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#438982 Quantum Computing and Reinforcement ...

Posted on March 17, 2021 by Android

Deep reinforcement learning is having a superstar moment.

Powering smarter robots. Simulating human neural networks. Trouncing physicians at medical diagnoses and crushing humanity’s best gamers at Go and Atari. While far from achieving the flexible, quick thinking that comes naturally to humans, this powerful machine learning idea seems unstoppable as a harbinger of better thinking machines.

Except there’s a massive roadblock: they take forever to run. Because the concept behind these algorithms is based on trial and error, a reinforcement learning AI “agent” only learns after being rewarded for its correct decisions. For complex problems, the time it takes an AI agent to try and fail to learn a solution can quickly become untenable.

But what if you could try multiple solutions at once?

This week, an international collaboration led by Dr. Philip Walther at the University of Vienna took the “classic” concept of reinforcement learning and gave it a quantum spin. They designed a hybrid AI that relies on both quantum and run-of-the-mill classic computing, and showed that—thanks to quantum quirkiness—it could simultaneously screen a handful of different ways to solve a problem.

The result is a reinforcement learning AI that learned over 60 percent faster than its non-quantum-enabled peers. This is one of the first tests that shows adding quantum computing can speed up the actual learning process of an AI agent, the authors explained.

Although only challenged with a “toy problem” in the study, the hybrid AI, once scaled, could impact real-world problems such as building an efficient quantum internet. The setup “could readily be integrated within future large-scale quantum communication networks,” the authors wrote.

The Bottleneck
Learning from trial and error comes intuitively to our brains.

Say you’re trying to navigate a new convoluted campground without a map. The goal is to get from the communal bathroom back to your campsite. Dead ends and confusing loops abound. We tackle the problem by deciding to turn either left or right at every branch in the road. One will get us closer to the goal; the other leads to a half hour of walking in circles. Eventually, our brain chemistry rewards correct decisions, so we gradually learn the correct route. (If you’re wondering…yeah, true story.)

Reinforcement learning AI agents operate in a similar trial-and-error way. As a problem becomes more complex, the number—and time—of each trial also skyrockets.

“Even in a moderately realistic environment, it may simply take too long to rationally respond to a given situation,” explained study author Dr. Hans Briegel at the Universität Innsbruck in Austria, who previously led efforts to speed up AI decision-making using quantum mechanics. If there’s pressure that allows “only a certain time for a response, an agent may then be unable to cope with the situation and to learn at all,” he wrote.

Many attempts have tried speeding up reinforcement learning. Giving the AI agent a short-term “memory.” Tapping into neuromorphic computing, which better resembles the brain. In 2014, Briegel and colleagues showed that a “quantum brain” of sorts can help propel an AI agent’s decision-making process after learning. But speeding up the learning process itself has eluded our best attempts.

The Hybrid AI
The new study went straight for that previously untenable jugular.

The team’s key insight was to tap into the best of both worlds—quantum and classical computing. Rather than building an entire reinforcement learning system using quantum mechanics, they turned to a hybrid approach that could prove to be more practical. Here, the AI agent uses quantum weirdness as it’s trying out new approaches—the “trial” in trial and error. The system then passes the baton to a classical computer to give the AI its reward—or not—based on its performance.

At the heart of the quantum “trial” process is a quirk called superposition. Stay with me. Our computers are powered by electrons, which can represent only two states—0 or 1. Quantum mechanics is far weirder, in that photons (particles of light) can simultaneously be both 0 and 1, with a slightly different probability of “leaning towards” one or the other.

This noncommittal oddity is part of what makes quantum computing so powerful. Take our reinforcement learning example of navigating a new campsite. In our classic world, we—and our AI—need to decide between turning left or right at an intersection. In a quantum setup, however, the AI can (in a sense) turn left and right at the same time. So when searching for the correct path back to home base, the quantum system has a leg up in that it can simultaneously explore multiple routes, making it far faster than conventional, consecutive trail and error.

“As a consequence, an agent that can explore its environment in superposition will learn significantly faster than its classical counterpart,” said Briegel.

It’s not all theory. To test out their idea, the team turned to a programmable chip called a nanophotonic processor. Think of it as a CPU-like computer chip, but it processes particles of light—photons—rather than electricity. These light-powered chips have been a long time in the making. Back in 2017, for example, a team from MIT built a fully optical neural network into an optical chip to bolster deep learning.

The chips aren’t all that exotic. Nanophotonic processors act kind of like our eyeglasses, which can carry out complex calculations that transform light that passes through them. In the glasses case, they let people see better. For a light-based computer chip, it allows computation. Rather than using electrical cables, the chips use “wave guides” to shuttle photons and perform calculations based on their interactions.

The “error” or “reward” part of the new hardware comes from a classical computer. The nanophotonic processor is coupled to a traditional computer, where the latter provides the quantum circuit with feedback—that is, whether to reward a solution or not. This setup, the team explains, allows them to more objectively judge any speed-ups in learning in real time.

In this way, a hybrid reinforcement learning agent alternates between quantum and classical computing, trying out ideas in wibbly-wobbly “multiverse” land while obtaining feedback in grounded, classic physics “normality.”

A Quantum Boost
In simulations using 10,000 AI agents and actual experimental data from 165 trials, the hybrid approach, when challenged with a more complex problem, showed a clear leg up.

The key word is “complex.” The team found that if an AI agent has a high chance of figuring out the solution anyway—as for a simple problem—then classical computing works pretty well. The quantum advantage blossoms when the task becomes more complex or difficult, allowing quantum mechanics to fully flex its superposition muscles. For these problems, the hybrid AI was 63 percent faster at learning a solution compared to traditional reinforcement learning, decreasing its learning effort from 270 guesses to 100.

Now that scientists have shown a quantum boost for reinforcement learning speeds, the race for next-generation computing is even more lit. Photonics hardware required for long-range light-based communications is rapidly shrinking, while improving signal quality. The partial-quantum setup could “aid specifically in problems where frequent search is needed, for example, network routing problems” that’s prevalent for a smooth-running internet, the authors wrote. With a quantum boost, reinforcement learning may be able to tackle far more complex problems—those in the real world—than currently possible.

“We are just at the beginning of understanding the possibilities of quantum artificial intelligence,” said lead author Walther.

Image Credit: Oleg Gamulinskiy from Pixabay Continue reading →

Posted in Human Robots

#438801 This AI Thrashes the Hardest Atari Games ...

Posted on March 4, 2021 by Android

Learning from rewards seems like the simplest thing. I make coffee, I sip coffee, I’m happy. My brain registers “brewing coffee” as an action that leads to a reward.

That’s the guiding insight behind deep reinforcement learning, a family of algorithms that famously smashed most of Atari’s gaming catalog and triumphed over humans in strategy games like Go. Here, an AI “agent” explores the game, trying out different actions and registering ones that let it win.

Except it’s not that simple. “Brewing coffee” isn’t one action; it’s a series of actions spanning several minutes, where you’re only rewarded at the very end. By just tasting the final product, how do you learn to fine-tune grind coarseness, water to coffee ratio, brewing temperature, and a gazillion other factors that result in the reward—tasty, perk-me-up coffee?

That’s the problem with “sparse rewards,” which are ironically very abundant in our messy, complex world. We don’t immediately get feedback from our actions—no video-game-style dings or points for just grinding coffee beans—yet somehow we’re able to learn and perform an entire sequence of arm and hand movements while half-asleep.

This week, researchers from UberAI and OpenAI teamed up to bestow this talent on AI.

The trick is to encourage AI agents to “return” to a previous step, one that’s promising for a winning solution. The agent then keeps a record of that state, reloads it, and branches out again to intentionally explore other solutions that may have been left behind on the first go-around. Video gamers are likely familiar with this idea: live, die, reload a saved point, try something else, repeat for a perfect run-through.

The new family of algorithms, appropriately dubbed “Go-Explore,” smashed notoriously difficult Atari games like Montezuma’s Revenge that were previously unsolvable by its AI predecessors, while trouncing human performance along the way.

It’s not just games and digital fun. In a computer simulation of a robotic arm, the team found that installing Go-Explore as its “brain” allowed it to solve a challenging series of actions when given very sparse rewards. Because the overarching idea is so simple, the authors say, it can be adapted and expanded to other real-world problems, such as drug design or language learning.

Growing Pains
How do you reward an algorithm?

Rewards are very hard to craft, the authors say. Take the problem of asking a robot to go to a fridge. A sparse reward will only give the robot “happy points” if it reaches its destination, which is similar to asking a baby, with no concept of space and danger, to crawl through a potential minefield of toys and other obstacles towards a fridge.

“In practice, reinforcement learning works very well, if you have very rich feedback, if you can tell, ‘hey, this move is good, that move is bad, this move is good, that move is bad,’” said study author Joost Huinzinga. However, in situations that offer very little feedback, “rewards can intentionally lead to a dead end. Randomly exploring the space just doesn’t cut it.”

The other extreme is providing denser rewards. In the same robot-to-fridge example, you could frequently reward the bot as it goes along its journey, essentially helping “map out” the exact recipe to success. But that’s troubling as well. Over-holding an AI’s hand could result in an extremely rigid robot that ignores new additions to its path—a pet, for example—leading to dangerous situations. It’s a deceptive AI solution that seems effective in a simple environment, but crashes in the real world.

What we need are AI agents that can tackle both problems, the team said.

Intelligent Exploration
The key is to return to the past.

For AI, motivation usually comes from “exploring new or unusual situations,” said Huizinga. It’s efficient, but comes with significant downsides. For one, the AI agent could prematurely stop going back to promising areas because it thinks it had already found a good solution. For another, it could simply forget a previous decision point because of the mechanics of how it probes the next step in a problem.

For a complex task, the end result is an AI that randomly stumbles around towards a solution while ignoring potentially better ones.

“Detaching from a place that was previously visited after collecting a reward doesn’t work in difficult games, because you might leave out important clues,” Huinzinga explained.

Go-Explore solves these problems with a simple principle: first return, then explore. In essence, the algorithm saves different approaches it previously tried and loads promising save points—once more likely to lead to victory—to explore further.

Digging a bit deeper, the AI stores screen caps from a game. It then analyzes saved points and groups images that look alike as a potential promising “save point” to return to. Rinse and repeat. The AI tries to maximize its final score in the game, and updates its save points when it achieves a new record score. Because Atari doesn’t usually allow people to revisit any random point, the team used an emulator, which is a kind of software that mimics the Atari system but with custom abilities such as saving and reloading at any time.

The trick worked like magic. When pitted against 55 Atari games in the OpenAI gym, now commonly used to benchmark reinforcement learning algorithms, Go-Explore knocked out state-of-the-art AI competitors over 85 percent of the time.

It also crushed games previously unbeatable by AI. Montezuma’s Revenge, for example, requires you to move Pedro, the blocky protagonist, through a labyrinth of underground temples while evading obstacles such as traps and enemies and gathering jewels. One bad jump could derail the path to the next level. It’s a perfect example of sparse rewards: you need a series of good actions to get to the reward—advancing onward.

Go-Explore didn’t just beat all levels of the game, a first for AI. It also scored higher than any previous record for reinforcement learning algorithms at lower levels while toppling the human world record.

Outside a gaming environment, Go-Explore was also able to boost the performance of a simulated robot arm. While it’s easy for humans to follow high-level guidance like “put the cup on this shelf in a cupboard,” robots often need explicit training—from grasping the cup to recognizing a cupboard, moving towards it while avoiding obstacles, and learning motions to not smash the cup when putting it down.

Here, similar to the real world, the digital robot arm was only rewarded when it placed the cup onto the correct shelf, out of four possible shelves. When pitted against another algorithm, Go-Explore quickly figured out the movements needed to place the cup, while its competitor struggled with even reliably picking the cup up.

Combining Forces
By itself, the “first return, then explore” idea behind Go-Explore is already powerful. The team thinks it can do even better.

One idea is to change the mechanics of save points. Rather than reloading saved states through the emulator, it’s possible to train a neural network to do the same, without needing to relaunch a saved state. It’s a potential way to make the AI even smarter, the team said, because it can “learn” to overcome one obstacle once, instead of solving the same problem again and again. The downside? It’s much more computationally intensive.

Another idea is to combine Go-Explore with an alternative form of learning, called “imitation learning.” Here, an AI observes human behavior and mimics it through a series of actions. Combined with Go-Explore, said study author Adrien Ecoffet, this could make more robust robots capable of handling all the complexity and messiness in the real world.

To the team, the implications go far beyond Go-Explore. The concept of “first return, then explore” seems to be especially powerful, suggesting “it may be a fundamental feature of learning in general.” The team said, “Harnessing these insights…may be essential…to create generally intelligent agents.”

Image Credit: Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O. Stanley, and Jeff Clune Continue reading →

Posted in Human Robots

#438006 Smellicopter Drone Uses Live Moth ...

Posted on January 28, 2021 by Android

Research into robotic sensing has, understandably I guess, been very human-centric. Most of us navigate and experience the world visually and in 3D, so robots tend to get covered with things like cameras and lidar. Touch is important to us, as is sound, so robots are getting pretty good with understanding tactile and auditory information, too. Smell, though? In most cases, smell doesn’t convey nearly as much information for us, so while it hasn’t exactly been ignored in robotics, it certainly isn’t the sensing modality of choice in most cases.

Part of the problem with smell sensing is that we just don’t have a good way of doing it, from a technical perspective. This has been a challenge for a long time, and it’s why we either bribe or trick animals like dogs, rats, vultures, and other animals to be our sensing systems for airborne chemicals. If only they’d do exactly what we wanted them to do all the time, this would be fine, but they don’t, so it’s not.

Until we get better at making chemical sensors, leveraging biology is the best we can do, and what would be ideal would be some sort of robot-animal hybrid cyborg thing. We’ve seen some attempts at remote controlled insects, but as it turns out, you can simplify things if you don’t use the entire insect, but instead just find a way to use its sensing system. Enter the Smellicopter.

There’s honestly not too much to say about the drone itself. It’s an open-source drone project called Crazyflie 2.0, with some additional off the shelf sensors for obstacle avoidance and stabilization. The interesting bits are a couple of passive fins that keep the drone pointed into the wind, and then the sensor, called an electroantennogram.

Image: UW

The drone’s sensor, called an electroantennogram, consists of a “single excised antenna” from a Manduca sexta hawkmoth and a custom signal processing circuit.

To make one of these sensors, you just, uh, “harvest” an antenna from a live hawkmoth. Obligingly, the moth antenna is hollow, meaning that you can stick electrodes up it. Whenever the olfactory neurons in the antenna (which is still technically alive even though it’s not attached to the moth anymore) encounter an odor that they’re looking for, they produce an electrical signal that the electrodes pick up. Plug the other ends of the electrodes into a voltage amplifier and filter, run it through an analog to digital converter, and you’ve got a chemical sensor that weighs just 1.5 gram and consumes only 2.7 mW of power. It’s significantly more sensitive than a conventional metal-oxide odor sensor, in a much smaller and more efficient form factor, making it ideal for drones.

To localize an odor, the Smellicopter uses a simple bioinspired approach called crosswind casting, which involves moving laterally left and right and then forward when an odor is detected. Here’s how it works:

The vehicle takes off to a height of 40 cm and then hovers for ten seconds to allow it time to orient upwind. The smellicopter starts casting left and right crosswind. When a volatile chemical is detected, the smellicopter will surge 25 cm upwind, and then resume casting. As long as the wind direction is fairly consistent, this strategy will bring the insect or robot increasingly closer to a singular source with each surge.

Since odors are airborne, they need a bit of a breeze to spread very far, and the Smellicopter won’t be able to detect them unless it’s downwind of the source. But, that’s just how odors work— even if you’re right next to the source, if the wind is blowing from you towards the source rather than the other way around, you might not catch a whiff of it.

Whenever the olfactory neurons in the antenna encounter an odor that they’re looking for, they produce an electrical signal that the electrodes pick up

There are a few other constraints to keep in mind with this sensor as well. First, rather than detecting something useful (like explosives), it’s going to detect the smells of pretty flowers, because moths like pretty flowers. Second, the antenna will literally go dead on you within a couple hours, since it only functions while its tissues are alive and metaphorically kicking. Interestingly, it may be possible to use CRISPR-based genetic modification to breed moths with antennae that do respond to useful smells, which would be a neat trick, and we asked the researchers—Melanie Anderson, a doctoral student of mechanical engineering at the University of Washington, in Seattle; Thomas Daniel, a UW professor of biology; and Sawyer Fuller, a UW assistant professor of mechanical engineering—about this, along with some other burning questions, via email.

IEEE Spectrum, asking the important questions first: So who came up with “Smellicopter”?

Melanie Anderson: Tom Daniel coined the term “Smellicopter”. Another runner up was “OdorRotor”!

In general, how much better are moths at odor localization than robots?

Melanie Anderson: Moths are excellent at odor detection and odor localization and need to be in order to find mates and food. Their antennae are much more sensitive and specialized than any portable man-made odor sensor. We can't ask the moths how exactly they search for odors so well, but being able to have the odor sensitivity of a moth on a flying platform is a big step in that direction.

Tom Daniel: Our best estimate is that they outperform robotic sensing by at least three orders of magnitude.

How does the localization behavior of the Smellicopter compare to that of a real moth?

Anderson: The cast-and-surge odor search strategy is a simplified version of what we believe the moth (and many other odor searching animals) are doing. It is a reactive strategy that relies on the knowledge that if you detect odor, you can assume that the source is somewhere up-wind of you. When you detect odor, you simply move upwind, and when you lose the odor signal you cast in a cross-wind direction until you regain the signal.

Can you elaborate on the potential for CRISPR to be able to engineer moths for the detection of specific chemicals?

Anderson: CRISPR is already currently being used to modify the odor detection pathways in moth species. It is one of our future efforts to specifically use this to make the antennae sensitive to other chemicals of interest, such as the chemical scent of explosives.

Sawyer Fuller: We think that one of the strengths of using a moth's antenna, in addition to its speed, is that it may provide a path to both high chemical specificity as well as high sensitivity. By expressing a preponderance of only one or a few chemosensors, we are anticipating that a moth antenna will give a strong response only to that chemical. There are several efforts underway in other research groups to make such specific, sensitive chemical detectors. Chemical sensing is an area where biology exceeds man-made systems in terms of efficiency, small size, and sensitivity. So that's why we think that the approach of trying to leverage biological machinery that already exists has some merit.

You mention that the antennae lifespan can be extended for a few days with ice- how feasible do you think this technology is outside of a research context?

Anderson: The antennae can be stored in tiny vials in a standard refrigerator or just with an ice pack to extend their life to about a week. Additionally, the process for attaching the antenna to the electrical circuit is a teachable skill. It is definitely feasible outside of a research context.

Considering the trajectory that sensor development is on, how long do you think that this biological sensor system will outperform conventional alternatives?

Anderson: It's hard to speak toward what will happen in the future, but currently, the moth antenna still stands out among any commercially-available portable sensors.

There have been some experiments with cybernetic insects; what are the advantages and disadvantages of your approach, as opposed to (say) putting some sort of tracking system on a live moth?

Daniel: I was part of a cyber insect team a number of years ago. The challenge of such research is that the animal has natural reactions to attempts to steer or control it.

Anderson: While moths are better at odor tracking than robots currently, the advantage of the drone platform is that we have control over it. We can tell it to constrain the search to a certain area, and return after it finishes searching.

What can you tell us about the health, happiness, and overall wellfare of the moths in your experiments?

Anderson: The moths are cold anesthetized before the antennae are removed. They are then frozen so that they can be used for teaching purposes or in other research efforts.

What are you working on next?

Daniel: The four big efforts are (1) CRISPR modification, (2) experiments aimed at improving the longevity of the antennal preparation, (3) improved measurements of antennal electrical responses to odors combined with machine learning to see if we can classify different odors, and (4) flight in outdoor environments.

Fuller: The moth's antenna sensor gives us a new ability to sense with a much shorter latency than was previously possible with similarly-sized sensors (e.g. semiconductor sensors). What exactly a robot agent should do to best take advantage of this is an open question. In particular, I think the speed may help it to zero in on plume sources in complex environments much more quickly. Think of places like indoor settings with flow down hallways that splits out at doorways, and in industrial settings festooned with pipes and equipment. We know that it is possible to search out and find odors in such scenarios, as anybody who has had to contend with an outbreak of fruit flies can attest. It is also known that these animals respond very quickly to sudden changes in odor that is present in such turbulent, patchy plumes. Since it is hard to reduce such plumes to a simple model, we think that machine learning may provide insights into how to best take advantage of the improved temporal plume information we now have available.

Tom Daniel also points out that the relative simplicity of this project (now that the UW researchers have it all figured out, that is) means that even high school students could potentially get involved in it, even if it’s on a ground robot rather than a drone. All the details are in the paper that was just published in Bioinspiration & Biomimetics. Continue reading →

Posted in Human Robots

#437884 Hyundai Buys Boston Dynamics for Nearly ...

Posted on December 11, 2020 by Android

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

#437758 Remotely Operated Robot Takes Straight ...

Posted on November 28, 2020 by Android

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

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

Photo: John Peter Whitney/Northeastern University

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

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

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

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

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

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

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

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

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

Posted in Human Robots