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#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

#437807 Why We Need Robot Sloths

Posted on November 30, 2020 by Android

An inherent characteristic of a robot (I would argue) is embodied motion. We tend to focus on motion rather a lot with robots, and the most dynamic robots get the most attention. This isn’t to say that highly dynamic robots don’t deserve our attention, but there are other robotic philosophies that, while perhaps less visually exciting, are equally valuable under the right circumstances. Magnus Egerstedt, a robotics professor at Georgia Tech, was inspired by some sloths he met in Costa Rica to explore the idea of “slowness as a design paradigm” through an arboreal robot called SlothBot.

Since the robot moves so slowly, why use a robot at all? It may be very energy-efficient, but it’s definitely not more energy efficient than a static sensing system that’s just bolted to a tree or whatever. The robot moves, of course, but it’s also going to be much more expensive (and likely much less reliable) than a handful of static sensors that could cover a similar area. The problem with static sensors, though, is that they’re constrained by power availability, and in environments like under a dense tree canopy, you’re not going to be able to augment their lifetime with solar panels. If your goal is a long-duration study of a small area (over weeks or months or more), SlothBot is uniquely useful in this context because it can crawl out from beneath a tree to find some sun to recharge itself, sunbathe for a while, and then crawl right back again to resume collecting data.

SlothBot is such an interesting concept that we had to check in with Egerstedt with a few more questions.

IEEE Spectrum: Tell us what you find so amazing about sloths!

Magnus Egerstedt: Apart from being kind of cute, the amazing thing about sloths is that they have carved out a successful ecological niche for themselves where being slow is not only acceptable but actually beneficial. Despite their pretty extreme low-energy lifestyle, they exhibit a number of interesting and sometimes outright strange behaviors. And, behaviors having to do with territoriality, foraging, or mating look rather different when you are that slow.

Are you leveraging the slothiness of the design for this robot somehow?

Sadly, the sloth design serves no technical purpose. But we are also viewing the SlothBot as an outreach platform to get kids excited about robotics and/or conservation biology. And having the robot look like a sloth certainly cannot hurt.

“Slowness is ideal for use cases that require a long-term, persistent presence in an environment, like for monitoring tasks. I can imagine slow robots being out on farm fields for entire growing cycles, or suspended on the ocean floor keeping track of pollutants or temperature variations.”
—Magnus Egerstedt, Georgia Tech

Can you talk more about slowness as a design paradigm?

The SlothBot is part of a broader design philosophy that I have started calling “Robot Ecology.” In ecology, the connections between individuals and their environments/habitats play a central role. And the same should hold true in robotics. The robot design must be understood in the environmental context in which it is to be deployed. And, if your task is to be present in a slowly varying environment over a long time scale, being slow seems like the right way to go. Slowness is ideal for use cases that require a long-term, persistent presence in an environment, like for monitoring tasks, where the environment itself is slowly varying. I can imagine slow robots being out on farm fields for entire growing cycles, or suspended on the ocean floor keeping track of pollutants or temperature variations.

How do sloths inspire SlothBot’s functionality?

Its motions are governed by what we call survival constraints. These constraints ensure that the SlothBot is always able to get to a sunny spot to recharge. The actual performance objective that we have given to the robot is to minimize energy consumption, i.e., to simply do nothing subject to the survival constraints. The majority of the time, the robot simply sits there under the trees, measuring various things, seemingly doing absolutely nothing and being rather sloth-like. Whenever the SlothBot does move, it does not move according to some fixed schedule. Instead, it moves because it has to in order to “survive.”

How would you like to improve SlothBot?

I have a few directions I would like to take the SlothBot. One is to make the sensor suites richer to make sure that it can become a versatile and useful science instrument. Another direction involves miniaturization – I would love to see a bunch of small SlothBots “living” among the trees somewhere in a rainforest for years, providing real-time data as to what is happening to the ecosystem. Continue reading →

Posted in Human Robots

#437791 Is the Pandemic Spurring a Robot ...

Posted on November 30, 2020 by Android

“Are robots really destined to take over restaurant kitchens?” This was the headline of an article published by Eater four years ago. One of the experts interviewed was Siddhartha Srinivasa, at the time professor of the Robotics Institute at Carnegie Mellon University and currently director of Robotics and AI for Amazon. He said, “I’d love to make robots unsexy. It’s weird to say this, but when something becomes unsexy, it means that it works so well that you don’t have to think about it. You don’t stare at your dishwasher as it washes your dishes in fascination, because you know it’s gonna work every time… I want to get robots to that stage of reliability.”

Have we managed to get there over the last four years? Are robots unsexy yet? And how has the pandemic changed the trajectory of automation across industries?

The Covid Effect
The pandemic has had a massive economic impact all over the world, and one of the problems faced by many companies has been keeping their businesses running without putting employees at risk of infection. Many organizations are seeking to remain operational in the short term by automating tasks that would otherwise be carried out by humans. According to Digital Trends, since the start of the pandemic we have seen a significant increase in automation efforts in manufacturing, meat packing, grocery stores and more. In a June survey, 44 percent of corporate financial officers said they were considering more automation in response to coronavirus.

MIT economist David Autor described the economic crisis and the Covid-19 pandemic as “an event that forces automation.” But he added that Covid-19 created a kind of disruption that has forced automation in sectors and activities with a shortage of workers, while at the same time there has been no reduction in demand. This hasn’t taken place in hospitality, where demand has practically disappeared, but it is still present in agriculture and distribution. The latter is being altered by the rapid growth of e-commerce, with more efficient and automated warehouses that can provide better service.

China Leads the Way
China is currently in a unique position to lead the world’s automation economy. Although the country boasts a huge workforce, labor costs have multiplied by 10 over the past 20 years. As the world’s factory, China has a strong incentive to automate its manufacturing sector, which enjoys a solid leadership in high quality products. China is currently the largest and fastest-growing market in the world for industrial robotics, with a 21 percent increase up to $5.4 billion in 2019. This represents one third of global sales. As a result, Chinese companies are developing a significant advantage in terms of learning to work with metallic colleagues.

The reasons behind this Asian dominance are evident: the population has a greater capacity and need for tech adoption. A large percentage of the population will soon be of retirement age, without an equivalent younger demographic to replace it, leading to a pressing need to adopt automation in the short term.

China is well ahead of other countries in restaurant automation. As reported in Bloomberg, in early 2020 UBS Group AG conducted a survey of over 13,000 consumers in different countries and found that 64 percent of Chinese participants had ordered meals through their phones at least once a week, compared to a mere 17 percent in the US. As digital ordering gains ground, robot waiters and chefs are likely not far behind. The West harbors a mistrust towards non-humans that the East does not.

The Robot Evolution
The pandemic was a perfect excuse for robots to replace us. But despite the hype around this idea, robots have mostly disappointed during the pandemic.

Just over 66 different kinds of “social” robots have been piloted in hospitals, health centers, airports, office buildings, and other public and private spaces in response to the pandemic, according to a study from researchers at Pompeu Fabra University (Barcelona, Spain). Their survey looked at 195 robot deployments across 35 countries including China, the US, Thailand, and Hong Kong.

But if the “robot revolution” is a movement in which automation, robotics, and artificial intelligence proliferate through the value chain of various industries, bringing a paradigm shift in how we produce, consume, and distribute products—it hasn’t happened yet.

But there’s a more nuanced answer: rather than a revolution, we’re seeing an incremental robot evolution. It’s a trend that will likely accelerate over the next five years, particularly when 5G takes center stage and robotics as a field leaves behind imitation and evolves independently.

Automation Anxiety
Why don’t we finally welcome the long-promised robotic takeover? Despite progress in AI and increased adoption of industrial robots, consumer-facing robotic products are not nearly as ubiquitous as popular culture predicted decades ago. As Amara’s Law says: “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.” It seems we are living through the Gartner hype cycle.

People have a complicated relationship with robots, torn between admiring them, fearing them, rejecting them, and even boycotting them, as has happened in the automobile industry.

Retail robot in a Walmart store. Credit: Bossa Nova Robotics
Walmart terminated its contract with Bossa Nova and withdrew its 1,000 inventory robots from its stores because the company was concerned about how shoppers were reacting to seeing the six-foot robots in the aisles.

With road blocks like this, will the World Economic Forum’s prediction of almost half of tasks being carried out by machines by 2025 come to pass?

At the rate we’re going, it seems unlikely, even with the boost in automation caused by the pandemic. Robotics will continue to advance its capabilities, and will take over more human jobs as it does so, but it’s unlikely we’ll hit a dramatic inflection point that could be described as a “revolution.” Instead, the robot evolution will happen the way most societal change does: incrementally, with time for people to adapt both practically and psychologically.

For now though, robots are still pretty sexy.

Image Credit: charles taylor / Shutterstock.com 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

#437778 A Bug-Sized Camera for Bug-Sized Robots ...

Posted on November 29, 2020 by Android

As if it’s not hard enough to make very small mobile robots, once you’ve gotten the power and autonomy all figured out (good luck with that), your robot isn’t going to be all that useful unless it can carry some payload. And the payload that everybody wants robots to carry is a camera, which is of course a relatively big, heavy, power hungry payload. Great, just great.

This whole thing is frustrating because tiny, lightweight, power efficient vision systems are all around us. Literally, all around us right this second, stuffed into the heads of insects. We can’t make anything quite that brilliant (yet), but roboticists from the University of Washington, in Seattle, have gotten us a bit closer, with the smallest wireless, steerable video camera we’ve ever seen—small enough to fit on the back of a microbot, or even a live bug.

To make a camera this small, the UW researchers, led by Shyam Gollakota, a professor of computer science and engineering, had to start nearly from scratch, primarily because existing systems aren’t nearly so constrained by power availability. Even things like swallowable pill cameras require batteries that weigh more than a gram, but only power the camera for under half an hour. With a focus on small size and efficiency, they started with an off-the-shelf ultra low-power image sensor that’s 2.3 mm wide and weighs 6.7 mg. They stuck on a Bluetooth 5.0 chip (3 mm wide, 6.8 mg), and had a fun time connecting those two things together without any intermediary hardware to broadcast the camera output. A functional wireless camera also requires a lens (20 mg) and an antenna, which is just 5 mm of wire. An accelerometer is useful so that insect motion can be used to trigger the camera, minimizing the redundant frames that you’d get from a robot or an insect taking a nap.

Photo: University of Washington

The microcamera developed by the UW researchers can stream monochrome video at up to 5 frames per second to a cellphone 120 meters away.

The last bit to make up this system is a mechanically steerable “head,” weighing 35 mg and bringing the total weight of the wireless camera system to 84 mg. If the look of the little piezoelectric actuator seems familiar, you have very good eyes because it’s tiny, and also, it’s the same kind of piezoelectric actuator that the folks at UW use to power their itty bitty flying robots. It’s got a 60-degree panning range, but also requires a 96 mg boost converter to function, which is a huge investment in size and weight just to be able to point the camera a little bit. But overall, the researchers say that this pays off, because not having to turn the entire robot (or insect) when you want to look around reduces the energy consumption of the system as a whole by a factor of up to 84 (!).

Photo: University of Washington

Insects are very mobile platforms for outdoor use, but they’re also not easy to steer, so the researchers also built a little insect-scale robot that they could remotely control while watching the camera feed. As it turns out, this seems to be the smallest, power-autonomous terrestrial robot with a camera ever made.

This efficiency means that the wireless camera system can stream video frames (160×120 pixels monochrome) to a cell phone up to 120 meters away for up to 6 hours when powered by a 0.5-g, 10-mAh battery. A live, first-bug view can be streamed at up to 5 frames per second. The system was successfully tested on a pair of darkling beetles that were allowed to roam freely outdoors, and the researchers noted that they could also mount it on spiders or moths, or anything else that could handle the payload. (The researchers removed the electronics from the insects after the experiments and observed no noticeable adverse effects on their behavior.)

The researchers are already thinking about what it might take to put a wireless camera system on something that flies, and it’s not going to be easy—a bumblebee can only carry between 100 and 200 mg. The power system is the primary limitation here, but it might be possible to use a solar cell to cut down on battery requirements. And the camera itself could be scaled down as well, by using a completely custom sensor and a different type of lens. The other thing to consider is that with a long-range wireless link and a vision system, it’s possible to add sophisticated vision-based autonomy to tiny robots by doing the computation remotely. So, next time you see something scuttling across the ground, give it another look, because it might be looking right back at you.

“Wireless steerable vision for live insects and insect-scale robots,” by Vikram Iyer, Ali Najafi, Johannes James, Sawyer Fuller, and Shyamnath Gollakota from the University of Washington, is published in Science Robotics. Continue reading →

Posted in Human Robots