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#437884 Hyundai Buys Boston Dynamics for Nearly ...

This morning just after 3 a.m. ET, Boston Dynamics sent out a media release confirming that Hyundai Motor Group has acquired a controlling interest in the company that values Boston Dynamics at US $1.1 billion:

Under the agreement, Hyundai Motor Group will hold an approximately 80 percent stake in Boston Dynamics and SoftBank, through one of its affiliates, will retain an approximately 20 percent stake in Boston Dynamics after the closing of the transaction.

The release is very long, but does have some interesting bits—we’ll go through them, and talk about what this might mean for both Boston Dynamics and Hyundai.

We’ve asked Boston Dynamics for comment, but they’ve been unusually quiet for the last few days (I wonder why!). So at this point just keep in mind that the only things we know for sure are the ones in the release. If (when?) we hear anything from either Boston Dynamics or Hyundai, we’ll update this post.

The first thing to be clear on is that the acquisition is split between Hyundai Motor Group’s affiliates, including Hyundai Motor, Hyundai Mobis, and Hyundai Glovis. Hyundai Motor makes cars, Hyundai Mobis makes car parts and seems to be doing some autonomous stuff as well, and Hyundai Glovis does logistics. There are many other groups that share the Hyundai name, but they’re separate entities, at least on paper. For example, there’s a Hyundai Robotics, but that’s part of Hyundai Heavy Industries, a different company than Hyundai Motor Group. But for this article, when we say “Hyundai,” we’re talking about Hyundai Motor Group.

What’s in it for Hyundai?
Let’s get into the press release, which is filled with press release-y terms like “synergies” and “working together”—you can view the whole thing here—but still has some parts that convey useful info.

By establishing a leading presence in the field of robotics, the acquisition will mark another major step for Hyundai Motor Group toward its strategic transformation into a Smart Mobility Solution Provider. To propel this transformation, Hyundai Motor Group has invested substantially in development of future technologies, including in fields such as autonomous driving technology, connectivity, eco-friendly vehicles, smart factories, advanced materials, artificial intelligence (AI), and robots.

If Hyundai wants to be a “Smart Mobility Solution Provider” with a focus on vehicles, it really seems like there’s a whole bunch of other ways they could have spent most of a billion dollars that would get them there quicker. Will Boston Dynamics’ expertise help them develop autonomous driving technology? Sure, I guess, but why not just buy an autonomous car startup instead? Boston Dynamics is more about “robots,” which happens to be dead last on the list above.

There was some speculation a couple of weeks ago that Hyundai was going to try and leverage Boston Dynamics to make a real version of this hybrid wheeled/legged concept car, so if that’s what Hyundai means by “Smart Mobility Solution Provider,” then I suppose the Boston Dynamics acquisition makes more sense. Still, I think that’s unlikely, because it’s just a concept car, after all.

In addition to “smart mobility,” which seems like a longer-term goal for Hyundai, the company also mentions other, more immediate benefits from the acquisition:

Advanced robotics offer opportunities for rapid growth with the potential to positively impact society in multiple ways. Boston Dynamics is the established leader in developing agile, mobile robots that have been successfully integrated into various business operations. The deal is also expected to allow Hyundai Motor Group and Boston Dynamics to leverage each other’s respective strengths in manufacturing, logistics, construction and automation.

“Successfully integrated” might be a little optimistic here. They’re talking about Spot, of course, but I think the best you could say at this point is that Spot is in the middle of some promising pilot projects. Whether it’ll be successfully integrated in the sense that it’ll have long-term commercial usefulness and value remains to be seen. I’m optimistic about this as well, but Spot is definitely not there yet.

What does probably hold a lot of value for Hyundai is getting Spot, Pick, and perhaps even Handle into that “manufacturing, logistics, construction” stuff. This is the bread and butter for robots right now, and Boston Dynamics has plenty of valuable technology to offer in those spaces.

Photo: Bob O’Connor

Boston Dynamics is selling Spot for $74,500, shipping included.

Betting on Spot and Pick
With Boston Dynamics founder Marc Raibert’s transition to Chairman of the company, the CEO position is now occupied by Robert Playter, the long-time VP of engineering and more recently COO at Boston Dynamics. Here’s his statement from the release:

“Boston Dynamics’ commercial business has grown rapidly as we’ve brought to market the first robot that can automate repetitive and dangerous tasks in workplaces designed for human-level mobility. We and Hyundai share a view of the transformational power of mobility and look forward to working together to accelerate our plans to enable the world with cutting edge automation, and to continue to solve the world’s hardest robotics challenges for our customers.”

Whether Spot is in fact “the first robot that can automate repetitive and dangerous tasks in workplaces designed for human-level mobility” on the market is perhaps something that could be argued against, although I won’t. Whether or not it was the first robot that can do these kinds of things, it’s definitely not the only robot that do these kinds of things, and going forward, it’s going to be increasingly challenging for Spot to maintain its uniqueness.

For a long time, Boston Dynamics totally owned the quadruped space. Now, they’re one company among many—ANYbotics and Unitree are just two examples of other quadrupeds that are being successfully commercialized. Spot is certainly very capable and easy to use, and we shouldn’t underestimate the effort required to create a robot as complex as Spot that can be commercially used and supported. But it’s not clear how long they’ll maintain that advantage, with much more affordable platforms coming out of Asia, and other companies offering some unique new capabilities.

Photo: Boston Dynamics

Boston Dynamics’ Handle is an all-electric robot featuring a leg-wheel hybrid mobility system, a manipulator arm with a vacuum gripper, and a counterbalancing tail.

Boston Dynamics’ picking system, which stemmed from their 2019 acquisition of Kinema Systems, faces the same kinds of challenges—it’s very good, but it’s not totally unique.

Boston Dynamics produces highly capable mobile robots with advanced mobility, dexterity and intelligence, enabling automation in difficult, dangerous, or unstructured environments. The company launched sales of its first commercial robot, Spot in June of 2020 and has since sold hundreds of robots in a variety of industries, such as power utilities, construction, manufacturing, oil and gas, and mining. Boston Dynamics plans to expand the Spot product line early next year with an enterprise version of the robot with greater levels of autonomy and remote inspection capabilities, and the release of a robotic arm, which will be a breakthrough in mobile manipulation.

Boston Dynamics is also entering the logistics automation market with the industry leading Pick, a computer vision-based depalletizing solution, and will introduce a mobile robot for warehouses in 2021.

Huh. We’ll be trying to figure out what “greater levels of autonomy” means, as well as whether the “mobile robot for warehouses” is Handle, or something more like an autonomous mobile robot (AMR) platform. I’d honestly be surprised if Handle was ready for work outside of Boston Dynamics next year, and it’s hard to imagine how Boston Dynamics could leverage their expertise into the AMR space with something that wouldn’t just seem… Dull, compared to what they usually do. I hope to be surprised, though!

A new deep-pocketed benefactor

Hyundai Motor Group’s decision to acquire Boston Dynamics is based on its growth potential and wide range of capabilities.

“Wide range of capabilities” we get, but that other phrase, “growth potential,” has a heck of a lot wrapped up in it. At the moment, Boston Dynamics is nowhere near profitable, as far as we know. SoftBank acquired Boston Dynamics in 2017 for between one hundred and two hundred million, and over the last three years they’ve poured hundreds of millions more into Boston Dynamics.

Hyundai’s 80 percent stake just means that they’ll need to take over the majority of that support, and perhaps even increase it if Boston Dynamics’ growth is one of their primary goals. Hyundai can’t have a reasonable expectation that Boston Dynamics will be profitable any time soon; they’re selling Spots now, but it’s an open question whether Spot will manage to find a scalable niche in which it’ll be useful in the sort of volume that will make it a sustainable commercial success. And even if it does become a success, it seems unlikely that Spot by itself will make a significant dent in Boston Dynamics’ burn rate anytime soon. Boston Dynamics will have more products of course, but it’s going to take a while, and Hyundai will need to support them in the interim.

Depending on whether Hyundai views Boston Dynamics as a company that does research or a company that makes robots that are useful and profitable, it may be difficult for Boston Dynamics to justify the cost to develop the
next Atlas, when the
current one still seems so far from commercialization

It’s become clear that to sustain itself, Boston Dynamics needs a benefactor with very deep pockets and a long time horizon. Initially, Boston Dynamics’ business model (or whatever you want to call it) was to do bespoke projects for defense-ish folks like DARPA, but from what we understand Boston Dynamics stopped that sort of work after Google acquired them back in 2013. From one perspective, that government funding did exactly what it was supposed to do, which was to fund the development of legged robots through low TRLs (technology readiness levels) to the point where they could start to explore commercialization.

The question now, though, is whether Hyundai is willing to let Boston Dynamics undertake the kinds of low-TRL, high-risk projects that led from BigDog to LS3 to Spot, and from PETMAN to DRC Atlas to the current Atlas. So will Hyundai be cool about the whole thing and be the sort of benefactor that’s willing to give Boston Dynamics the resources that they need to keep doing what they’re doing, without having to answer too many awkward questions about things like practicality and profitability? Hyundai can certainly afford to do this, but so could SoftBank, and Google—the question is whether Hyundai will want to, over the length of time that’s required for the development of the kind of ultra-sophisticated robotics hardware that Boston Dynamics specializes in.

To put it another way: Depending whether Hyundai’s perspective on Boston Dynamics is as a company that does research or a company that makes robots that are useful and profitable, it may be difficult for Boston Dynamics to justify the cost to develop the next Atlas, when the current one still seems so far from commercialization.

Google, SoftBank, now Hyundai

Boston Dynamics possesses multiple key technologies for high-performance robots equipped with perception, navigation, and intelligence.

Hyundai Motor Group’s AI and Human Robot Interaction (HRI) expertise is highly synergistic with Boston Dynamics’s 3D vision, manipulation, and bipedal/quadruped expertise.

As it turns out, Hyundai Motors does have its own robotics lab, called Hyundai Motors Robotics Lab. Their website is not all that great, but here’s a video from last year:

I’m not entirely clear on what Hyundai means when they use the word “synergistic” when they talk about their robotics lab and Boston Dynamics, but it’s a little bit concerning. Usually, when a big company buys a little company that specializes in something that the big company is interested in, the idea is that the little company, to some extent, will be absorbed into the big company to give them some expertise in that area. Historically, however, Boston Dynamics has been highly resistant to this, maintaining its post-acquisition independence and appearing to be very reluctant to do anything besides what it wants to do, at whatever pace it wants to do it, and as by itself as possible.

From what we understand, Boston Dynamics didn’t integrate particularly well with Google’s robotics push in 2013, and we haven’t seen much evidence that SoftBank’s experience was much different. The most direct benefit to SoftBank (or at least the most visible one) was the addition of a fleet of Spot robots to the SoftBank Hawks baseball team cheerleading squad, along with a single (that we know about) choreographed gymnastics routine from an Atlas robot that was only shown on video.

And honestly, if you were a big manufacturing company with a bunch of money and you wanted to build up your own robotics program quickly, you’d probably have much better luck picking up some smaller robotics companies who were a bit less individualistic and would probably be more amenable to integration and would cost way less than a billion dollars-ish. And if integration is ultimately Hyundai’s goal, we’ll be very sad, because it’ll likely signal the end of Boston Dynamics doing the unfettered crazy stuff that we’ve grown to love.

Photo: Bob O’Connor

Possibly the most agile humanoid robot ever built, Atlas can run, climb, jump over obstacles, and even get up after a fall.

Boston Dynamics contemplates its future

The release ends by saying that the transaction is “subject to regulatory approvals and other customary closing conditions” and “is expected to close by June of 2021.” Again, you can read the whole thing here.

My initial reaction is that, despite the “synergies” described by Hyundai, it’s certainly not immediately obvious why the company wants to own 80 percent of Boston Dynamics. I’d also like a better understanding of how they arrived at the $1.1 billion valuation. I’m not saying this because I don’t believe in what Boston Dynamics is doing or in the inherent value of the company, because I absolutely do, albeit perhaps in a slightly less tangible sense. But when you start tossing around numbers like these, a big pile of expectations inevitably comes along with them. I hope that Boston Dynamics is unique enough that the kinds of rules that normally apply to robotics companies (or companies in general) can be set aside, at least somewhat, but I also worry that what made Boston Dynamics great was the explicit funding for the kinds of radical ideas that eventually resulted in robots like Atlas and Spot.

Can Hyundai continue giving Boston Dynamics the support and freedom that they need to keep doing the kinds of things that have made them legendary? I certainly hope so. Continue reading

Posted in Human Robots

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

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

#437751 Startup and Academics Find Path to ...

Engineers have been chasing a form of AI that could drastically lower the energy required to do typical AI things like recognize words and images. This analog form of machine learning does one of the key mathematical operations of neural networks using the physics of a circuit instead of digital logic. But one of the main things limiting this approach is that deep learning’s training algorithm, back propagation, has to be done by GPUs or other separate digital systems.

Now University of Montreal AI expert Yoshua Bengio, his student Benjamin Scellier, and colleagues at startup Rain Neuromorphics have come up with way for analog AIs to train themselves. That method, called equilibrium propagation, could lead to continuously learning, low-power analog systems of a far greater computational ability than most in the industry now consider possible, according to Rain CTO Jack Kendall.

Analog circuits could save power in neural networks in part because they can efficiently perform a key calculation, called multiply and accumulate. That calculation multiplies values from inputs according to various weights, and then it sums all those values up. Two fundamental laws of electrical engineering can basically do that, too. Ohm’s Law multiplies voltage and conductance to give current, and Kirchoff’s Current Law sums the currents entering a point. By storing a neural network’s weights in resistive memory devices, such as memristors, multiply-and-accumulate can happen completely in analog, potentially reducing power consumption by orders of magnitude.

The reason analog AI systems can’t train themselves today has a lot to do with the variability of their components. Just like real neurons, those in analog neural networks don’t all behave exactly alike. To do back propagation with analog components, you must build two separate circuit pathways. One going forward to come up with an answer (called inferencing), the other going backward to do the learning so that the answer becomes more accurate. But because of the variability of analog components, the pathways don't match up.

“You end up accumulating error as you go backwards through the network,” says Bengio. To compensate, a network would need lots of power-hungry analog-to-digital and digital-to-analog circuits, defeating the point of going analog.

Equilibrium propagation allows learning and inferencing to happen on the same network, partly by adjusting the behavior of the network as a whole. “What [equilibrium propagation] allows us to do is to say how we should modify each of these devices so that the overall circuit performs the right thing,” he says. “We turn the physical computation that is happening in the analog devices directly to our advantage.”

Right now, equilibrium propagation is only working in simulation. But Rain plans to have a hardware proof-of-principle in late 2021, according to CEO and cofounder Gordon Wilson. “We are really trying to fundamentally reimagine the hardware computational substrate for artificial intelligence, find the right clues from the brain, and use those to inform the design of this,” he says. The result could be what they call end-to-end analog AI systems that capable of running sophisticated robots or even playing a role in data centers. Both of those are currently considered beyond the capabilities of analog AI, which is now focused only on adding inferencing abilities to sensors and other low-power “edge” devices, while leaving the learning to GPUs. Continue reading

Posted in Human Robots

#437707 Video Friday: This Robot Will Restock ...

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.

Tokyo startup Telexistence has recently unveiled a new robot called the Model-T, an advanced teleoperated humanoid that can use tools and grasp a wide range of objects. Japanese convenience store chain FamilyMart plans to test the Model-T to restock shelves in up to 20 stores by 2022. In the trial, a human “pilot” will operate the robot remotely, handling items like beverage bottles, rice balls, sandwiches, and bento boxes.

With Model-T and AWP, FamilyMart and TX aim to realize a completely new store operation by remoteizing and automating the merchandise restocking work, which requires a large number of labor-hours. As a result, stores can operate with less number of workers and enable them to recruit employees regardless of the store’s physical location.

[ Telexistence ]

Quadruped dance-off should be a new robotics competition at IROS or ICRA.

I dunno though, that moonwalk might keep Spot in the lead…

[ Unitree ]

Through a hybrid of simulation and real-life training, this air muscle robot is learning to play table tennis.

Table tennis requires to execute fast and precise motions. To gain precision it is necessary to explore in this high-speed regimes, however, exploration can be safety-critical at the same time. The combination of RL and muscular soft robots allows to close this gap. While robots actuated by pneumatic artificial muscles generate high forces that are required for e.g. smashing, they also offer safe execution of explosive motions due to antagonistic actuation.

To enable practical training without real balls, we introduce Hybrid Sim and Real Training (HYSR) that replays prerecorded real balls in simulation while executing actions on the real system. In this manner, RL can learn the challenging motor control of the PAM-driven robot while executing ~15000 hitting motions.

[ Max Planck Institute ]

Thanks Dieter!

Anthony Cowley wrote in to share his recent thesis work on UPSLAM, a fast and lightweight SLAM technique that records data in panoramic depth images (just PNGs) that are easy to visualize and even easier to share between robots, even on low-bandwidth networks.

[ UPenn ]

Thanks Anthony!

GITAI’s G1 is the space dedicated general-purpose robot. G1 robot will enable automation of various tasks internally & externally on space stations and for lunar base development.

[ Gitai ]

The University of Michigan has a fancy new treadmill that’s built right into the floor, which proves to be a bit much for Mini Cheetah.

But Cassie Blue won’t get stuck on no treadmill! She goes for a 0.3 mile walk across campus, which ends when a certain someone ran the gantry into Cassie Blue’s foot.

[ Michigan Robotics ]

Some serious quadruped research going on at UT Austin Human Centered Robotics Lab.

[ HCRL ]

Will Burrard-Lucas has spent lockdown upgrading his slightly indestructible BeetleCam wildlife photographing robot.

[ Will Burrard-Lucas ]

Teleoperated surgical robots are becoming commonplace in operating rooms, but many are massive (sometimes taking up an entire room) and are difficult to manipulate, especially if a complication arises and the robot needs to removed from the patient. A new collaboration between the Wyss Institute, Harvard University, and Sony Corporation has created the mini-RCM, a surgical robot the size of a tennis ball that weighs as much as a penny, and performed significantly better than manually operated tools in delicate mock-surgical procedures. Importantly, its small size means it is more comparable to the human tissues and structures on which it operates, and it can easily be removed by hand if needed.

[ Harvard Wyss ]

Yaskawa appears to be working on a robot that can scan you with a temperature gun and then jam a mask on your face?

[ Motoman ]

Maybe we should just not have people working in mines anymore, how about that?

[ Exyn ]

Many current human-robot interactive systems tend to use accurate and fast – but also costly – actuators and tracking systems to establish working prototypes that are safe to use and deploy for user studies. This paper presents an embedded framework to build a desktop space for human-robot interaction, using an open-source robot arm, as well as two RGB cameras connected to a Raspberry Pi-based controller that allow a fast yet low-cost object tracking and manipulation in 3D. We show in our evaluations that this facilitates prototyping a number of systems in which user and robot arm can commonly interact with physical objects.

[ Paper ]

IBM Research is proud to host professor Yoshua Bengio — one of the world’s leading experts in AI — in a discussion of how AI can contribute to the fight against COVID-19.

[ IBM Research ]

Ira Pastor, ideaXme life sciences ambassador interviews Professor Dr. Hiroshi Ishiguro, the Director of the Intelligent Robotics Laboratory, of the Department of Systems Innovation, in the Graduate School of Engineering Science, at Osaka University, Japan.

[ ideaXme ]

A CVPR talk from Stanford’s Chelsea Finn on “Generalization in Visuomotor Learning.”

[ Stanford ] Continue reading

Posted in Human Robots

#437701 Robotics, AI, and Cloud Computing ...

IBM must be brimming with confidence about its new automated system for performing chemical synthesis because Big Blue just had twenty or so journalists demo the complex technology live in a virtual room.

IBM even had one of the journalists choose the molecule for the demo: a molecule in a potential Covid-19 treatment. And then we watched as the system synthesized and tested the molecule and provided its analysis in a PDF document that we all saw in the other journalist’s computer. It all worked; again, that’s confidence.

The complex system is based upon technology IBM started developing three years ago that uses artificial intelligence (AI) to predict chemical reactions. In August 2018, IBM made this service available via the Cloud and dubbed it RXN for Chemistry.

Now, the company has added a new wrinkle to its Cloud-based AI: robotics. This new and improved system is no longer named simply RXN for Chemistry, but RoboRXN for Chemistry.

All of the journalists assembled for this live demo of RoboRXN could watch as the robotic system executed various steps, such as moving the reactor to a small reagent and then moving the solvent to a small reagent. The robotic system carried out the entire set of procedures—completing the synthesis and analysis of the molecule—in eight steps.

Image: IBM Research

IBM RXN helps predict chemical reaction outcomes or design retrosynthesis in seconds.

In regular practice, a user will be able to suggest a combination of molecules they would like to test. The AI will pick up the order and task a robotic system to run the reactions necessary to produce and test the molecule. Users will be provided analyses of how well their molecules performed.

Back in March of this year, Silicon Valley-based startup Strateos demonstrated something similar that they had developed. That system also employed a robotic system to help researchers working from the Cloud create new chemical compounds. However, what distinguishes IBM’s system is its incorporation of a third element: the AI.

The backbone of IBM’s AI model is a machine learning translation method that treats chemistry like language translation. It translates the language of chemistry by converting reactants and reagents to products through the use of Statistical Machine Intelligence and Learning Engine (SMILE) representation to describe chemical entities.

IBM has also leveraged an automatic data driven strategy to ensure the quality of its data. Researchers there used millions of chemical reactions to teach the AI system chemistry, but contained within that data set were errors. So, how did IBM clean this so-called noisy data to eliminate the potential for bad models?

According to Alessandra Toniato, a researcher at IBM Zurichh, the team implemented what they dubbed the “forgetting experiment.”

Toniato explains that, in this approach, they asked the AI model how sure it was that the chemical examples it was given were examples of correct chemistry. When faced with this choice, the AI identified chemistry that it had “never learnt,” “forgotten six times,” or “never forgotten.” Those that were “never forgotten” were examples that were clean, and in this way they were able to clean the data that AI had been presented.

While the AI has always been part of the RXN for Chemistry, the robotics is the newest element. The main benefit that turning over the carrying out of the reactions to a robotic system is expected to yield is to free up chemists from doing the often tedious process of having to design a synthesis from scratch, says Matteo Manica, a research staff member in Cognitive Health Care and Life Sciences at IBM Research Zürich.

“In this demo, you could see how the system is synergistic between a human and AI,” said Manica. “Combine that with the fact that we can run all these processes with a robotic system 24/7 from anywhere in the world, and you can see how it will really help up to speed up the whole process.”

There appear to be two business models that IBM is pursuing with its latest technology. One is to deploy the entire system on the premises of a company. The other is to offer licenses to private Cloud installations.

Photo: Michael Buholzer

Teodoro Laino of IBM Research Europe.

“From a business perspective you can think of having a system like we demonstrated being replicated on the premise within companies or research groups that would like to have the technology available at their disposal,” says Teodoro Laino, distinguished RSM, manager at IBM Research Europe. “On the other hand, we are also pushing at bringing the entire system to a service level.”

Just as IBM is brimming with confidence about its new technology, the company also has grand aspirations for it.

Laino adds: “Our aim is to provide chemical services across the world, a sort of Amazon of chemistry, where instead of looking for chemistry already in stock, you are asking for chemistry on demand.”

< Back to IEEE COVID-19 Resources Continue reading

Posted in Human Robots