Democritus; Henschel, Christopher; Birkicht, Bernd; Andel, Benjamin; Charalampous, Konstantinos; Kostavelis, Ioannis; May, Richard; Gasteratos, Antonios

https://youtu.be/g5yRiI-mQ8Q

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Path planning in the face of uncertainty

An Online and Approximate Solver for POMDPs with Continuous Action Space | 
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• #ICRA15 videos of plenaries and keynotes

• Team RBO from Berlin wins Amazon Picking Challenge convincingly

• #ICRA15 video trailer: Preview the latest in robotics and AI

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

RBO Team from TU Berlin wins the Amazon Picking Challenge at ICRA 2015. Photo Credit: RBO.

Capturing and processing camera and sensor data and recognizing various shapes to determine a set of robotic actions is conceptually easy. Yet Amazon challenged the industry to do a selecting and picking task robotically and 28 teams from around the world rose to it.

Perception isn’t just about cameras and sensors. Software has to convert the data and infer as to what it “sees”. In the case of the Amazon Picking Challenge held last week at the IEEE International Conference on Robotics & Automation (ICRA), each team’s robot was to pick from a shopping list of consumer items of varying shapes and sizes – from pencils, to toys, tennis balls, cookies and cereal boxes – which were haphazardly stored on shelves, and then place their selected items in a bin. They could use any robot, mobile or not, and any arm and end-of-arm grasping tool or tools to accomplish the task.

It’s tricky for robots using sensors to identify and locate objects that can be confused by plastic packaging within the shelf or storage area. Rodney Brooks, of iRobot, MIT and Rethink Robotics fame, often speaks of an industry-wide aspirational goal regarding perception in robotics: “If we were only able to provide the visual capabilities of a 2-year old child, robots would quickly get a lot better.” That is what this contest is all about.

Software has to first identify the item to pick and then figure out the best way to grab it and move it out of the storage area. Amazon, with its acquisition of Kiva Systems, has mastered bringing goods to the picker/packer and now wants to automate the remaining process of picking the correct goods from the shelves and placing them in the packing box, hence their Amazon Picking Challenge.

The top three winners were the teams from Technical University (TU) – Berlin, with 148 points; MIT in 2nd place with 88 points; and the 3rd place finisher (Oakland U and Dataspeed), which only got 35 points. Teams were scored on how many items were correctly selected, picked and placed.

Many commercial companies with proprietary software for just this type of application (such as Tenessee-based Universal Robotics and their Neocortex Vision System, and Silicon Valley startup Fetch Robotics who were premiering their new Fetch and Freight system at the same ICRA conference) chose not to enter because the terms of the challenge included that the software be open sourced.

Team RBO from TU-Berlin wrote their own vision system software and will soon be working on a paper on the subject. They used a Barrett WAM arm because it was the most flexible device for this task amongst the arms that they had access to in their lab. They used a vacuum cleaner tool augmented with a suction cup and a vacuum cleaner to power the suction. For a base, they decided they needed to go mobile and used an old Nomadic Technologies platform, which they upgraded to fit the needs of the contest. Nomadic no longer exists. It was acquired by 3Com in 2000. TU’s photo at right shows robot’s camera view and random placement of items in cubby holes. Watch a video of their winning run here:

Noriko Takiguchi, a Japanese reporter for RoboNews.net, who was at the contest, observed the TU team and said that their approach was torque-based plus position control of the arm and the mobile base, consequently they had good torque control that that gave them flexibility in choosing where to place the suction cup and how much suction to apply.

Team RBO received a 1st place prize of $20,000 plus travel costs for the equipment and team members.

ICRA 2015 – exhibition hall

A small photo essay from the exhibition hall of ICRA 2015. (All photos by Ioannis Erripis @ Robohub.)

Integrating Matlab with ROS – Mathworks

Robotic arm – Schunk GmbH & Co. KG

Lego EV3 robot – Washington FIRST Robotics

MaxAero – Autel quadcopter

Fetch Robotics

OUR Robot – Smokie Robotics

Motion capture installation – Motion Analysis Corporation

Roomba – iRobot

Stanley Innovation

Harmonic Drive LLC

mems IMU & AHRS – MOOG Inc.

Titanium 3D printed actuator – MOOG Inc.

Clearpath Robotics Inc.

Mobile Baxter platform – Clearpath Robotics Inc.

Robotic Gripper – Robotiq

Abraham Lincoln animatronic head – Walt Disney Imagineering Research & Development

LBR IIWA – KUKA Robotics

Dynamixel OpenCM – Robotis Inc.

Dexterous Hand – Shadow Robot Company

GRP 4400 wheeled platform – American Robot Company

Matrix 100 quadrotor for developers – DJI

WAM arm – Barett Technology Inc.

Maplesoft

da Vinci Xi – Intuitive Surgical

Baxter – Rethink Robotics

Flexible Grasping – RightHand Robotics

Hexapod build with modular field robotic actuators – HEBI Robotics

Modular field robotic actuator – HEBI Robotics

MINI – Robotis

Rapid manufacturing – The Rapid Group

Dual Arm SCARA robot – Mecademic Inc.

3D Force sensor demo – OptoForce

Raven II – Applied Dexterity Inc.

Adept Mobile Robots

University of Washington College of Engineering

SICK 3D laser scanners

[updated] Have a look below for the videos of plenary talks and keynotes at ICRA 2015. You can also use this youtube playlist. We’ll be uploading new videos as they arrive.

One Robot for Every Task – Daniela Rus, MIT – Wednesday Plenary Session ICRA 2015

Panel Discussion on the Relationship of AI & Robotics (Partial)

Panelists: Ruzena Bajcsy, Rod Brooks, Peter Hart, Nils Nilsson, James Kuffner, Ben Kuipers, Manuela Veloso
Moderator: Raja Chatila

Imagining a Cloud-enabled Shakey – James Kuffner, Google Research

Taming the Swarm – Radhika Nagpal, Harvard University

Autonomous Continuum Manipulation – Jing Xiao, University of North Carolina

Making Shakey – Peter Hart, SRI, retired

Closing the Ring – Danny Halperin, Tel Aviv University

Robotics in Education in Africa – Ayorkor Korsah, Ashesi University College

Robotics Innovation Start Up Style – Helen Greiner, CyPhy Works (Plenary Day 2 ICRA 2015)

Dean Kamen – Innovation: Past, Present and Future

Alessandro De Luca – A Control Architecture for Human Robot Collaboration

Danica Kragic – Seeing, caging, grasping

Automaton posted a very good selection of videos from papers presented at ICRA

The Amazon Picking Challenge is over and two things stood out. One: how many different arm gripper solutions were possible; and two: just how difficult the challenge still is. The gap between the top two teams and the other 26 teams was significant, with Team RBO scoring 148 points, Team MIT scoring 88 points and Team Grizzly next best with 35 points.

Just in: video from winning Team RBO (hattip to SushiCapacitor on Reddit)

https://youtu.be/LtWPH-bcc4M

The Amazon Picking Challenge was developed to spur advancement in fundamental technologies for automated picking in unstructured warehouse environments. This is something that Amazon badly needs to do better in order to fulfill all our e-commerce instant delivery orders. And it’s also something that will enable robots to better work in all areas of our life. Not surprisingly then, there was a lot of excitement at ICRA around the 2 day challenge.

One of the conference expo halls was given over to robot competitions. There were 16 bays with a set of warehouse shelves in each. Amazon had announced a menu of common e-commerce items that would be used in the challenge, but before each round the shelves were stocked with a randomized selection of items from the menu and each team was given a order list to fulfill. Each of the 28 teams was given time slots to practice in and then compete. Over the course of 2 days, 28 teams rotated through the competition.

It was similar to the DARPA Robotics Challenge, at least the 2013 first round. “Like watching paint dry.” With so much happening at ICRA, I was only able to visit the Picking Challenge for short periods of time. As a spectator, I spent most of my time watching robots do nothing. Large amounts of nothing. Occasionally nothing would be enlivened by an attempt to pick up nothing, or perhaps the shelf itself. Once or twice I saw a real pick… get dropped.

If I was very lucky, because the competition area was crowded with media, spectators and members of other teams, I might get to look at the laptops running code and showing representations of what the robot was perceiving while it looked like nothing was happening.

And the perception was the real event. I talked to Team MIT after they posted their score of 88 points, blowing all previous entrants out of the water. I had particularly noticed their combination of an ABB 2 armed robot with a flat scoop and a suction gripper. When asked if they attributed their success to the end effector or other mechanical solutions, the team gave all credit instead to their perception and path planning algorithms.

Ultimately that is the secret sauce that Amazon, and all the other major robot companies, would like to capture; better algorithms for existing robots. The range of hardware in use in the competition was broad. There were several Baxters, Yaskawa Motomans, Universal Arms, ABBs, PR2s, Barrett Arms, custom built 3d printer style rigs and factory automation. There were scoops, hands, grippers, and suction.

End effectors were made or covered in a range of substances, wood, metal, plastic, soft silicon. While it might all be about the algorithm, I will point out that the two most efficient teams did both use suction and soft surfaced grippers. A third thing that stood out to me was how many large robotics companies were in the audience of the Picking Challenge, watching.

Congratulations to the winning teams – and to Amazon for the support they gave the robotics community for this event. Amazon awarded travel grants to ICRA, arranged practice equipment and a $26,000 prize pool.  Participants will be encouraged to share and disseminate their approach to improve future challenge results and industrial implementations.

Some more information from Barrett Technology:

Of 25 teams from around the world, the winner of the Amazon Robotic Bin-Picking Challenge is the Technische Universität Berlin using Barrett’s WAM robotic arm.

The Amazon Picking Challenge is the centerpiece of the record-attendance IEEE Conference on Robotics & Automation in Seattle this year.  There are 28 teams here from around the globe who have brought their hardware and software to the competition.  Entries include robotic arms from ABB, Fanuc, Rethink Robotics, Universal Robots, and Yaskawa-Motoman.  The competition just finished, and the scores are in…  Technische Universität Berlin’s Robotics and Biology Laboratory using Barrett’s WAM arm in 1st place at 148 points followed, in 2nd place with 88 points, by the Massachusetts Institute of Technology using an ABB arm and a gripper with creative finger geometries.  The 3rd place finisher came in at 35 points.

Prof. Oliver Brock of TU-Berlin remarks: “This has been a fantastic team effort.  Every single member of our team contributed with enthusiasm and ingenuity, enabling us to produce a compelling showcase for mobile manipulation as a winning approach to industrial manipulation.”  Speaking to media, Townsend adds: “We are grateful that TU-Berlin won this competition, and of course especially grateful that they chose to use the WAM arm.  The WAM’s dexterity is world-class, the long-and-slender links allow the robot to reach easily in and around tight workspaces, and the WAM’s famed backdrivability enables deft interactions with the environment. While it’s a powerful combination of capabilities, the great creativity and dedication of the students and staff under Oliver’s leadership has been essential, and it has been an absolute pleasure supporting them.”

The WAM arm (shown with a BarrettHand).

To learn more about the team from the Technische Universität Berlin, go to

http://www.robotics.tu-berlin.de

Contact Prof. Alberto Rodriguez for more information about the MIT team, at

http://www.cs.cmu.edu/~albertor

To read more about the 2015 Amazon Robotic Bin-Picking Challenge, go to

http://amazonpickingchallenge.org

For information on the IEEE International Conference on Robotics & Automation, go to

http://icra2015.org

The conference will include plenary and contributed paper sessions, workshops, exhibitions and robot challenges, as well as some events that are new to ICRA.

Robohub have a large contingent at this year’s event – Sabine, Hallie, Andra, Yannis, Audrow, Ryan and Per – so watch this space for our latest coverage, including podcasts, videos, blogs and tweets.

View more videos in IEEE Robotics & Automation Society – ICRA Conference youtube channel and on our post featuring all the available #ICRA15 Conference Videos

• ICRA 2015 social media workshop for roboticists: Communicating your research in 140 characters or less
• #ICRA15 video trailer: Preview the latest in robotics and AI

Tweets about #icra15 OR #icra2015

ICRA | May 26-30, 2015 | Seattle, Washington.

Roboticists have amazing stories that the public wants to hear about, but many researchers worry that their work will be oversimplified, misrepresented or misunderstood. By engaging directly with the public, in their own words, roboticists can explain the importance of tax-funded projects, attract students and collaborators, spur innovation, facilitate technology transfer, and take part in the discussion of issues that are important to the community.

In this crash course, you will learn the tools and techniques to efficiently promote your robotics research to the public and the media via the main social channels and tech news aggregators such as Twitter, Facebook, Hacker News, Reddit, YouTube and Slashdot. We will walk you through the development of past successful research promotion campaigns in robotics so you can see how to turn your research into a social media headline that is clear, concise, and tells a story that readers want to hear.

Time/Location

Tuesday, May 26
12:00-13:30
Room WSCC 620
http://icra2015.org/conference/forums/11-conference/51-social-media-workshop

Attendance is open to all; lunch will be provided to participants registered to the workshop through the conference CVENT registration system.

Outcomes

Our goal is to leave you feeling confident about using social media to promote your research, and to help you promote your recently published ICRA paper using at least one social media platform before the end of the conference.

In this crash course you will:

• learn about the top communication platforms that are most relevant to the robotics community, and build confidence in using these platforms to extend your reach
• identify pitfalls in communicating about robotics via social media, and how to avoid them
• discover how to craft a headline or tweet that will drive traffic and improve awareness about your work
• learn how to approach journalists about your research
• if you wish, receive one-on-one follow up advice and coaching from experienced science communicators

Presenters

Sabine Hauert, Bristol Robotics Laboratory Lecturer, Robohub Founder
Hallie Siegel, Robohub Managing Editor
Evan Ackerman, Senior Writer at IEEE Spectrum Automaton Blog

Format

The foundation of this crash course is a 90-minute lecture-based interactive session. Come prepared to think about how to turn your ICRA paper into a tweet!

For anyone seeking to further advance their social media skills after the lunch time session, Robohub staff will be available at a specially marked stand in the exhibit hall throughout the remainder of the conference. Here we will provide one-on-one follow up advice and coaching on how to craft a tweet or write a headline to submit to a news aggregator such as Slashdot or Hacker News.

We will also have an “ICRA Cam” set up where you can pitch your research on video for publication to YouTube.

Who’s It For

This crash course is for any roboticist who would like to learn more about how to use social media to communicate about their work.

#ICRA15 just released a trailer featuring some of the highlights from the conference. It’s a nice sneak peak at the latest robots that will be presented in Seattle this May.

A must-watch!

For other languages, follow the links below:
Mandarin
Japanese
Korean
Arabic
Spanish
French
Italian

Shame that the missing engineering hero on the cover of IEEE Spectrum’s July issue looks like superMAN , because the IEEE Women in Engineering (WIE) lunch at IROS 2014 was a super gathering of high achievers who are making statements about 6% representation looking more like a glass half full, if you harness the potential. Lynne Parker, for example, is the General Chair of ICRA 2015, with a 50 strong all-female organizing committee for the premier robotics research event .

ICRA is the IEEE Robotics and Automation Society’s flagship conference and is a premier international forum for robotics researchers to present their work. The 2015 conference will be held May 26-30, 2015 at the Washington State Convention Center in Seattle, Washington, USA. Having an all-women committee serves as an important watershed for the robotics community.

Parker said, “There are usually only one or two women involved in conferences and when I ask why so few, people say that there aren’t enough women with experience. So, look at all these women with experience now who can be helping with future committees.” Women might make up only 6% of the global IEEE community but Parker feels that even that presence hasn’t been sufficiently felt. She also encourages women to submit papers or workshops to ICRA 2015 by the deadline of October 1st, 2014 (just around the corner!).

As well as the incredible list of women on the ICRA 2015 committee (below), Robohub published a ’25 women in robotics you need to know about’ list in 2013 and we’ll be publishing a new list for Ada Lovelace Day on October 14, 2014. Ada Lovelace Day celebrates the achievement of women in computing, science, technology and engineering.

The idea is to provide inspiring role models across a wide range of robotics fields. The women on these lists are a fantastic sample from an admittedly small group. I encourage everyone to look further afield when searching for women to meet targets for boards, committees, positions and outreach, rather than expect the same few women to pick up all the slack. (This ’empowerment 101′ applies to any diversity demographic.)

Of course women are not the only ones who can change the demographics of the field, but it definitely helps to see women rising to positions of influence. For example, Lynne Parker has just been appointed Division Director for the NSF’s Information and Intelligent Systems (IIS) Division within the Computer and Information Science and Engineering (CISE) Directorate.

This 3-year role covers overseeing the National Robotics Initiative alongside big data, human-centered computing, and core computer science. Parker is looking not just at internal program management but is also looking ahead to see what are the new directions for the robotics community. “To be somewhat visionary, to the extent that we can be,” said Parker. “For example, we’re an international community. Can we do better at representing that from a funding perspective? That’s a hard challenge, but that does’t mean we can’t look at it.”

In closing, the CISE Directorate enthusiastically welcomes Dr. Parker to NSF and looks forward to working with her to advance the frontiers of knowledge in information and intelligent systems.” Farnam Jahanian, NSF 

We also heard from Ayanna Howard, HumAnS Laboratory and GIT,  and Jeff Trinkle, NSF, NRI and Rensselaer, at the WIE luncheon at IROS 2014. With over 15,000 members around the world, IEEE’s Women in Engineering (WIE) is the largest international professional organization dedicated to promoting women engineers and scientists and inspiring girls around the world to follow their academic interests to a career in engineering. Formed in 1994, the luncheon marks the 20th anniversary of WIE.

https://www.youtube.com/watch?v=pukuQw0Ov0s

ICRA 2015 Organizing Committee

Honorary Chair
Ruzena Bajcsy (University of California, Berkeley)

General Chair
Lynne Parker (University of Tennessee)

Program Chair
Nancy M. Amato (Texas A&M University)

Program Co-Chairs
Dani Kragic (Royal Institute of Technology, KTH)
Hong Qiao (Chinese Academy of Sciences)
Jing Xiao (University of North Carolina, Charlotte)

CEB Editor-in-Chief
Allison Okamura (Stanford University)

Finance Chair
Yi Guo (Stevens Institute of Technology)

Workshops and Tutorials Chair
Monica Anderson (University of Alabama)

Workshops and Tutorials Co-Chairs
Spring Berman (Arizona State University)
Alicia Casals (Technical University of Catalonia)
Yukie Nagai (Osaka University)

Interactive Sessions Chair
Ani Hsieh (Drexel University)

Interactive Sessions Co-Chair
Ming Lin (University of North Carolina, Chapel Hill)

Awards Chair
Lydia Kavraki (Rice University)

Awards Co-Chairs
Jessica Hodgins (Carnegie Mellon University)
Daniela Rus (Massachusetts Institute of Technology)
Carme Torras (Institut de Robòtica i Informàtica Industrial, CSIC-UPC)

Publication Chair
Dawn Tilbury (University of Michigan)

Publicity Chair
Nora Ayanian (University of Southern California)

Publicity Co-Chairs
Raffaela Carloni (University of Twente)
MIhoko Otake (Chiba University)

Exhibitions Chair
Robin Murphy (Texas A&M University)

Exhibitions Co-Chair
Cecilia Laschi (Scuola Superiore Sant’Anna)

Sponsorships Co-Chairs
Manuela Veloso (Carnegie Mellon University)
Maja Mataric (University of Southern California)

Industry Forum Chair
Aude Billard (Ecole Polytechniuque Federale de Lausanne)

Industry Forum Co-Chairs
Dana Kulic (University of Waterloo)
Angelika Peer (TU Munich)
Yuru Zhang (Beihang University)

Developing Countries Outreach Chair
M. Bernardine Dias (Carnegie Mellon University)

Developing Countries Outreach Co-Chairs
Chinwe Ekenna (Texas A&M University)
Ayorkor Korsah (Carnegie Mellon University)

Career Fair Chair
Hadas Kress-Gazit (Cornell University)

Career Fair Co-Chairs
Maren Bennewitz (University of Freiburg)
Jana Koseca (George Mason University)

PhD Forum Chair
Ayanna Howard (Georgia Institute of Technology)

PhD Forum Co-Chairs
Jamie Paik (EPFL)
Hae Won Park (Georgia Institute of Technology)
Xiaorui Zhu (Harbin Institute of Technology)

Competitions Co-Chairs
Sonia Chernova (Worcester Polytechnic Institute)
Manuela Veloso (Carnegie Mellon University)

Travel Awards Chair
Maria Gini (University of Minnesota)

Student Activities Chair
Lydia Tapia (University of New Mexico)

Student Activities Co-Chair
Hanna Kurniawati (University of Queensland)

K-12 Outreach Chairs
Radhika Nagpal (Harvard University)
Claire Tomlin (University of California, Berkeley)

Web Chair
Shawna Thomas (Texas A&M University)

Local Arrangements Chair
Kristy Morganson (University of Washington)

Local Arrangements Co-Chair
Maya Cakmak (University of Washington)

Senior Program Committee

Ruzena Bajcsy (University of California, Berkeley)
Alicia Casals (Technical University of Catalonia)
Bernardine Dias (Carnegie Mellon University)
Maria Gini (University of Minnesota)
Yi Guo (Stevens Institute of Technology)
Ayanna Howard (Georgia Institute of Technology)
Lydia Kavraki (Rice University)
Jana Koseca (George Mason University)
Dani Kragic (Royal Institute of Technology, KTH)
Ming Lin (University of North Carolina, Chapel Hill)
Robin Murphy (Texas A&M University)
Radhika Nagpal (Harvard University)
Allison Okamura (Stanford University)
Katia Sycara (Carnegie Mellon University)
Dawn Tilbury (University of Michigan)
Carme Torras (Institut de Robòtica i Informàtica Industrial, CSIC-UPC)
Manuela Veloso (Carnegie Mellon University)
Jing Xiao (University of North Carolina, Charlotte)
Yuru Zhang (Beihang University)