Tag Archives: drive

#437809 Q&A: The Masterminds Behind ...

Posted on November 30, 2020 by Android

Illustration: iStockphoto

Getting a car to drive itself is undoubtedly the most ambitious commercial application of artificial intelligence (AI). The research project was kicked into life by the 2004 DARPA Urban Challenge and then taken up as a business proposition, first by Alphabet, and later by the big automakers.

The industry-wide effort vacuumed up many of the world’s best roboticists and set rival companies on a multibillion-dollar acquisitions spree. It also launched a cycle of hype that paraded ever more ambitious deadlines—the most famous of which, made by Alphabet’s Sergei Brin in 2012, was that full self-driving technology would be ready by 2017. Those deadlines have all been missed.

Much of the exhilaration was inspired by the seeming miracles that a new kind of AI—deep learning—was achieving in playing games, recognizing faces, and transliterating voices. Deep learning excels at tasks involving pattern recognition—a particular challenge for older, rule-based AI techniques. However, it now seems that deep learning will not soon master the other intellectual challenges of driving, such as anticipating what human beings might do.

Among the roboticists who have been involved from the start are Gill Pratt, the chief executive officer of Toyota Research Institute (TRI) , formerly a program manager at the Defense Advanced Research Projects Agency (DARPA); and Wolfram Burgard, vice president of automated driving technology for TRI and president of the IEEE Robotics and Automation Society. The duo spoke with IEEE Spectrum’s Philip Ross at TRI’s offices in Palo Alto, Calif.

This interview has been condensed and edited for clarity.

IEEE Spectrum: How does AI handle the various parts of the self-driving problem?

Photo: Toyota

Gill Pratt

Gill Pratt: There are three different systems that you need in a self-driving car: It starts with perception, then goes to prediction, and then goes to planning.

The one that by far is the most problematic is prediction. It’s not prediction of other automated cars, because if all cars were automated, this problem would be much more simple. How do you predict what a human being is going to do? That’s difficult for deep learning to learn right now.

Spectrum: Can you offset the weakness in prediction with stupendous perception?

Photo: Toyota Research Institute for Burgard

Wolfram Burgard

Wolfram Burgard: Yes, that is what car companies basically do. A camera provides semantics, lidar provides distance, radar provides velocities. But all this comes with problems, because sometimes you look at the world from different positions—that’s called parallax. Sometimes you don’t know which range estimate that pixel belongs to. That might make the decision complicated as to whether that is a person painted onto the side of a truck or whether this is an actual person.

With deep learning there is this promise that if you throw enough data at these networks, it’s going to work—finally. But it turns out that the amount of data that you need for self-driving cars is far larger than we expected.

Spectrum: When do deep learning’s limitations become apparent?

Pratt: The way to think about deep learning is that it’s really high-performance pattern matching. You have input and output as training pairs; you say this image should lead to that result; and you just do that again and again, for hundreds of thousands, millions of times.

Here’s the logical fallacy that I think most people have fallen prey to with deep learning. A lot of what we do with our brains can be thought of as pattern matching: “Oh, I see this stop sign, so I should stop.” But it doesn’t mean all of intelligence can be done through pattern matching.

“I asked myself, if all of those cars had automated drive, how good would they have to be to tolerate the number of crashes that would still occur?”
—Gill Pratt, Toyota Research Institute

For instance, when I’m driving and I see a mother holding the hand of a child on a corner and trying to cross the street, I am pretty sure she’s not going to cross at a red light and jaywalk. I know from my experience being a human being that mothers and children don’t act that way. On the other hand, say there are two teenagers—with blue hair, skateboards, and a disaffected look. Are they going to jaywalk? I look at that, you look at that, and instantly the probability in your mind that they’ll jaywalk is much higher than for the mother holding the hand of the child. It’s not that you’ve seen 100,000 cases of young kids—it’s that you understand what it is to be either a teenager or a mother holding a child’s hand.

You can try to fake that kind of intelligence. If you specifically train a neural network on data like that, you could pattern-match that. But you’d have to know to do it.

Spectrum: So you’re saying that when you substitute pattern recognition for reasoning, the marginal return on the investment falls off pretty fast?

Pratt: That’s absolutely right. Unfortunately, we don’t have the ability to make an AI that thinks yet, so we don’t know what to do. We keep trying to use the deep-learning hammer to hammer more nails—we say, well, let’s just pour more data in, and more data.

Spectrum: Couldn’t you train the deep-learning system to recognize teenagers and to assign the category a high propensity for jaywalking?

Burgard: People have been doing that. But it turns out that these heuristics you come up with are extremely hard to tweak. Also, sometimes the heuristics are contradictory, which makes it extremely hard to design these expert systems based on rules. This is where the strength of the deep-learning methods lies, because somehow they encode a way to see a pattern where, for example, here’s a feature and over there is another feature; it’s about the sheer number of parameters you have available.

Our separation of the components of a self-driving AI eases the development and even the learning of the AI systems. Some companies even think about using deep learning to do the job fully, from end to end, not having any structure at all—basically, directly mapping perceptions to actions.

Pratt: There are companies that have tried it; Nvidia certainly tried it. In general, it’s been found not to work very well. So people divide the problem into blocks, where we understand what each block does, and we try to make each block work well. Some of the blocks end up more like the expert system we talked about, where we actually code things, and other blocks end up more like machine learning.

Spectrum: So, what’s next—what new technique is in the offing?

Pratt: If I knew the answer, we’d do it. [Laughter]

Spectrum: You said that if all cars on the road were automated, the problem would be easy. Why not “geofence” the heck out of the self-driving problem, and have areas where only self-driving cars are allowed?

Pratt: That means putting in constraints on the operational design domain. This includes the geography—where the car should be automated; it includes the weather, it includes the level of traffic, it includes speed. If the car is going slow enough to avoid colliding without risking a rear-end collision, that makes the problem much easier. Street trolleys operate with traffic still in some parts of the world, and that seems to work out just fine. People learn that this vehicle may stop at unexpected times. My suspicion is, that is where we’ll see Level 4 autonomy in cities. It’s going to be in the lower speeds.

“We are now in the age of deep learning, and we don’t know what will come after.”
—Wolfram Burgard, Toyota Research Institute

That’s a sweet spot in the operational design domain, without a doubt. There’s another one at high speed on a highway, because access to highways is so limited. But unfortunately there is still the occasional debris that suddenly crosses the road, and the weather gets bad. The classic example is when somebody irresponsibly ties a mattress to the top of a car and it falls off; what are you going to do? And the answer is that terrible things happen—even for humans.

Spectrum: Learning by doing worked for the first cars, the first planes, the first steam boilers, and even the first nuclear reactors. We ran risks then; why not now?

Pratt: It has to do with the times. During the era where cars took off, all kinds of accidents happened, women died in childbirth, all sorts of diseases ran rampant; the expected characteristic of life was that bad things happened. Expectations have changed. Now the chance of dying in some freak accident is quite low because of all the learning that’s gone on, the OSHA [Occupational Safety and Health Administration] rules, UL code for electrical appliances, all the building standards, medicine.

Furthermore—and we think this is very important—we believe that empathy for a human being at the wheel is a significant factor in public acceptance when there is a crash. We don’t know this for sure—it’s a speculation on our part. I’ve driven, I’ve had close calls; that could have been me that made that mistake and had that wreck. I think people are more tolerant when somebody else makes mistakes, and there’s an awful crash. In the case of an automated car, we worry that that empathy won’t be there.

Photo: Toyota

Toyota is using this
Platform 4 automated driving test vehicle, based on the Lexus LS, to develop Level-4 self-driving capabilities for its “Chauffeur” project.

Spectrum: Toyota is building a system called Guardian to back up the driver, and a more futuristic system called Chauffeur, to replace the driver. How can Chauffeur ever succeed? It has to be better than a human plus Guardian!

Pratt: In the discussions we’ve had with others in this field, we’ve talked about that a lot. What is the standard? Is it a person in a basic car? Or is it a person with a car that has active safety systems in it? And what will people think is good enough?

These systems will never be perfect—there will always be some accidents, and no matter how hard we try there will still be occasions where there will be some fatalities. At what threshold are people willing to say that’s okay?

Spectrum: You were among the first top researchers to warn against hyping self-driving technology. What did you see that so many other players did not?

Pratt: First, in my own case, during my time at DARPA I worked on robotics, not cars. So I was somewhat of an outsider. I was looking at it from a fresh perspective, and that helps a lot.

Second, [when I joined Toyota in 2015] I was joining a company that is very careful—even though we have made some giant leaps—with the Prius hybrid drive system as an example. Even so, in general, the philosophy at Toyota is kaizen—making the cars incrementally better every single day. That care meant that I was tasked with thinking very deeply about this thing before making prognostications.

And the final part: It was a new job for me. The first night after I signed the contract I felt this incredible responsibility. I couldn’t sleep that whole night, so I started to multiply out the numbers, all using a factor of 10. How many cars do we have on the road? Cars on average last 10 years, though ours last 20, but let’s call it 10. They travel on an order of 10,000 miles per year. Multiply all that out and you get 10 to the 10th miles per year for our fleet on Planet Earth, a really big number. I asked myself, if all of those cars had automated drive, how good would they have to be to tolerate the number of crashes that would still occur? And the answer was so incredibly good that I knew it would take a long time. That was five years ago.

Burgard: We are now in the age of deep learning, and we don’t know what will come after. We are still making progress with existing techniques, and they look very promising. But the gradient is not as steep as it was a few years ago.

Pratt: There isn’t anything that’s telling us that it can’t be done; I should be very clear on that. Just because we don’t know how to do it doesn’t mean it can’t be done. Continue reading →

Posted in Human Robots

#437783 Ex-Googler’s Startup Comes Out of ...

Posted on November 29, 2020 by Android

Over the last 10 years, the PR2 has helped roboticists make an enormous amount of progress in mobile manipulation over a relatively short time. I mean, it’s been a decade already, but still—robots are hard, and giving a bunch of smart people access to a capable platform where they didn’t have to worry about hardware and could instead focus on doing interesting and useful things helped to establish a precedent for robotics research going forward.

Unfortunately, not everyone can afford an enormous US $400,000 robot, and even if they could, PR2s are getting very close to the end of their lives. There are other mobile manipulators out there taking the place of the PR2, but so far, size and cost have largely restricted them to research labs. Lots of good research is being done, but it’s getting to the point where folks want to take the next step: making mobile manipulators real-world useful.

Today, a company called Hello Robot is announcing a new mobile manipulator called the Stretch RE1. With offices in the San Francisco Bay Area and in Atlanta, Ga., Hello Robot is led by Aaron Edsinger and Charlie Kemp, and by combining decades of experience in industry and academia they’ve managed to come up with a robot that’s small, lightweight, capable, and affordable, all at the same time. For now, it’s a research platform, but eventually, its creators hope that it will be able to come into our homes and take care of us when we need it to.

A fresh look at mobile manipulators
To understand the concept behind Stretch, it’s worth taking a brief look back at what Edsinger and Kemp have been up to for the past 10 years. Edsinger co-founded Meka Robotics in 2007, which built expensive, high performance humanoid arms, torsos, and heads for the research market. Meka was notable for being the first robotics company (as far as we know) to sell robot arms that used series elastic actuators, and the company worked extensively with Georgia Tech researchers. In 2011, Edsinger was one of the co-founders of Redwood Robotics (along with folks from SRI and Willow Garage), which was going to develop some kind of secret and amazing new robot arm before Google swallowed it in late 2013. At the same time, Google also acquired Meka and a bunch of other robotics companies, and Edsinger ended up at Google as one of the directors of its robotics program, until he left to co-found Hello Robot in 2017.

Meanwhile, since 2007 Kemp has been a robotics professor at Georgia Tech, where he runs the Healthcare Robotics Lab. Kemp’s lab was one of the 11 PR2 beta sites, giving him early experience with a ginormous mobile manipulator. Much of the research that Kemp has spent the last decade on involves robots providing assistance to untrained users, often through direct physical contact, and frequently either in their own homes or in a home environment. We should mention that the Georgia Tech PR2 is still going, most recently doing some clever material classification work in a paper for IROS later this year.

Photo: Hello Robot

Hello Robot co-founder and CEO Aaron Edsinger says that, although Stretch is currently a research platform, he hopes to see the robot deployed in home environments, adding that the “impact we want to have is through robots that are helpful to people in society.”

So with all that in mind, where’d Hello Robot come from? As it turns out, both Edsinger and Kemp were in Rodney Brooks’ group at MIT, so it’s perhaps not surprising that they share some of the same philosophies about what robots should be and what they should be used for. After collaborating on a variety of projects over the years, in 2017 Edsinger was thinking about his next step after Google when Kemp stopped by to show off some video of a new robot prototype that he’d been working on—the prototype for Stretch. “As soon as I saw it, I knew that was exactly the kind of thing I wanted to be working on,” Edsinger told us. “I’d become frustrated with the complexity of the robots being built to do manipulation in home environments and around people, and it solved a lot of problems in an elegant way.”

For Kemp, Stretch is an attempt to get everything he’s been teaching his robots out of his lab at Georgia Tech and into the world where it can actually be helpful to people. “Right from the beginning, we were trying to take our robots out to real homes and interact with real people,” says Kemp. Georgia Tech’s PR2, for example, worked extensively with Henry and Jane Evans, helping Henry (a quadriplegic) regain some of the bodily autonomy he had lost. With the assistance of the PR2, Henry was able to keep himself comfortable for hours without needing a human caregiver to be constantly with him. “I felt like I was making a commitment in some ways to some of the people I was working with,” Kemp told us. “But 10 years later, I was like, where are these things? I found that incredibly frustrating. Stretch is an effort to try to push things forward.”

A robot you can put in the backseat of a car
One way to put Stretch in context is to think of it almost as a reaction to the kitchen sink philosophy of the PR2. Where the PR2 was designed to be all the robot anyone could ever need (plus plenty of robot that nobody really needed) embodied in a piece of hardware that weighs 225 kilograms and cost nearly half a million dollars, Stretch is completely focused on being just the robot that is actually necessary in a form factor that’s both much smaller and affordable. The entire robot weighs a mere 23 kg in a footprint that’s just a 34 cm square. As you can see from the video, it’s small enough (and safe enough) that it can be moved by a child. The cost? At $17,950 apiece—or a bit less if you buy a bunch at once—Stretch costs a fraction of what other mobile manipulators sell for.

It might not seem like size or weight should be that big of an issue, but it very much is, explains Maya Cakmak, a robotics professor at the University of Washington, in Seattle. Cakmak worked with PR2 and Henry Evans when she was at Willow Garage, and currently has access to both a PR2 and a Fetch research robot. “When I think about my long term research vision, I want to deploy service robots in real homes,” Cakmak told us. Unfortunately, it’s the robots themselves that have been preventing her from doing this—both the Fetch and the PR2 are large enough that moving them anywhere requires a truck and a lift, which also limits the home that they can be used in. “For me, I felt immediately that Stretch is very different, and it makes a lot of sense,” she says. “It’s safe and lightweight, you can probably put it in the backseat of a car.” For Cakmak, Stretch’s size is the difference between being able to easily take a robot to the places she wants to do research in, and not. And cost is a factor as well, since a cheaper robot means more access for her students. “I got my refurbished PR2 for $180,000,” Cakmak says. “For that, with Stretch I could have 10!”

“I felt immediately that Stretch is very different. It’s safe and lightweight, you can probably put it in the backseat of a car. I got my refurbished PR2 for $180,000. For that, with Stretch I could have 10!”
—Maya Cakmak, University of Washington

Of course, a portable robot doesn’t do you any good if the robot itself isn’t sophisticated enough to do what you need it to do. Stretch is certainly a compromise in functionality in the interest of small size and low cost, but it’s a compromise that’s been carefully thought out, based on the experience that Edsinger has building robots and the experience that Kemp has operating robots in homes. For example, most mobile manipulators are essentially multi-degrees-of-freedom arms on mobile bases. Stretch instead leverages its wheeled base to move its arm in the horizontal plane, which (most of the time) works just as well as an extra DoF or two on the arm while saving substantially on weight and cost. Similarly, Stretch relies almost entirely on one sensor, an Intel RealSense D435i on a pan-tilt head that gives it a huge range of motion. The RealSense serves as a navigation camera, manipulation camera, a 3D mapping system, and more. It’s not going to be quite as good for a task that might involve fine manipulation, but most of the time it’s totally workable and you’re saving on cost and complexity.

Stretch has been relentlessly optimized to be the absolutely minimum robot to do mobile manipulation in a home or workplace environment. In practice, this meant figuring out exactly what it was absolutely necessary for Stretch to be able to do. With an emphasis on manipulation, that meant defining the workspace of the robot, or what areas it’s able to usefully reach. “That was one thing we really had to push hard on,” says Edsinger. “Reachability.” He explains that reachability and a small mobile base tend not to go together, because robot arms (which tend to weigh a lot) can cause a small base to tip, especially if they’re moving while holding a payload. At the same time, Stretch needed to be able to access both countertops and the floor, while being able to reach out far enough to hand people things without having to be right next to them. To come up with something that could meet all those requirements, Edsinger and Kemp set out to reinvent the robot arm.

Stretch’s key innovation: a stretchable arm
The design they came up with is rather ingenious in its simplicity and how well it works. Edsinger explains that the arm consists of five telescoping links: one fixed and four moving. They are constructed of custom carbon fiber, and are driven by a single motor, which is attached to the robot’s vertical pole. The strong, lightweight structure allows the arm to extend over half a meter and hold up to 1.5 kg. Although the company has a patent pending for the design, Edsinger declined to say whether the links are driven by a belt, cables, or gears. “We don’t want to disclose too much of the secret sauce [with regard to] the drive mechanism.” He added that the arm was “one of the most significant engineering challenges on the robot in terms of getting the desired reach, compactness, precision, smoothness, force sensitivity, and low cost to all happily coexist.”

Photo: Hello Robot

Stretch’s arm consists of five telescoping links constructed of custom carbon fiber, and are driven by a single motor, which is attached to the robot’s vertical pole, minimizing weight and inertia. The arm has a reach of over half a meter and can hold up to 1.5 kg.

Another interesting features of Stretch is its interface with the world—its gripper. There are countless different gripper designs out there, each and every one of which is the best at gripping some particular subset of things. But making a generalized gripper for all of the stuff that you’d find in a home is exceptionally difficult. Ideally, you’d want some sort of massive experimental test program where thousands and thousands of people test out different gripper designs in their homes for long periods of time and then tell you which ones work best. Obviously, that’s impractical for a robotics startup, but Kemp realized that someone else was already running the study for him: Amazon.

“I had this idea that there are these assistive grabbers that people with disabilities use to grasp objects in the real world,” he told us. Kemp went on Amazon’s website and looked at the top 10 grabbers and the reviews from thousands of users. He then bought a bunch of different ones and started testing them. “This one [Stretch’s gripper], I almost didn’t order it, it was such a weird looking thing,” he says. “But it had great reviews on Amazon, and oh my gosh, it just blew away the other grabbers. And I was like, that’s it. It just works.”

Stretch’s teleoperated and autonomous capabilities
As with any robot intended to be useful outside of a structured environment, hardware is only part of the story, and arguably not even the most important part. In order for Stretch to be able to operate out from under the supervision of a skilled roboticist, it has to be either easy to control, or autonomous. Ideally, it’s both, and that’s what Hello Robot is working towards, although things didn’t start out that way, Kemp explains. “From a minimalist standpoint, we began with the notion that this would be a teleoperated robot. But in the end, you just don’t get the real power of the robot that way, because you’re tied to a person doing stuff. As much as we fought it, autonomy really is a big part of the future for this kind of system.”

Here’s a look at some of Stretch’s teleoperated capabilities. We’re told that Stretch is very easy to get going right out of the box, although this teleoperation video from Hello Robot looks like it’s got a skilled and experienced user in the loop:

For such a low-cost platform, the autonomy (even at this early stage) is particularly impressive:

Since it’s not entirely clear from the video exactly what’s autonomous, here’s a brief summary of a couple of the more complex behaviors that Kemp sent us:

Object grasping: Stretch uses its 3D camera to find the nearest flat surface using a virtual overhead view. It then segments significant blobs on top of the surface. It selects the largest blob in this virtual overhead view and fits an ellipse to it. It then generates a grasp plan that makes use of the center of the ellipse and the major and minor axes. Once it has a plan, Stretch orients its gripper, moves to the pre-grasp pose, moves to the grasp pose, closes its gripper based on the estimated object width, lifts up, and retracts.
Mapping, navigating, and reaching to a 3D point: These demonstrations all use FUNMAP (Fast Unified Navigation, Manipulation and Planning). It’s all novel custom Python code. Even a single head scan performed by panning the 3D camera around can result in a very nice 3D representation of Stretch’s surroundings that includes the nearby floor. This is surprisingly unusual for robots, which often have their cameras too low to see many interesting things in a human environment. While mapping, Stretch selects where to scan next in a non-trivial way that considers factors such as the quality of previous observations, expected new observations, and navigation distance. The plan that Stretch uses to reach the target 3D point has been optimized for navigation and manipulation. For example, it finds a final robot pose that provides a large manipulation workspace for Stretch, which must consider nearby obstacles, including obstacles on the ground.
Object handover: This is a simple demonstration of object handovers. Stretch performs Cartesian motions to move its gripper to a body-relative position using a good motion heuristic, which is to extend the arm as the last step. These simple motions work well due to the design of Stretch. It still surprises me how well it moves the object to comfortable places near my body, and how unobtrusive it is. The goal point is specified relative to a 3D frame attached to the person’s mouth estimated using deep learning models (shown in the RViz visualization video). Specifically, Stretch targets handoff at a 3D point that is 20 cm below the estimated position of the mouth and 25 cm away along the direction of reaching.

Much of these autonomous capabilities come directly from Kemp’s lab, and the demo code is available for anyone to use. (Hello Robot says all of Stretch’s software is open source.)

Photo: Hello Robot

Hello Robot co-founder and CEO Aaron Edsinger says Stretch is designed to work with people in homes and workplaces and can be teleoperated to do a variety of tasks, including picking up toys, removing laundry from a dryer, and playing games with kids.

As of right now, Stretch is very much a research platform. You’re going to see it in research labs doing research things, and hopefully in homes and commercial spaces as well, but still under the supervision of professional roboticists. As you may have guessed, though, Hello Robot’s vision is a bit broader than that. “The impact we want to have is through robots that are helpful to people in society,” Edsinger says. “We think primarily in the home context, but it could be in healthcare, or in other places. But we really want to have our robots be impactful, and useful. To us, useful is exciting.” Adds Kemp: “I have a personal bias, but we’d really like this technology to benefit older adults and caregivers. Rather than creating a specialized assistive device, we want to eventually create an inexpensive consumer device for everyone that does lots of things.”

Neither Edsinger nor Kemp would say much more on this for now, and they were very explicit about why—they’re being deliberately cautious about raising expectations, having seen what’s happened to some other robotics companies over the past few years. Without VC funding (Hello Robot is currently bootstrapping itself into existence), Stretch is being sold entirely on its own merits. So far, it seems to be working. Stretch robots are already in a half dozen research labs, and we expect that with today’s announcement, we’ll start seeing them much more frequently.

This article appears in the October 2020 print issue as “A Robot That Keeps It Simple.” Continue reading →

Posted in Human Robots

#437758 Remotely Operated Robot Takes Straight ...

Posted on November 28, 2020 by Android

Roboticists love hard problems. Challenges like the DRC and SubT have helped (and are still helping) to catalyze major advances in robotics, but not all hard problems require a massive amount of DARPA funding—sometimes, a hard problem can just be something very specific that’s really hard for a robot to do, especially relative to the ease with which a moderately trained human might be able to do it. Catching a ball. Putting a peg in a hole. Or using a straight razor to shave someone’s face without Sweeney Todd-izing them.

This particular roboticist who sees straight-razor face shaving as a hard problem that robots should be solving is John Peter Whitney, who we first met back at IROS 2014 in Chicago when (working at Disney Research) he introduced an elegant fluidic actuator system. These actuators use tubes containing a fluid (like air or water) to transmit forces from a primary robot to a secondary robot in a very efficient way that also allows for either compliance or very high fidelity force feedback, depending on the compressibility of the fluid.

Photo: John Peter Whitney/Northeastern University

Barber meets robot: Boston based barber Jesse Cabbage [top, right] observes the machine created by roboticist John Peter Whitney. Before testing the robot on Whitney’s face, they used his arm for a quick practice [bottom].

Whitney is now at Northeastern University, in Boston, and he recently gave a talk at the RSS workshop on “Reacting to Contact,” where he suggested that straight razor shaving would be an interesting and valuable problem for robotics to work toward, due to its difficulty and requirement for an extremely high level of both performance and reliability.

Now, a straight razor is sort of like a safety razor, except with the safety part removed, which in fact does make it significantly less safe for humans, much less robots. Also not ideal for those worried about safety is that as part of the process the razor ends up in distressingly close proximity to things like the artery that is busily delivering your brain’s entire supply of blood, which is very close to the top of the list of things that most people want to keep blades very far away from. But that didn’t stop Whitney from putting his whiskers where his mouth is and letting his robotic system mediate the ministrations of a professional barber. It’s not an autonomous robotic straight-razor shave (because Whitney is not totally crazy), but it’s a step in that direction, and requires that the hardware Whitney developed be dead reliable.

Perhaps that was a poor choice of words. But, rest assured that Whitney lived long enough to answer our questions after. Here’s the video; it’s part of a longer talk, but it should start in the right spot, at about 23:30.

If Whitney looked a little bit nervous to you, that’s because he was. “This was the first time I’d ever been shaved by someone (something?!) else with a straight razor,” he told us, and while having a professional barber at the helm was some comfort, “the lack of feeling and control on my part was somewhat unsettling.” Whitney says that the barber, Jesse Cabbage of Dentes Barbershop in Somerville, Mass., was surprised by how well he could feel the tactile sensations being transmitted from the razor. “That’s one of the reasons we decided to make this video,” Whitney says. “I can’t show someone how something feels, so the next best thing is to show a delicate task that either from experience or intuition makes it clear to the viewer that the system must have these properties—otherwise the task wouldn’t be possible.”

And as for when Whitney might be comfortable getting shaved by a robotic system without a human in the loop? It’s going to take a lot of work, as do most other hard problems in robotics. “There are two parts to this,” he explains. “One is fault-tolerance of the components themselves (software, electronics, etc.) and the second is the quality of the perception and planning algorithms.”

He offers a comparison to self-driving cars, in which similar (or greater) risks are incurred: “To learn how to perceive, interpret, and adapt, we need a very high-fidelity model of the problem, or a wealth of data and experience, or both” he says. “But in the case of shaving we are greatly lacking in both!” He continues with the analogy: “I think there is a natural progression—the community started with autonomous driving of toy cars on closed courses and worked up to real cars carrying human passengers; in robotic manipulation we are beginning to move out of the ‘toy car’ stage and so I think it’s good to target high-consequence hard problems to help drive progress.”

The ultimate goal is much more general than the creation of a dedicated straight razor shaving robot. This particular hardware system is actually a testbed for exploring MRI-compatible remote needle biopsy.

Of course, the ultimate goal here is much more general than the creation of a dedicated straight razor shaving robot; it’s a challenge that includes a host of sub-goals that will benefit robotics more generally. This particular hardware system Whitney is developing is actually a testbed for exploring MRI-compatible remote needle biopsy, and he and his students are collaborating with Brigham and Women’s Hospital in Boston on adapting this technology to prostate biopsy and ablation procedures. They’re also exploring how delicate touch can be used as a way to map an environment and localize within it, especially where using vision may not be a good option. “These traits and behaviors are especially interesting for applications where we must interact with delicate and uncertain environments,” says Whitney. “Medical robots, assistive and rehabilitation robots and exoskeletons, and shared-autonomy teleoperation for delicate tasks.”
A paper with more details on this robotic system, “Series Elastic Force Control for Soft Robotic Fluid Actuators,” is available on arXiv. Continue reading →

Posted in Human Robots

#437735 Robotic Chameleon Tongue Snatches Nearby ...

Posted on November 27, 2020 by Android

Chameleons may be slow-moving lizards, but their tongues can accelerate at astounding speeds, snatching insects before they have any chance of fleeing. Inspired by this remarkable skill, researchers in South Korea have developed a robotic tongue that springs forth quickly to snatch up nearby items.

They envision the tool, called Snatcher, being used by drones and robots that need to collect items without getting too close to them. “For example, a quadrotor with this manipulator will be able to snatch distant targets, instead of hovering and picking up,” explains Gwang-Pil Jung, a researcher at Seoul National University of Science and Technology (SeoulTech) who co-designed the new device.

There has been other research into robotic chameleon tongues, but what’s unique about Snatcher is that it packs chameleon-tongue fast snatching performance into a form factor that’s portable—the total size is 12 x 8.5 x 8.5 centimeters and it weighs under 120 grams. Still, it’s able to fast snatch up to 30 grams from 80 centimeters away in under 600 milliseconds.

Image: SeoulTech

The fast snatching deployable arm is powered by a wind-up spring attached to a motor (a series elastic actuator) combined with an active clutch. The clutch is what allows the single spring to drive both the shooting and the retracting.

To create Snatcher, Jung and a colleague at SeoulTech, Dong-Jun Lee, set about developing a spring-like device that’s controlled by an active clutch combined with a single series elastic actuator. Powered by a wind-up spring, a steel tapeline—analogous to a chameleon’s tongue—passes through two geared feeders. The clutch is what allows the single spring unwinding in one direction to drive both the shooting and the retracting, by switching a geared wheel between driving the forward feeder or the backward feeder.

The end result is a lightweight snatching device that can retrieve an object 0.8 meters away within 600 milliseconds. Jung notes that some other, existing devices designed for retrieval are capable of accomplishing the task quicker, at about 300 milliseconds, but these designs tend to be bulky. A more detailed description of Snatcher was published July 21 in IEEE Robotics and Automation Letters.

Photo: Dong-Jun Lee and Gwang-Pil Jung/SeoulTech

Snatcher’s relative small size means that it can be installed on a DJI Phantom drone. The researchers want to find out if their system can help make package delivery or retrieval faster and safer.

“Our final goal is to install the Snatcher to a commercial drone and achieve meaningful work, such as grasping packages,” says Jung. One of the challenges they still need to address is how to power the actuation system more efficiently. “To solve this issue, we are finding materials having high energy density.” Another improvement is designing a chameleon tongue-like gripper, replacing the simple hook that’s currently used to pick up objects. “We are planning to make a bi-stable gripper to passively grasp a target object as soon as the gripper contacts the object,” says Jung.

< Back to IEEE Journal Watch Continue reading →

Posted in Human Robots

#437721 Video Friday: Child Robot Learning to ...

Posted on November 26, 2020 by Android

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!):

CLAWAR 2020 – August 24-26, 2020 – [Online Conference]
ICUAS 2020 – September 1-4, 2020 – Athens, Greece
ICRES 2020 – September 28-29, 2020 – Taipei, Taiwan
AUVSI EXPONENTIAL 2020 – October 5-8, 2020 – [Online Conference]
IROS 2020 – October 25-29, 2020 – Las Vegas, Nev., USA
CYBATHLON 2020 – November 13-14, 2020 – [Online Event]
ICSR 2020 – November 14-16, 2020 – Golden, Colo., USA
Let us know if you have suggestions for next week, and enjoy today’s videos.

We first met Ibuki, Hiroshi Ishiguro’s latest humanoid robot, a couple of years ago. A recent video shows how Ishiguro and his team are teaching the robot to express its emotional state through gait and body posture while moving.

This paper presents a subjective evaluation of the emotions of a wheeled mobile humanoid robot expressing emotions during movement by replicating human gait-induced upper body motion. For this purpose, we proposed the robot equipped with a vertical oscillation mechanism that generates such motion by focusing on human center-of-mass trajectory. In the experiment, participants watched videos of the robot’s different emotional gait-induced upper body motions, and assess the type of emotion shown, and their confidence level in their answer.

[ Hiroshi Ishiguro Lab ] via [ RobotStart ]

ICYMI: This is a zinc-air battery made partly of Kevlar that can be used to support weight, not just add to it.

Like biological fat reserves store energy in animals, a new rechargeable zinc battery integrates into the structure of a robot to provide much more energy, a team led by the University of Michigan has shown.

The new battery works by passing hydroxide ions between a zinc electrode and the air side through an electrolyte membrane. That membrane is partly a network of aramid nanofibers—the carbon-based fibers found in Kevlar vests—and a new water-based polymer gel. The gel helps shuttle the hydroxide ions between the electrodes. Made with cheap, abundant and largely nontoxic materials, the battery is more environmentally friendly than those currently in use. The gel and aramid nanofibers will not catch fire if the battery is damaged, unlike the flammable electrolyte in lithium ion batteries. The aramid nanofibers could be upcycled from retired body armor.

[ University of Michigan ]

In what they say is the first large-scale study of the interactions between sound and robotic action, researchers at CMU’s Robotics Institute found that sounds could help a robot differentiate between objects, such as a metal screwdriver and a metal wrench. Hearing also could help robots determine what type of action caused a sound and help them use sounds to predict the physical properties of new objects.

[ CMU ]

Captured on Aug. 11 during the second rehearsal of the OSIRIS-REx mission’s sample collection event, this series of images shows the SamCam imager’s field of view as the NASA spacecraft approaches asteroid Bennu’s surface. The rehearsal brought the spacecraft through the first three maneuvers of the sampling sequence to a point approximately 131 feet (40 meters) above the surface, after which the spacecraft performed a back-away burn.

These images were captured over a 13.5-minute period. The imaging sequence begins at approximately 420 feet (128 meters) above the surface – before the spacecraft executes the “Checkpoint” maneuver – and runs through to the “Matchpoint” maneuver, with the last image taken approximately 144 feet (44 meters) above the surface of Bennu.

[ NASA ]

The DARPA AlphaDogfight Trials Final Event took place yesterday; the livestream is like 5 hours long, but you can skip ahead to 4:39 ish to see the AI winner take on a human F-16 pilot in simulation.

Some things to keep in mind about the result: The AI had perfect situational knowledge while the human pilot had to use eyeballs, and in particular, the AI did very well at lining up its (virtual) gun with the human during fast passing maneuvers, which is the sort of thing that autonomous systems excel at but is not necessarily reflective of better strategy.

[ DARPA ]

Coming soon from Clearpath Robotics!

[ Clearpath ]

This video introduces Preferred Networks’ Hand type A, a tendon-driven robot gripper with passively switchable underactuated surface.

[ Preferred Networks ]

CYBATHLON 2020 will take place on 13 – 14 November 2020 – at the teams’ home bases. They will set up their infrastructure for the competition and film their races. Instead of starting directly next to each other, the pilots will start individually and under the supervision of CYBATHLON officials. From Zurich, the competitions will be broadcast through a new platform in a unique live programme.

[ Cybathlon ]

In this project, we consider the task of autonomous car racing in the top-selling car racing game Gran Turismo Sport. Gran Turismo Sport is known for its detailed physics simulation of various cars and tracks. Our approach makes use of maximum-entropy deep reinforcement learning and a new reward design to train a sensorimotor policy to complete a given race track as fast as possible. We evaluate our approach in three different time trial settings with different cars and tracks. Our results show that the obtained controllers not only beat the built-in non-player character of Gran Turismo Sport, but also outperform the fastest known times in a dataset of personal best lap times of over 50,000 human drivers.

[ UZH ]

With the help of the software pitasc from Fraunhofer IPA, an assembly task is no longer programmed point by point, but workpiece-related. Thus, pitasc adapts the assembly process itself for new product variants with the help of updated parameters.

[ Fraunhofer ]

In this video, a multi-material robot simulator is used to design a shape-changing robot, which is then transferred to physical hardware. The simulated and real robots can use shape change to switch between rolling gaits and inchworm gaits, to locomote in multiple environments.

[ Yale ]

This work presents a novel loco-manipulation control framework for the execution of complex tasks with kinodynamic constraints using mobile manipulators. As a representative example, we consider the handling and re-positioning of pallet jacks in unstructured environments. While these results reveal with a proof-of- concept the effectiveness of the proposed framework, they also demonstrate the high potential of mobile manipulators for relieving human workers from such repetitive and labor intensive tasks. We believe that this extended functionality can contribute to increasing the usability of mobile manipulators in different application scenarios.

[ Paper ] via [ IIT ]

I don’t know why this dinosaur ice cream serving robot needs to blow smoke out of its nose, but I like it.

[ Connected Robotics ] via [ RobotStart ]

Guardian S remote visual inspection and surveillance robots make laying cable runs in confined or hard to reach spaces easy. With advanced maneuverability and the ability to climb vertical, ferrous surfaces, the robot reaches areas that are not always easily accessible.

[ Sarcos ]

Looks like the company that bought Anki is working on an add-on to let cars charge while they drive.

[ Digital Dream Labs ]

Chris Atkeson gives a brief talk for the CMU Robotics Institute orientation.

[ CMU RI ]

A UofT Robotics Seminar, featuring Russ Tedrake from MIT and TRI on “Feedback Control for Manipulation.”

Control theory has an answer for just about everything, but seems to fall short when it comes to closing a feedback loop using a camera, dealing with the dynamics of contact, and reasoning about robustness over the distribution of tasks one might find in the kitchen. Recent examples from RL and imitation learning demonstrate great promise, but don’t leverage the rigorous tools from systems theory. I’d like to discuss why, and describe some recent results of closing feedback loops from pixels for “category-level” robot manipulation.

[ UofT ] Continue reading →

Posted in Human Robots