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#439095 DARPA Prepares for the Subterranean ...
The DARPA Subterranean Challenge Final Event is scheduled to take place at the Louisville Mega Cavern in Louisville, Kentucky, from September 21 to 23. We’ve followed SubT teams as they’ve explored their way through abandoned mines, unfinished nuclear reactors, and a variety of caves, and now everything comes together in one final course where the winner of the Systems Track will take home the $2 million first prize.
It’s a fitting reward for teams that have been solving some of the hardest problems in robotics, but winning isn’t going to be easy, and we’ll talk with SubT Program Manager Tim Chung about what we have to look forward to.
Since we haven’t talked about SubT in a little while (what with the unfortunate covid-related cancellation of the Systems Track Cave Circuit), here’s a quick refresher of where we are: the teams have made it through the Tunnel Circuit, the Urban Circuit, and a virtual version of the Cave Circuit, and some of them have been testing in caves of their own. The Final Event will include all of these environments, and the teams of robots will have 60 minutes to autonomously map the course, locating artifacts to score points. Since I’m not sure where on Earth there’s an underground location that combines tunnels and caves with urban structures, DARPA is going to have to get creative, and the location in which they’ve chosen to do that is Louisville, Kentucky.
The Louisville Mega Cavern is a former limestone mine, most of which is under the Louisville Zoo. It’s not all that deep, mostly less than 30 meters under the surface, but it’s enormous: with 370,000 square meters of rooms and passages, the cavern currently hosts (among other things) a business park, a zipline course, and mountain bike trails, because why not. While DARPA is keeping pretty quiet on the details, I’m guessing that they’ll be taking over a chunk of the cavern and filling it with features representing as many of the environmental challenges as they can.
To learn more about how the SubT Final Event is going to go, we spoke with SubT Program Manager Tim Chung. But first, we talked about Tim’s perspective on the success of the Urban Circuit, and how teams have been managing without an in-person Cave Circuit.
IEEE Spectrum: How did the SubT Urban Circuit go?
Tim Chung: On a couple fronts, Urban Circuit was really exciting. We were in this unfinished nuclear power plant—I’d be surprised if any of the competitors had prior experience in such a facility, or anything like it. I think that was illuminating both from an experiential point of view for the competitors, but also from a technology point of view, too.
One thing that I thought was really interesting was that we, DARPA, didn't need to make the venue more challenging. The real world is really that hard. There are places that were just really heinous for these robots to have to navigate through in order to look in every nook and cranny for artifacts. There were corners and doorways and small corridors and all these kind of things that really forced the teams to have to work hard, and the feedback was, why did DARPA have to make it so hard? But we didn’t, and in fact there were places that for the safety of the robots and personnel, we had to ensure the robots couldn’t go.
It sounds like some teams thought this course was on the more difficult side—do you think you tuned it to just the right amount of DARPA-hard?
Our calibration worked quite well. We were able to tease out and help refine and better understand what technologies are both useful and critical and also those technologies that might not necessarily get you the leap ahead capability. So as an example, the Urban Circuit really emphasized verticality, where you have to be able to sense, understand, and maneuver in three dimensions. Being able to capitalize on their robot technologies to address that verticality really stratified the teams, and showed how critical those capabilities are.
We saw teams that brought a lot of those capabilities do very well, and teams that brought baseline capabilities do what they could on the single floor that they were able to operate on. And so I think we got the Goldilocks solution for Urban Circuit that combined both difficulty and ambition.
Photos: Evan Ackerman/IEEE Spectrum
Two SubT Teams embedded networking equipment in balls that they could throw onto the course.
One of the things that I found interesting was that two teams independently came up with throwable network nodes. What was DARPA’s reaction to this? Is any solution a good solution, or was it more like the teams were trying to game the system?
You mean, do we want teams to game the rules in any way so as to get a competitive advantage? I don't think that's what the teams were doing. I think they were operating not only within the bounds of the rules, which permitted such a thing as throwable sensors where you could stand at the line and see how far you could chuck these things—not only was that acceptable by the rules, but anticipated. Behind the scenes, we tried to do exactly what these teams are doing and think through different approaches, so we explicitly didn't forbid such things in our rules because we thought it's important to have as wide an aperture as possible.
With these comms nodes specifically, I think they’re pretty clever. They were in some cases hacked together with a variety of different sports paraphernalia to see what would provide the best cushioning. You know, a lot of that happens in the field, and what it captured was that sometimes you just need to be up at two in the morning and thinking about things in a slightly different way, and that's when some nuggets of innovation can arise, and we see this all the time with operators in the field as well. They might only have duct tape or Styrofoam or whatever the case may be and that's when they come up with different ways to solve these problems. I think from DARPA’s perspective, and certainly from my perspective, wherever innovation can strike, we want to try to encourage and inspire those opportunities. I thought it was great, and it’s all part of the challenge.
Is there anything you can tell us about what your original plan had been for the Cave Circuit?
I can say that we’ve had the opportunity to go through a number of these caves scattered all throughout the country, and engage with caving communities—cavers clubs, speleologists that conduct research, and then of course the cave rescue community. The single biggest takeaway
is that every cave, and there are tens of thousands of them in the US alone, every cave has its own personality, and a lot of that personality is quite hidden from humans, because we can’t explore or access all of the cave. This led us to a number of different caves that were intriguing from a DARPA perspective but also inspirational for our Cave Circuit Virtual Competition.
How do you feel like the tuning was for the Virtual Cave Circuit?
The Virtual Competition, as you well know, was exciting in the sense that we could basically combine eight worlds into one competition, whereas the systems track competition really didn’t give us that opportunity. Even if we were able have held the Cave Circuit Systems Competition in person, it would have been at one site, and it would have been challenging to represent the level of diversity that we could with the Virtual Competition. So I think from that perspective, it’s clearly an advantage in terms of calibration—diversity gets you the ability to aggregate results to capture those that excel across all worlds as well as those that do well in one world or some worlds and not the others. I think the calibration was great in the sense that we were able to see the gamut of performance. Those that did well, did quite well, and those that have room to grow showed where those opportunities are for them as well.
We had to find ways to capture that diversity and that representativeness, and I think one of the fun ways we did that was with the different cave world tiles that we were able to combine in a variety of different ways. We also made use of a real world data set that we were able to take from a laser scan. Across the board, we had a really great chance to illustrate why virtual testing and simulation still plays such a dominant role in robotics technology development, and why I think it will continue to play an increasing role for developing these types of autonomy solutions.
Photo: Team CSIRO Data 61
How can systems track teams learn from their testing in whatever cave is local to them and effectively apply that to whatever cave environment is part of the final considering what the diversity of caves is?
I think that hits the nail on the head for what we as technologists are trying to discover—what are the transferable generalizable insights and how does that inform our technology development? As roboticists we want to optimize our systems to perform well at the tasks that they were designed to do, and oftentimes that means specialization because we get increased performance at the expense of being a generalist robot. I think in the case of SubT, we want to have our cake and eat it too—we want robots that perform well and reliably, but we want them to do so not just in one environment, which is how we tend to think about robot performance, but we want them to operate well in many environments, many of which have yet to be faced.
And I think that's kind of the nuance here, that we want robot systems to be generalists for the sake of being able to handle the unknown, namely the real world, but still achieve a high level of performance and perhaps they do that to their combined use of different technologies or advances in autonomy or perception approaches or novel mechanisms or mobility, but somehow they're still able, at least in aggregate, to achieve high performance.
We know these teams eagerly await any type of clue that DARPA can provide like about the SubT environments. From the environment previews for Tunnel, Urban, and even Cave, the teams were pivoting around and thinking a little bit differently. The takeaway, however, was that they didn't go to a clean sheet design—their systems were flexible enough that they could incorporate some of those specialist trends while still maintaining the notion of a generalist framework.
Looking ahead to the SubT Final, what can you tell us about the Louisville Mega Cavern?
As always, I’ll keep you in suspense until we get you there, but I can say that from the beginning of the SubT Challenge we had always envisioned teams of robots that are able to address not only the uncertainty of what's right in front of them, but also the uncertainty of what comes next. So I think the teams will be advantaged by thinking through subdomain awareness, or domain awareness if you want to generalize it, whether that means tuning multi-purpose robots, or deploying different robots, or employing your team of robots differently. Knowing which subdomain you are in is likely to be helpful, because then you can take advantage of those unique lessons learned through all those previous experiences then capitalize on that.
As far as specifics, I think the Mega Cavern offers many of the features important to what it means to be underground, while giving DARPA a pretty blank canvas to realize our vision of the SubT Challenge.
The SubT Final will be different from the earlier circuits in that there’s just one 60-minute run, rather than two. This is going to make things a lot more stressful for teams who have experienced bad robot days—why do it this way?
The preliminary round has two 30-minute runs, and those two runs are very similar to how we have done it during the circuits, of a single run per configuration per course. Teams will have the opportunity to show that their systems can face the obstacles in the final course, and it's the sum of those scores much like we did during the circuits, to help mitigate some of the concerns that you mentioned of having one robot somehow ruin their chances at a prize.
The prize round does give DARPA as well as the community a chance to focus on the top six teams from the preliminary round, and allows us to understand how they came to be at the top of the pack while emphasizing their technological contributions. The prize round will be one and done, but all of these teams we anticipate will be putting their best robot forward and will show the world why they deserve to win the SubT Challenge.
We’ve always thought that when called upon these robots need to operate in really challenging environments, and in the context of real world operations, there is no second chance. I don't think it's actually that much of a departure from our interests and insistence on bringing reliable technologies to the field, and those teams that might have something break here and there, that's all part of the challenge, of being resilient. Many teams struggled with robots that were debilitated on the course, and they still found ways to succeed and overcome that in the field, so maybe the rules emphasize that desire for showing up and working on game day which is consistent, I think, with how we've always envisioned it. This isn’t to say that these systems have to work perfectly, they just have to work in a way such that the team is resilient enough to tackle anything that they face.
It’s not too late for teams to enter for both the Virtual Track and the Systems Track to compete in the SubT Final, right?
Yes, that's absolutely right. Qualifications are still open, we are eager to welcome new teams to join in along with our existing competitors. I think any dark horse competitors coming into the Finals may be able to bring something that we haven't seen before, and that would be really exciting. I think it'll really make for an incredibly vibrant and illuminating final event.
The final event qualification deadline for the Systems Competition is April 21, and the qualification deadline for the Virtual Competition is June 29. More details here. Continue reading →
Posted in Human Robots
#439006 Low-Cost Drones Learn Precise Control ...
I’ll admit to having been somewhat skeptical about the strategy of dangling payloads on long tethers for drone delivery. I mean, I get why Wing does it— it keeps the drone and all of its spinny bits well away from untrained users while preserving the capability of making deliveries to very specific areas that may have nearby obstacles. But it also seems like you’re adding some risk as well, because once your payload is out on that long tether, it’s more or less out of your control in at least two axes. And you can forget about your drone doing anything while this is going on, because who the heck knows what’s going to happen to your payload if the drone starts moving around?
NYU roboticists, that’s who.
This research is by Guanrui Li, Alex Tunchez, and Giuseppe Loianno at the Agile Robotics and Perception Lab (ARPL) at NYU. As you can see from the video, the drone makes keeping rock-solid control over that suspended payload look easy, but it’s very much not, especially considering that everything you see is running onboard the drone itself at 500Hz— all it takes is an IMU and a downward-facing monocular camera, along with the drone’s Snapdragon processor.
To get this to work, the drone has to be thinking about two things. First, there’s state estimation, which is the behavior of the drone itself along with its payload at the end of the tether. The drone figures this out by watching how the payload moves using its camera and tracking its own movement with its IMU. Second, there’s predicting what the payload is going to do next, and how that jibes (or not) with what the drone wants to do next. The researchers developed a model predictive control (MPC) system for this, with some added perception constraints to make sure that the behavior of the drone keeps the payload in view of the camera.
At the moment, the top speed of the system is 4 m/s, but it sounds like rather than increasing the speed of a single payload-swinging drone, the next steps will be to make the overall system more complicated by somehow using multiple drones to cooperatively manage tethered payloads that are too big or heavy for one drone to handle alone.
For more on this, we spoke with Giuseppe Loianno, head of the ARPL.
IEEE Spectrum: We've seen some examples of delivery drones delivering suspended loads. How will this work improve their capabilities?
Giuseppe Loianno: For the first time, we jointly design a perception-constrained model predictive control and state estimation approaches to enable the autonomy of a quadrotor with a cable suspended payload using onboard sensing and computation. The proposed control method guarantees the visibility of the payload in the robot camera as well as the respect of the system dynamics and actuator constraints. These are critical design aspects to guarantee safety and resilience for such a complex and delicate task involving transportation of objects.
The additional challenge involves the fact that we aim to solve the aforementioned problem using a minimal sensor suite for autonomous navigation made by a single camera and IMU. This is an ambitious goal since it concurrently involves estimating the load and the vehicle states. Previous approaches leverage GPS or motion capture systems for state estimation and do not consider the perception and physical constraints when solving the problem. We are confident that our solution will contribute to making a reality the autonomous delivery process in warehouses or in dense urban areas where the GPS signal is currently absent or shadowed.
Will it make a difference to delivery systems that use an actuated cable and only leave the load suspended for the delivery itself?
This is certainly an interesting question. We believe that adding an actuated cable will introduce more disadvantages than benefits. Certainly, an actuated cable can be leveraged to compensate for cable's swinging motions in windy conditions and/or increase the delivery precision. However, the introduction of additional actuated mechanisms and components come at the price of an increased system mass and inertia. This will reduce the overall flight time and the vehicle’s agility as well as the system resilience with respect to the transportation task. Finally, active mechanisms are also more difficult to design compared to passive ones.
What's challenging about doing all of this on-vehicle?
There are several challenges to solve on-board this problem. First, it is very difficult to concurrently run perception and action on such computationally constrained platforms in real-time. Second, the first aspect becomes even more challenging if we consider as in our case a perception-based constrained receding horizon control problem that aims to guarantee the visibility of the payload during the motion, while concurrently respecting all the system physical and sensing limitations. Finally, it has been challenging to run the entire system at a high rate to fully unleash the system’s agility. We are currently able to reach rates of 500 Hz.
Can your method adapt to loads of varying shapes, sizes, and masses? What about aerodynamics or flying in wind?
Technically, our approach can easily be adapted to varying objects sizes and masses. Our previous contributions have already shown the ability to estimate online changes in the vehicle/load configuration and can potentially be used to operate the proposed system in dynamic conditions, where the load’s characteristics are unknown and/or may vary across consecutive flights. This can be useful for both package delivery or warehouse operations, where different types of objects need to be transported or manipulated.
The aerodynamics problem is a great point. Overall, our past work has investigated the aerodynamics of wind disturbances for a single robot without a load. Formulating these problems for the proposed system is challenging and is still an open research question. We have some ideas to approach this problem combining Bayesian estimation techniques with more recent machine learning approaches and we will tackle it in the near future.
What are the limitations on the performance of the system? How fast and agile can it be with a suspended payload?
The limits of the performances are established by the actuating and sensing system. Our approach intrinsically considers both physical and sensing limitations of our system. From a sensing and computation perspective, we believe to be close to the limits with speeds of up to 4 m/s. Faster speeds can potentially introduce motion blur while decreasing the load tracking precision. Moreover, faster motions will increase as well aerodynamic disturbances that we have just mentioned. In the future, modeling these phenomena and their incorporation in the proposed solution can further push the agility.
Your paper talks about extending this approach to multiple vehicles cooperatively transporting a payload, can you tell us more about that?
We are currently working on a distributed perception and control approach for cooperative transportation. We already have some very exciting results that we will share with you very soon! Overall, we can employ a team of aerial robots to cooperatively transport a payload to increase the payload capacity and endow the system with additional resilience in case of vehicles’ failures. A cooperative cable suspended payload cooperative transportation system allows as well to concurrently and independently control the load’s position and orientation. This is not possible just using rigid connections. We believe that our approach will have a strong impact in real-world settings for delivery and constructions in warehouses and GPS-denied environments such as dense urban areas. Moreover, in post disaster scenarios, a team of physically interconnected aerial robots can deliver supplies and establish communication in areas where GPS signal is intermittent or unavailable.
PCMPC: Perception-Constrained Model Predictive Control for Quadrotors with Suspended Loads using a Single Camera and IMU, by Guanrui Li, Alex Tunchez, and Giuseppe Loianno from NYU, will be presented (virtually) at ICRA 2021.
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Posted in Human Robots
#439000 Can AI Stop People From Believing Fake ...
Machine learning algorithms provide a way to detect misinformation based on writing style and how articles are shared.
On topics as varied as climate change and the safety of vaccines, you will find a wave of misinformation all over social media. Trust in conventional news sources may seem lower than ever, but researchers are working on ways to give people more insight on whether they can believe what they read. Researchers have been testing artificial intelligence (AI) tools that could help filter legitimate news. But how trustworthy is AI when it comes to stopping the spread of misinformation?
Researchers at the Rensselaer Polytechnic Institute (RPI) and the University of Tennessee collaborated to study the role of AI in helping people identify whether the news they’re reading is legitimate or not.
The research paper, “Tailoring Heuristics and Timing AI Interventions for Supporting News Veracity Assessments,” was published in Computers in Human Behavior Reports. It discussed how crowdsourcing marketplace Amazon Mechanical Turk (AMT) can be used to identify misinformation for fresh news and specific heuristics, which are rules of thumb used to process information and consider its veracity. In other words, heuristics are essentially “shortcuts for decisions,” explained Dorit Nevo, an associate professor at RPI’s Lally School of Management and a lead author for the paper.
The study found that AI would be successful in flagging false stories only if the reader did not already have an opinion on the topic, Nevo said. When study subjects were set in their beliefs, confirmation bias kept them from reassessing their views.
Nevo said the first part of the project focused on whether subjects could detect misinformation around climate change and vaccines like the one designed to prevent chicken pox. Then, beginning in April 2020, her team studied how people responded to news related to COVID-19.
“With COVID-19, there was a significant difference,” Nevo said. They found that about 72 percent of respondents could identify misinformation about the coronavirus without heuristic clues, and roughly 93 percent were able to be convinced by the researcher’s heuristics that the content was fake.
Examples of heuristic clues include text with too many capital letters or the use of strong language, Nevo said.
There were two types of heuristics mentioned in the team’s paper: objective heuristics and source heuristics. They put a statement at the top of each article the subjects read; it instructed them to read the article and indicate whether they believed its central thesis.
“We either put a statement that says the AI finds this article reliable and accurate based on the objective heuristics, or we said the AI finds the source reliable,” Nevo said. “So that's the source heuristic.”
In her research on heuristics, Nevo found that people’s thinking takes one of two paths: The first path is to read the article, think about it and decide if they believe it; the second is to consider the source and what others think about the news, and decide whether to believe it before reading it.
Image: Dorit Nevo/RPI/IEEE Spectrum
Researchers at RPI researched the role of heuristics and AI in detecting whether people thought news was credible
Another research paper, “Timing Matters When Correcting Fake News,” published in the Proceedings of the National Academy of Science by researchers at Harvard University, differed from the RPI researchers in its findings. While Nevo and her collaborators found that it’s easier to convince people that a story is fake news before reading it, the Harvard researchers, led by Nadia M. Brashier, a psychologist and neuroscientist, discovered that a fact-check can convince people of misinformation even after reading headlines. When study subjects read true or false labels after reading a headline, that resulted in a 25.3 percent reduction in “subsequent misclassification,” when compared to headlines with no tag, Brashier and her team found.
In the end, fighting misinformation will require both computing and human efforts such as policy changes, says Benjamin D. Horne, an assistant professor of Information Sciences at the University of Tennessee and one of Nevo’s co-authors. He says the RPI-Tennessee work was inspired by AI tools he designed previously. Horne was previously a research assistant at RPI, where he developed machine learning (ML) algorithms that can detect partial truths as well as decontextualized truths and out-of-date information.
“Our algorithms are trained on source-level behavior, both when using the textual content of an article and the network of other news sources that it draws news from,” Horne said. “We have found that these two types of features together are quite good at distinguishing between sources labeled as reliable or unreliable by external news source ratings.”
The machine learning algorithms analyze the writing style and the content-sharing behavior of news outlets, Horne said. Researchers trained a supervised ML algorithm called Random Forest, a classification algorithm that uses decision trees.
AI for Detecting Fake News
So, what’s the potential for AI to be successful in detecting misinformation?
“The tools we have developed, and other tools developed in this area, have fairly high accuracy in lab settings,” says Horne. “For example, our most recent technical work showed around 83% accuracy in predicting when the source of a news article is reliable or unreliable.”
Despite the effectiveness of algorithms, old-fashioned fact-checking by journalists will still be required to combat fake news. AI could filter the information for fact-checkers to verify, according to Horne.
“AI tools are great at dealing with high quantities of information at fast speeds but lack the nuanced analysis that a journalist or fact-checker can provide,” Horne said. “I see a future where the two work together.” Continue reading →
Posted in Human Robots
#437940 How Boston Dynamics Taught Its Robots to ...
A week ago, Boston Dynamics posted a video of Atlas, Spot, and Handle dancing to “Do You Love Me.” It was, according to the video description, a way “to celebrate the start of what we hope will be a happier year.” As of today the video has been viewed nearly 24 million times, and the popularity is no surprise, considering the compelling mix of technical prowess and creativity on display.
Strictly speaking, the stuff going on in the video isn’t groundbreaking, in the sense that we’re not seeing any of the robots demonstrate fundamentally new capabilities, but that shouldn’t take away from how impressive it is—you’re seeing state-of-the-art in humanoid robotics, quadrupedal robotics, and whatever-the-heck-Handle-is robotics.
What is unique about this video from Boston Dynamics is the artistic component. We know that Atlas can do some practical tasks, and we know it can do some gymnastics and some parkour, but dancing is certainly something new. To learn more about what it took to make these dancing robots happen (and it’s much more complicated than it might seem), we spoke with Aaron Saunders, Boston Dynamics’ VP of Engineering.
Saunders started at Boston Dynamics in 2003, meaning that he’s been a fundamental part of a huge number of Boston Dynamics’ robots, even the ones you may have forgotten about. Remember LittleDog, for example? A team of two designed and built that adorable little quadruped, and Saunders was one of them.
While he’s been part of the Atlas project since the beginning (and had a hand in just about everything else that Boston Dynamics works on), Saunders has spent the last few years leading the Atlas team specifically, and he was kind enough to answer our questions about their dancing robots.
IEEE Spectrum: What’s your sense of how the Internet has been reacting to the video?
Aaron Saunders: We have different expectations for the videos that we make; this one was definitely anchored in fun for us. The response on YouTube was record-setting for us: We received hundreds of emails and calls with people expressing their enthusiasm, and also sharing their ideas for what we should do next, what about this song, what about this dance move, so that was really fun. My favorite reaction was one that I got from my 94-year-old grandma, who watched the video on YouTube and then sent a message through the family asking if I’d taught the robot those sweet moves. I think this video connected with a broader audience, because it mixed the old-school music with new technology.
We haven’t seen Atlas move like this before—can you talk about how you made it happen?
We started by working with dancers and a choreographer to create an initial concept for the dance by composing and assembling a routine. One of the challenges, and probably the core challenge for Atlas in particular, was adjusting human dance moves so that they could be performed on the robot. To do that, we used simulation to rapidly iterate through movement concepts while soliciting feedback from the choreographer to reach behaviors that Atlas had the strength and speed to execute. It was very iterative—they would literally dance out what they wanted us to do, and the engineers would look at the screen and go “that would be easy” or “that would be hard” or “that scares me.” And then we’d have a discussion, try different things in simulation, and make adjustments to find a compatible set of moves that we could execute on Atlas.
Throughout the project, the time frame for creating those new dance moves got shorter and shorter as we built tools, and as an example, eventually we were able to use that toolchain to create one of Atlas’ ballet moves in just one day, the day before we filmed, and it worked. So it’s not hand-scripted or hand-coded, it’s about having a pipeline that lets you take a diverse set of motions, that you can describe through a variety of different inputs, and push them through and onto the robot.
Image: Boston Dynamics
Were there some things that were particularly difficult to translate from human dancers to Atlas? Or, things that Atlas could do better than humans?
Some of the spinning turns in the ballet parts took more iterations to get to work, because they were the furthest from leaping and running and some of the other things that we have more experience with, so they challenged both the machine and the software in new ways. We definitely learned not to underestimate how flexible and strong dancers are—when you take elite athletes and you try to do what they do but with a robot, it’s a hard problem. It’s humbling. Fundamentally, I don’t think that Atlas has the range of motion or power that these athletes do, although we continue developing our robots towards that, because we believe that in order to broadly deploy these kinds of robots commercially, and eventually in a home, we think they need to have this level of performance.
One thing that robots are really good at is doing something over and over again the exact same way. So once we dialed in what we wanted to do, the robots could just do it again and again as we played with different camera angles.
I can understand how you could use human dancers to help you put together a routine with Atlas, but how did that work with Spot, and particularly with Handle?
I think the people we worked with actually had a lot of talent for thinking about motion, and thinking about how to express themselves through motion. And our robots do motion really well—they’re dynamic, they’re exciting, they balance. So I think what we found was that the dancers connected with the way the robots moved, and then shaped that into a story, and it didn’t matter whether there were two legs or four legs. When you don’t necessarily have a template of animal motion or human behavior, you just have to think a little harder about how to go about doing something, and that’s true for more pragmatic commercial behaviors as well.
“We used simulation to rapidly iterate through movement concepts while soliciting feedback from the choreographer to reach behaviors that Atlas had the strength and speed to execute. It was very iterative—they would literally dance out what they wanted us to do, and the engineers would look at the screen and go ‘that would be easy’ or ‘that would be hard’ or ‘that scares me.’”
—Aaron Saunders, Boston Dynamics
How does the experience that you get teaching robots to dance, or to do gymnastics or parkour, inform your approach to robotics for commercial applications?
We think that the skills inherent in dance and parkour, like agility, balance, and perception, are fundamental to a wide variety of robot applications. Maybe more importantly, finding that intersection between building a new robot capability and having fun has been Boston Dynamics’ recipe for robotics—it’s a great way to advance.
One good example is how when you push limits by asking your robots to do these dynamic motions over a period of several days, you learn a lot about the robustness of your hardware. Spot, through its productization, has become incredibly robust, and required almost no maintenance—it could just dance all day long once you taught it to. And the reason it’s so robust today is because of all those lessons we learned from previous things that may have just seemed weird and fun. You’ve got to go into uncharted territory to even know what you don’t know.
Image: Boston Dynamics
It’s often hard to tell from watching videos like these how much time it took to make things work the way you wanted them to, and how representative they are of the actual capabilities of the robots. Can you talk about that?
Let me try to answer in the context of this video, but I think the same is true for all of the videos that we post. We work hard to make something, and once it works, it works. For Atlas, most of the robot control existed from our previous work, like the work that we’ve done on parkour, which sent us down a path of using model predictive controllers that account for dynamics and balance. We used those to run on the robot a set of dance steps that we’d designed offline with the dancers and choreographer. So, a lot of time, months, we spent thinking about the dance and composing the motions and iterating in simulation.
Dancing required a lot of strength and speed, so we even upgraded some of Atlas’ hardware to give it more power. Dance might be the highest power thing we’ve done to date—even though you might think parkour looks way more explosive, the amount of motion and speed that you have in dance is incredible. That also took a lot of time over the course of months; creating the capability in the machine to go along with the capability in the algorithms.
Once we had the final sequence that you see in the video, we only filmed for two days. Much of that time was spent figuring out how to move the camera through a scene with a bunch of robots in it to capture one continuous two-minute shot, and while we ran and filmed the dance routine multiple times, we could repeat it quite reliably. There was no cutting or splicing in that opening two-minute shot.
There were definitely some failures in the hardware that required maintenance, and our robots stumbled and fell down sometimes. These behaviors are not meant to be productized and to be a 100 percent reliable, but they’re definitely repeatable. We try to be honest with showing things that we can do, not a snippet of something that we did once. I think there’s an honesty required in saying that you’ve achieved something, and that’s definitely important for us.
You mentioned that Spot is now robust enough to dance all day. How about Atlas? If you kept on replacing its batteries, could it dance all day, too?
Atlas, as a machine, is still, you know… there are only a handful of them in the world, they’re complicated, and reliability was not a main focus. We would definitely break the robot from time to time. But the robustness of the hardware, in the context of what we were trying to do, was really great. And without that robustness, we wouldn’t have been able to make the video at all. I think Atlas is a little more like a helicopter, where there’s a higher ratio between the time you spend doing maintenance and the time you spend operating. Whereas with Spot, the expectation is that it’s more like a car, where you can run it for a long time before you have to touch it.
When you’re teaching Atlas to do new things, is it using any kind of machine learning? And if not, why not?
As a company, we’ve explored a lot of things, but Atlas is not using a learning controller right now. I expect that a day will come when we will. Atlas’ current dance performance uses a mixture of what we like to call reflexive control, which is a combination of reacting to forces, online and offline trajectory optimization, and model predictive control. We leverage these techniques because they’re a reliable way of unlocking really high performance stuff, and we understand how to wield these tools really well. We haven’t found the end of the road in terms of what we can do with them.
We plan on using learning to extend and build on the foundation of software and hardware that we’ve developed, but I think that we, along with the community, are still trying to figure out where the right places to apply these tools are. I think you’ll see that as part of our natural progression.
Image: Boston Dynamics
Much of Atlas’ dynamic motion comes from its lower body at the moment, but parkour makes use of upper body strength and agility as well, and we’ve seen some recent concept images showing Atlas doing vaults and pullups. Can you tell us more?
Humans and animals do amazing things using their legs, but they do even more amazing things when they use their whole bodies. I think parkour provides a fantastic framework that allows us to progress towards whole body mobility. Walking and running was just the start of that journey. We’re progressing through more complex dynamic behaviors like jumping and spinning, that’s what we’ve been working on for the last couple of years. And the next step is to explore how using arms to push and pull on the world could extend that agility.
One of the missions that I’ve given to the Atlas team is to start working on leveraging the arms as much as we leverage the legs to enhance and extend our mobility, and I’m really excited about what we’re going to be working on over the next couple of years, because it’s going to open up a lot more opportunities for us to do exciting stuff with Atlas.
What’s your perspective on hydraulic versus electric actuators for highly dynamic robots?
Across my career at Boston Dynamics, I’ve felt passionately connected to so many different types of technology, but I’ve settled into a place where I really don’t think this is an either-or conversation anymore. I think the selection of actuator technology really depends on the size of the robot that you’re building, what you want that robot to do, where you want it to go, and many other factors. Ultimately, it’s good to have both kinds of actuators in your toolbox, and I love having access to both—and we’ve used both with great success to make really impressive dynamic machines.
I think the only delineation between hydraulic and electric actuators that appears to be distinct for me is probably in scale. It’s really challenging to make tiny hydraulic things because the industry just doesn’t do a lot of that, and the reciprocal is that the industry also doesn’t tend to make massive electrical things. So, you may find that to be a natural division between these two technologies.
Besides what you’re working on at Boston Dynamics, what recent robotics research are you most excited about?
For us as a company, we really love to follow advances in sensing, computer vision, terrain perception, these are all things where the better they get, the more we can do. For me personally, one of the things I like to follow is manipulation research, and in particular manipulation research that advances our understanding of complex, friction-based interactions like sliding and pushing, or moving compliant things like ropes.
We’re seeing a shift from just pinching things, lifting them, moving them, and dropping them, to much more meaningful interactions with the environment. Research in that type of manipulation I think is going to unlock the potential for mobile manipulators, and I think it’s really going to open up the ability for robots to interact with the world in a rich way.
Is there anything else you’d like people to take away from this video?
For me personally, and I think it’s because I spend so much of my time immersed in robotics and have a deep appreciation for what a robot is and what its capabilities and limitations are, one of my strong desires is for more people to spend more time with robots. We see a lot of opinions and ideas from people looking at our videos on YouTube, and it seems to me that if more people had opportunities to think about and learn about and spend time with robots, that new level of understanding could help them imagine new ways in which robots could be useful in our daily lives. I think the possibilities are really exciting, and I just want more people to be able to take that journey. Continue reading →
Posted in Human Robots
#437912 “Boston Dynamics Will Continue to ...
Last week’s announcement that Hyundai acquired Boston Dynamics from SoftBank left us with a lot of questions. We attempted to answer many of those questions ourselves, which is typically bad practice, but sometimes it’s the only option when news like that breaks.
Fortunately, yesterday we were able to speak with Michael Patrick Perry, vice president of business development at Boston Dynamics, who candidly answered our questions about Boston Dynamics’ new relationship with Hyundai and what the near future has in store.
IEEE Spectrum: Boston Dynamics is worth 1.1 billion dollars! Can you put that valuation into context for us?
Michael Patrick Perry: Since 2018, we’ve shifted to becoming a commercial organization. And that’s included a number of things, like taking our existing technology and bringing it to market for the first time. We’ve gone from zero to 400 Spot robots deployed, building out an ecosystem of software developers, sensor providers, and integrators. With that scale of deployment and looking at the pipeline of opportunities that we have lined up over the next year, I think people have started to believe that this isn’t just a one-off novelty—that there’s actual value that Spot is able to create. Secondly, with some of our efforts in the logistics market, we’re getting really strong signals both with our Pick product and also with some early discussions around Handle’s deployment in warehouses, which we think are going to be transformational for that industry.
So, the thing that’s really exciting is that two years ago, we were talking about this vision, and people said, “Wow, that sounds really cool, let’s see how you do.” And now we have the validation from the market saying both that this is actually useful, and that we’re able to execute. And that’s where I think we’re starting to see belief in the long-term viability of Boston Dynamics, not just as a cutting-edge research shop, but also as a business.
Photo: Boston Dynamics
Boston Dynamics says it has deployed 400 Spot robots, building out an “ecosystem of software developers, sensor providers, and integrators.”
How would you describe Hyundai’s overall vision for the future of robotics, and how do they want Boston Dynamics to fit into that vision?
In the immediate term, Hyundai’s focus is to continue our existing trajectories, with Spot, Handle, and Atlas. They believe in the work that we’ve done so far, and we think that combining with a partner that understands many of the industries in which we’re targeting, whether its manufacturing, construction, or logistics, can help us improve our products. And obviously as we start thinking about producing these robots at scale, Hyundai’s expertise in manufacturing is going to be really helpful for us.
Looking down the line, both Boston Dynamics and Hyundai believe in the value of smart mobility, and they’ve made a number of plays in that space. Whether it’s urban air mobility or autonomous driving, they’ve been really thinking about connecting the digital and the physical world through moving systems, whether that’s a car, a vertical takeoff and landing multi-rotor vehicle, or a robot. We are well positioned to take on robotics side of that while also connecting to some of these other autonomous services.
Can you tell us anything about the kind of robotics that the Hyundai Motor Group has going on right now?
So they’re working on a lot of really interesting stuff—exactly how that connects, you know, it’s early days, and we don’t have anything explicitly to share. But they’ve got a smart and talented robotics team that’s working in a variety of directions that shares overlap with us. Obviously, a lot of things related to autonomous driving shares some DNA with the work that we’re doing in autonomy for Spot and Handle, so it’s pretty exciting to see.
What are you most excited about here? How do you think this deal will benefit Boston Dynamics?
I think there are a number of things. One is that they have an expertise in hardware, in a way that’s unique. They understand and appreciate the complexity of creating large complex robotic systems. So I think there’s some shared understanding of what it takes to create a great hardware product. And then also they have the resources to help us actually build those products with them together—they have manufacturing resources and things like that.
“Robotics isn’t a short term game. We’ve scaled pretty rapidly but if you start looking at what the full potential of a company like Boston Dynamics is, it’s going to take years to realize, and I think Hyundai is committed to that long-term vision”
Another thing that’s exciting is that Hyundai has some pretty visionary bets for autonomous driving and unmanned aerial systems, and all of that fits very neatly into the connected vision of robotics that we were talking about before. Robotics isn’t a short term game. We’ve scaled pretty rapidly for a robotics company in terms of the scale of robots we’ve able to deploy in the field, but if you start looking at what the full potential of a company like Boston Dynamics is, it’s going to take years to realize, and I think Hyundai is committed to that long-term vision.
And when you’ve been talking with Hyundai, what are they most excited about?
I think they’re really excited about our existing products and our technology. Looking at some of the things that Spot, Pick, and Handle are able to do now, there are applications that many of Hyundai’s customers could benefit from in terms of mobility, remote sensing, and material handling. Looking down the line, Hyundai is also very interested in smart city technology, and mobile robotics is going to be a core piece of that.
We tend to focus on Spot and Handle and Atlas in terms of platform capabilities, but can you talk a bit about some of the component-level technology that’s unique to Boston Dynamics, and that could be of interest to Hyundai?
Creating very power-dense actuator design is something that we’ve been successful at for several years, starting back with BigDog and LS3. And Handle has some hydraulic actuators and valves that are pretty unique in terms of their design and capability. Fundamentally, we have a systems engineering approach that brings together both hardware and software internally. You’ll often see different groups that specialize in something, like great mechanical or electrical engineering groups, or great controls teams, but what I think makes Boston Dynamics so special is that we’re able to put everything on the table at once to create a system that’s incredibly capable. And that’s why with something like Spot, we’re able to produce it at scale, while also making it flexible enough for all the different applications that the robot is being used for right now.
It’s hard to talk specifics right now, but there are obviously other disciplines within mechanical engineering or electrical engineering or controls for robots or autonomous systems where some of our technology could be applied.
Photo: Boston Dynamics
Boston Dynamics is in the process of commercializing Handle, iterating on its design and planning to get box-moving robots on-site with customers in the next year or two.
While Boston Dynamics was part of Google, and then SoftBank, it seems like there’s been an effort to maintain independence. Is it going to be different with Hyundai? Will there be more direct integration or collaboration?
Obviously it’s early days, but right now, we have support to continue executing against all the plans that we have. That includes all the commercialization of Spot, as well as things for Atlas, which is really going to be pushing the capability of our team to expand into new areas. That’s going to be our immediate focus, and we don’t see anything that’s going to pull us away from that core focus in the near term.
As it stands right now, Boston Dynamics will continue to be Boston Dynamics under this new ownership.
How much of what you do at Boston Dynamics right now would you characterize as fundamental robotics research, and how much is commercialization? And how do you see that changing over the next couple of years?
We have been expanding our commercial team, but we certainly keep a lot of the core capabilities of fundamental robotics research. Some of it is very visible, like the new behavior development for Atlas where we’re pushing the limits of perception and path planning. But a lot of the stuff that we’re working on is a little bit under the hood, things that are less obvious—terrain handling, intervention handling, how to make safe faults, for example. Initially when Spot started slipping on things, it would flail around trying to get back up. We’ve had to figure out the right balance between the robot struggling to stand, and when it should decide to just lock its limbs and fall over because it’s safer to do that.
I’d say the other big thrust for us is manipulation. Our gripper for Spot is coming out early next year, and that’s going to unlock a new set of capabilities for us. We have years and years of locomotion experience, but the ability to manipulate is a space that’s still relatively new to us. So we’ve been ramping up a lot of work over the last several years trying to get to an early but still valuable iteration of the technology, and we’ll continue pushing on that as we start learning what’s most useful to our customers.
“I’d say the other big thrust for us is manipulation. Our gripper for Spot is coming out early next year, and that’s going to unlock a new set of capabilities for us. We have years and years of locomotion experience, but the ability to manipulate is a space that’s still relatively new to us”
Looking back, Spot as a commercial robot has a history that goes back to robots like LS3 and BigDog, which were very ambitious projects funded by agencies like DARPA without much in the way of commercial expectations. Do you think these very early stage, very expensive, very technical projects are still things that Boston Dynamics can take on?
Yes—I would point to a lot of the things we do with Atlas as an example of that. While we don’t have immediate plans to commercialize Atlas, we can point to technologies that come out of Atlas that have enabled some of our commercial efforts over time. There’s not necessarily a clear roadmap of how every piece of Atlas research is going to feed over into a commercial product; it’s more like, this is a really hard fundamental robotics challenge, so let’s tackle it and learn things that we can then benefit from across the company.
And fundamentally, our team loves doing cool stuff with robots, and you’ll continue seeing that in the months to come.
Photo: Boston Dynamics
Spot’s arm with gripper is coming out early next year, and Boston Dynamics says that’s going to “unlock a new set of capabilities for us.”
What would it take to commercialize Atlas? And are you getting closer with Handle?
We’re in the process of commercializing Handle. We’re at a relatively early stage, but we have a plan to get the first versions for box moving on-site with customers in the next year or two. Last year, we did some on-site deployments as proof-of-concept trials, and using the feedback from that, we did a new design pass on the robot, and we’re looking at increasing our manufacturing capability. That’s all in progress.
For Atlas, it’s like the Formula 1 of robots—you’re not going to take a Formula 1 car and try to make it less capable so that you can drive it on the road. We’re still trying to see what are some applications that would necessitate an energy and computationally intensive humanoid robot as opposed to something that’s more inherently stable. Trying to understand that application space is something that we’re interested in, and then down the line, we could look at creating new morphologies to help address specific applications. In many ways, Handle is the first version of that, where we said, “Atlas is good at moving boxes but it’s very complicated and expensive, so let’s create a simpler and smaller design that can achieve some of the same things.”
The press release mentioned a mobile robot for warehouses that will be introduced next year—is that Handle?
Yes, that’s the work that we’re doing on Handle.
As we start thinking about a whole robotic solution for the warehouse, we have to look beyond a high power, low footprint, dynamic platform like Handle and also consider things that are a little less exciting on video. We need a vision system that can look at a messy stack of boxes and figure out how to pick them up, we need an interface between a robot and an order building system—things where people might question why Boston Dynamics is focusing on them because it doesn’t fit in with our crazy backflipping robots, but it’s really incumbent on us to create that full end-to-end solution.
Are you confident that under Hyundai’s ownership, Boston Dynamics will be able to continue taking the risks required to remain on the cutting edge of robotics?
I think we will continue to push the envelope of what robots are capable of, and I think in the near term, you’ll be able to see that realized in our products and the research that we’re pushing forward with. 2021 is going to be a great year for us. Continue reading →
Posted in Human Robots