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#439095 DARPA Prepares for the Subterranean ...

Posted on April 9, 2021 by Android

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

#439089 Ingenuity’s Chief Pilot Explains How ...

Posted on April 9, 2021 by Android

On April 11, the Mars helicopter Ingenuity will take to the skies of Mars for the first time. It will do so fully autonomously, out of necessity—the time delay between Ingenuity’s pilots at the Jet Propulsion Laboratory and Jezero Crater on Mars makes manual or even supervisory control impossible. So the best that the folks at JPL can do is practice as much as they can in simulation, and then hope that the helicopter can handle everything on its own.

Here on Earth, simulation is a critical tool for many robotics applications, because it doesn’t rely on access to expensive hardware, is non-destructive, and can be run in parallel and at faster-than-real-time speeds to focus on solving specific problems. Once you think you’ve gotten everything figured out in simulation, you can always give it a try on the real robot and see how close you came. If it works in real life, great! And if not, well, you can tweak some stuff in the simulation and try again.

For the Mars helicopter, simulation is much more important, and much higher stakes. Testing the Mars helicopter under conditions matching what it’ll find on Mars is not physically possible on Earth. JPL has flown engineering models in Martian atmospheric conditions, and they’ve used an actuated tether to mimic Mars gravity, but there’s just no way to know what it’ll be like flying on Mars until they’ve actually flown on Mars. With that in mind, the Ingenuity team has been relying heavily on simulation, since that’s one of the best tools they have to prepare for their Martian flights. We talk with Ingenuity’s Chief Pilot, Håvard Grip, to learn how it all works.

Ingenuity Facts:
Body Size: a box of tissues

Brains: Qualcomm Snapdragon 801

Weight: 1.8 kilograms

Propulsion: Two 1.2m carbon fiber rotors

Navigation sensors: VGA camera, laser altimeter, inclinometer

Ingenuity is scheduled to make its first flight no earlier than April 11. Before liftoff, the Ingenuity team will conduct a variety of pre-flight checks, including verifying the responsiveness of the control system and spinning the blades up to full speed (2,537 rpm) without lifting off. If everything looks good, the first flight will consist of a 1 meter per second climb to 3 meters, 30 seconds of hover at 3 meters while rotating in place a bit, and then a descent to landing. If Ingenuity pulls this off, that will have made its entire mission a success. There will be more flights over the next few weeks, but all it takes is one to prove that autonomous helicopter flight on Mars is possible.

Last month, we spoke with Mars Helicopter Operations Lead Tim Canham about Ingenuity’s hardware, software, and autonomy, but we wanted to know more about how the Ingenuity team has been using simulation for everything from vehicle design to flight planning. To answer our questions, we talked with JPL’s Håvard Grip, who led the development of Ingenuity’s navigation and flight control systems. Grip also has the title of Ingenuity Chief Pilot, which is pretty awesome. He summarizes this role as “operating the flight control system to make the helicopter do what we want it to do.”

IEEE Spectrum: Can you tell me about the simulation environment that JPL uses for Ingenuity’s flight planning?

Håvard Grip: We developed a Mars helicopter simulation ourselves at JPL, based on a multi-body simulation framework that’s also developed at JPL, called DARTS/DSHELL. That's a system that has been in development at JPL for about 30 years now, and it's been used in a number of missions. And so we took that multibody simulation framework, and based on it we built our own Mars helicopter simulation, put together our own rotor model, our own aerodynamics models, and everything else that's needed in order to simulate a helicopter. We also had a lot of help from the rotorcraft experts at NASA Ames and NASA Langley.

Image: NASA/JPL

Ingenuity in JPL’s flight simulator.

Without being able to test on Mars, how much validation are you able to do of what you’re seeing in simulation?

We can do a fair amount, but it requires a lot of planning. When we made our first real prototype (with a full-size rotor that looked like what we were thinking of putting on Mars) we first spent a lot of time designing it and using simulation tools to guide that design, and when we were sufficiently confident that we were close enough, and that we understood enough about it, then we actually built the thing and designed a whole suite of tests in a vacuum chamber where where we could replicate Mars atmospheric conditions. And those tests were before we tried to fly the helicopter—they were specifically targeted at what we call system identification, which has to do with figuring out what the true properties, the true dynamics of a system are, compared to what we assumed in our models. So then we got to see how well our models did, and in the places where they needed adjustment, we could go back and do that.

The simulation work that we really started after that very first initial lift test, that’s what allowed us to unlock all of the secrets to building a helicopter that can fly on Mars.
—Håvard Grip, Ingenuity Chief Pilot

We did a lot of this kind of testing. It was a big campaign, in several stages. But there are of course things that you can't fully replicate, and you do depend on simulation to tie things together. For example, we can't truly replicate Martian gravity on Earth. We can replicate the atmosphere, but not the gravity, and so we have to do various things when we fly—either make the helicopter very light, or we have to help it a little bit by pulling up on it with a string to offload some of the weight. These things don't fully replicate what it will be like on Mars. We also can't simultaneously replicate the Mars aerodynamic environment and the physical and visual surroundings that the helicopter will be flying in. These are places where simulation tools definitely come in handy, with the ability to do full flight tests from A to B, with the helicopter taking off from the ground, running the flight software that it will be running on board, simulating the images that the navigation camera takes of the ground below as it flies, feeding that back into the flight software, and then controlling it.

To what extent can simulation really compensate for the kinds of physical testing that you can’t do on Earth?

It gives you a few different possibilities. We can take certain tests on Earth where we replicate key elements of the environment, like the atmosphere or the visual surroundings for example, and you can validate your simulation on those parameters that you can test on Earth. Then, you can combine those things in simulation, which gives you the ability to set up arbitrary scenarios and do lots and lots of tests. We can Monte Carlo things, we can do a flight a thousand times in a row, with small perturbations of various parameters and tease out what our sensitivities are to those things. And those are the kinds of things that you can't do with physical tests, both because you can't fully replicate the environment and also because of the resources that would be required to do the same thing a thousand times in a row.

Because there are limits to the physical testing we can do on Earth, there are elements where we know there's more uncertainty. On those aspects where the uncertainty is high, we tried to build in enough margin that we can handle a range of things. And simulation gives you the ability to then maybe play with those parameters, and put them at their outer limits, and test them beyond where the real parameters are going to be to make sure that you have robustness even in those extreme cases.

How do you make sure you’re not relying on simulation too much, especially since in some ways it’s your only option?

It’s about anchoring it in real data, and we’ve done a lot of that with our physical testing. I think what you’re referring to is making your simulation too perfect, and we’re careful to model the things that matter. For example, the simulated sensors that we use have realistic levels of simulated noise and bias in them, the navigation camera images have realistic levels of degradation, we have realistic disturbances from wind gusts. If you don’t properly account for those things, then you’re missing important details. So, we try to be as accurate as we can, and to capture that by overbounding in areas where we have a high degree of uncertainty.

What kinds of simulated challenges have you put the Mars helicopter through, and how do you decide how far to push those challenges?

One example is that we can simulate going over rougher terrain. We can push that, and see how far we can go and still have the helicopter behave the way that we want it to. Or we can inject levels of noise that maybe the real sensors don't see, but you want to just see how far you can push things and make sure that it's still robust.

Where we put the limits on this and what we consider to be realistic is often a challenge. We consider this on a case by case basis—if you have a sensor that you're dealing with, you try to do testing with it to characterize it and understand its performance as much as possible, and you build a level of confidence in it that allows you to find the proper balance.

When it comes to things like terrain roughness, it's a little bit of a different thing, because we're actually picking where we're flying the helicopter. We have made that choice, and we know what the terrain looks like around us, so we don’t have to wonder about that anymore.

Image: NASA/JPL-Caltech/University of Arizona

Satellite image of the Ingenuity flight area.

The way that we’re trying to approach this operationally is that we should be done with the engineering at this point. We’re not depending on going back and resimulating things, other than a few checks here and there.

Are there any examples of things you learned as part of the simulation process that resulted in changes to the hardware or mission?

You know, it’s been a journey. One of the early things that we discovered as part of modeling the helicopter was that the rotor dynamics were quite different for a helicopter on Mars, in particular with respect to how the rotor responds to the up and down bending of the blades because they’re not perfectly rigid. That motion is a very important influence on the overall flight dynamics of the helicopter, and what we discovered as we started modeling was that this motion is damped much less on Mars. Under-damped oscillatory things like that, you kind of figure might pose a control issue, and that is the case here: if you just naively design it as you might a helicopter on Earth, without taking this into account, you could have a system where the response to control inputs becomes very sluggish. So that required changes to the vehicle design from some of the very early concepts, and it led us to make a rotor that’s extremely light and rigid.

The design cycle for the Mars helicopter—it’s not like we could just build something and take it out to the back yard and try it and then come back and tweak it if it doesn’t work. It’s a much bigger effort to build something and develop a test program where you have to use a vacuum chamber to test it. So you really want to get as close as possible up front, on your first iteration, and not have to go back to the drawing board on the basic things.

So how close were you able to get on your first iteration of the helicopter design?

[This video shows] a very early demo which was done more or less just assuming that things were going to behave as they would on Earth, and that we’d be able to fly in a Martian atmosphere just spinning the rotor faster and having a very light helicopter. We were basically just trying to demonstrate that we could produce enough lift. You can see the helicopter hopping around, with someone trying to joystick it, but it turned out to be very hard to control. This was prior to doing any of the modeling that I talked about earlier. But once we started seriously focusing on the modeling and simulation, we then went on to build a prototype vehicle which had a full-size rotor that’s very close to the rotor that will be flying on Mars. One difference is that prototype had cyclic control only on the lower rotor, and later we added cyclic control on the upper rotor as well, and that decision was informed in large part by the work we did in simulation—we’d put in the kinds of disturbances that we thought we might see on Mars, and decided that we needed to have the extra control authority.

How much room do you think there is for improvement in simulation, and how could that help you in the future?

The tools that we have were definitely sufficient for doing the job that we needed to do in terms of building a helicopter that can fly on Mars. But simulation is a compute-intensive thing, and so I think there’s definitely room for higher fidelity simulation if you have the compute power to do so. For a future Mars helicopter, you could get some benefits by more closely coupling together high-fidelity aerodynamic models with larger multi-body models, and doing that in a fast way, where you can iterate quickly. There’s certainly more potential for optimizing things.

Photo: NASA/JPL-Caltech

Ingenuity preparing for flight.

Watching Ingenuity’s first flight take place will likely be much like watching the Perseverance landing—we’ll be able to follow along with the Ingenuity team while they send commands to the helicopter and receive data back, although the time delay will mean that any kind of direct control won’t be possible. If everything goes the way it’s supposed to, there will hopefully be some preliminary telemetry from Ingenuity saying so, but it sounds like we’ll likely have to wait until April 12 before we get pictures or video of the flight itself.

Because Mars doesn’t care what time it is on Earth, the flight will actually be taking place very early on April 12, with the JPL Mission Control livestream starting at 3:30 a.m. EDT (12:30 a.m. PDT). Details are here. Continue reading →

Posted in Human Robots

#439023 In ‘Klara and the Sun,’ We Glimpse ...

Posted on March 25, 2021 by Android

In a store in the center of an unnamed city, humanoid robots are displayed alongside housewares and magazines. They watch the fast-moving world outside the window, anxiously awaiting the arrival of customers who might buy them and take them home. Among them is Klara, a particularly astute robot who loves the sun and wants to learn as much as possible about humans and the world they live in.

So begins Kazuo Ishiguro’s new novel Klara and the Sun, published earlier this month. The book, told from Klara’s perspective, portrays an eerie future society in which intelligent machines and other advanced technologies have been integrated into daily life, but not everyone is happy about it.

Technological unemployment, the progress of artificial intelligence, inequality, the safety and ethics of gene editing, increasing loneliness and isolation—all of which we’re grappling with today—show up in Ishiguro’s world. It’s like he hit a fast-forward button, mirroring back to us how things might play out if we don’t approach these technologies with caution and foresight.

The wealthy genetically edit or “lift” their children to set them up for success, while the poor have to make do with the regular old brains and bodies bequeathed them by evolution. Lifted and unlifted kids generally don’t mix, and this is just one of many sinister delineations between a new breed of haves and have-nots.

There’s anger about robots’ steady infiltration into everyday life, and questions about how similar their rights should be to those of humans. “First they take the jobs. Then they take the seats at the theater?” one woman fumes.

References to “changes” and “substitutions” allude to an economy where automation has eliminated millions of jobs. While “post-employed” people squat in abandoned buildings and fringe communities arm themselves in preparation for conflict, those whose livelihoods haven’t been destroyed can afford to have live-in housekeepers and buy Artificial Friends (or AFs) for their lonely children.

“The old traditional model that we still live with now—where most of us can get some kind of paid work in exchange for our services or the goods we make—has broken down,” Ishiguro said in a podcast discussion of the novel. “We’re not talking just about the difference between rich and poor getting bigger. We’re talking about a gap appearing between people who participate in society in an obvious way and people who do not.”

He has a point; as much as techno-optimists claim that the economic changes brought by automation and AI will give us all more free time, let us work less, and devote time to our passion projects, how would that actually play out? What would millions of “post-employed” people receiving basic income actually do with their time and energy?

In the novel, we don’t get much of a glimpse of this side of the equation, but we do see how the wealthy live. After a long wait, just as the store manager seems ready to give up on selling her, Klara is chosen by a 14-year-old girl named Josie, the daughter of a woman who wears “high-rank clothes” and lives in a large, sunny home outside the city. Cheerful and kind, Josie suffers from an unspecified illness that periodically flares up and leaves her confined to her bed for days at a time.

Her life seems somewhat bleak, the need for an AF clear. In this future world, the children of the wealthy no longer go to school together, instead studying alone at home on their digital devices. “Interaction meetings” are set up for them to learn to socialize, their parents carefully eavesdropping from the next room and trying not to intervene when there’s conflict or hurt feelings.

Klara does her best to be a friend, aide, and confidante to Josie while continuing to learn about the world around her and decode the mysteries of human behavior. We surmise that she was programmed with a basic ability to understand emotions, which evolves along with her other types of intelligence. “I believe I have many feelings. The more I observe, the more feelings become available to me,” she explains to one character.

Ishiguro does an excellent job of representing Klara’s mind: a blend of pre-determined programming, observation, and continuous learning. Her narration has qualities both robotic and human; we can tell when something has been programmed in—she “Gives Privacy” to the humans around her when that’s appropriate, for example—and when she’s figured something out for herself.

But the author maintains some mystery around Klara’s inner emotional life. “Does she actually understand human emotions, or is she just observing human emotions and simulating them within herself?” he said. “I suppose the question comes back to, what are our emotions as human beings? What do they amount to?”

Klara is particularly attuned to human loneliness, since she essentially was made to help prevent it. It is, in her view, peoples’ biggest fear, and something they’ll go to great lengths to avoid, yet can never fully escape. “Perhaps all humans are lonely,” she says.

Warding off loneliness through technology isn’t a futuristic idea, it’s something we’ve been doing for a long time, with the technologies at hand growing more and more sophisticated. Products like AFs already exist. There’s XiaoIce, a chatbot that uses “sentiment analysis” to keep its 660 million users engaged, and Azuma Hikari, a character-based AI designed to “bring comfort” to users whose lives lack emotional connection with other humans.

The mere existence of these tools would be sinister if it wasn’t for their widespread adoption; when millions of people use AIs to fill a void in their lives, it raises deeper questions about our ability to connect with each other and whether technology is building it up or tearing it down.

This isn’t the only big question the novel tackles. An overarching theme is one we’ve been increasingly contemplating as computers start to acquire more complex capabilities, like the beginnings of creativity or emotional awareness: What is it that truly makes us human?

“Do you believe in the human heart?” one character asks. “I don’t mean simply the organ, obviously. I’m speaking in the poetic sense. The human heart. Do you think there is such a thing? Something that makes each of us special and individual?”

The alternative, at least in the story, is that people don’t have a unique essence, but rather we’re all a blend of traits and personalities that can be reduced to strings of code. Our understanding of the brain is still elementary, but at some level, doesn’t all human experience boil down to the firing of billions of neurons between our ears? Will we one day—in a future beyond that painted by Ishiguro, but certainly foreshadowed by it—be able to “decode” our humanity to the point that there’s nothing mysterious left about it? “A human heart is bound to be complex,” Klara says. “But it must be limited.”

Whether or not you agree, Klara and the Sun is worth the read. It’s both a marvelous, engaging story about what it means to love and be human, and a prescient warning to approach technological change with caution and nuance. We’re already living in a world where AI keeps us company, influences our behavior, and is wreaking various forms of havoc. Ishiguro’s novel is a snapshot of one of our possible futures, told through the eyes of a robot who keeps you rooting for her to the end.

Image Credit: Marion Wellmann from Pixabay Continue reading →

Posted in Human Robots

#439006 Low-Cost Drones Learn Precise Control ...

Posted on March 19, 2021 by Android

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

#438749 Folding Drone Can Drop Into Inaccessible ...

Posted on February 23, 2021 by Android

Inspecting old mines is a dangerous business. For humans, mines can be lethal: prone to rockfalls and filled with noxious gases. Robots can go where humans might suffocate, but even robots can only do so much when mines are inaccessible from the surface.

Now, researchers in the UK, led by Headlight AI, have developed a drone that could cast a light in the darkness. Named Prometheus, this drone can enter a mine through a borehole not much larger than a football, before unfurling its arms and flying around the void. Once down there, it can use its payload of scanning equipment to map mines where neither humans nor robots can presently go. This, the researchers hope, could make mine inspection quicker and easier. The team behind Prometheus published its design in November in the journal Robotics.

Mine inspection might seem like a peculiarly specific task to fret about, but old mines can collapse, causing the ground to sink and damaging nearby buildings. It’s a far-reaching threat: the geotechnical engineering firm Geoinvestigate, based in Northeast England, estimates that around 8 percent of all buildings in the UK are at risk from any of the thousands of abandoned coal mines near the country’s surface. It’s also a threat to transport, such as road and rail. Indeed, Prometheus is backed by Network Rail, which operates Britain’s railway infrastructure.

Such grave dangers mean that old mines need periodic check-ups. To enter depths that are forbidden to traditional wheeled robots—such as those featured in the DARPA SubT Challenge—inspectors today drill boreholes down into the mine and lower scanners into the darkness.

But that can be an arduous and often fruitless process. Inspecting the entirety of a mine can take multiple boreholes, and that still might not be enough to chart a complete picture. Mines are jagged, labyrinthine places, and much of the void might lie out of sight. Furthermore, many old mines aren’t well-mapped, so it’s hard to tell where best to enter them.

Prometheus can fly around some of those challenges. Inspectors can lower Prometheus, tethered to a docking apparatus, down a single borehole. Once inside the mine, the drone can undock and fly around, using LIDAR scanners—common in mine inspection today—to generate a 3D map of the unknown void. Prometheus can fly through the mine autonomously, using infrared data to plot out its own course.

Other drones exist that can fly underground, but they’re either too small to carry a relatively heavy payload of scanning equipment, or too large to easily fit down a borehole. What makes Prometheus unique is its ability to fold its arms, allowing it to squeeze down spaces its counterparts cannot.

It’s that ability to fold and enter a borehole that makes Prometheus remarkable, says Jason Gross, a professor of mechanical and aerospace engineering at West Virginia University. Gross calls Prometheus “an exciting idea,” but he does note that it has a relatively short flight window and few abilities beyond scanning.

The researchers have conducted a number of successful test flights, both in a basement and in an old mine near Shrewsbury, England. Not only was Prometheus able to map out its space, the drone was able to plot its own course in an unknown area.

The researchers’ next steps, according to Puneet Chhabra, co-founder of Headlight AI, will be to test Prometheus’s ability to unfold in an actual mine. Following that, researchers plan to conduct full-scale test flights by the end of 2021. Continue reading →

Posted in Human Robots