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#439632 Intel Will Keep Selling RealSense Stereo ...

Posted on August 20, 2021 by Android

On Tuesday, CRN reported that Intel will be shutting down its RealSense division, which creates 3D vision systems used extensively in robotics. We confirmed the news with Intel directly on Wednesday, and Intel provided us with the following statement:
We are winding down our RealSense business and transitioning our computer vision talent, technology and products to focus on advancing innovative technologies that better support our core businesses and IDM 2.0 strategy. We will continue to meet our commitments to our current customers and are working with our employees and customers to ensure a smooth transition.However, after speaking with some of our industry sources to try and get a better sense of what happened, we learned that what's actually going on might be more nuanced. And as it turns out, it is: Intel will continue to provide RealSense stereo cameras to people who want them for now, although long term, things don't look good.
Intel's “RealSense business” encompasses a variety of different products. There's stereo depth, which includes the D415, D435, and D455 camera systems—these are what roboticists often use for 3D sensing. There's also lidar in the form of the L515 and associated software products, as well as biometric identification, which uses the F455 depth sensor, and a series of tracking and coded light cameras.
Intel has just confirmed with us that everything but the stereo cameras has been end of life'd. Here's the statement:
Intel has decided to wind down the RealSense business and is announcing the EOL of LiDAR, Facial Authentication and Tracking product lines this month. Intel will continue to provide select Stereo products to its current distribution customers. Hmm. The very careful wording here suggests some things to me, none of them good. The “RealSense business” is still being wound down, and while Intel will “continue to provide” RealSense cameras to customers, my interpretation is that they're still mostly doing what they said in their first release, which is moving their focus and talent elsewhere. So, no more development of new RealSense products, no more community engagement, and probably a minimal amount of support. If you want to buy a RealSense camera from a distributor, great, go ahead and do that, but I wouldn't look for much else. Also, “continue to provide” doesn't necessarily mean “continue to manufacture.” It could be that Intel has a big pile of cameras that they need to get rid of, and that once they're gone, that'll be the end of RealSense.
CRN managed to speak with Intel CEO Pat Gelsinger on Tuesday, and Gelsinger had this to add about the RealSense business:
“Hey, there's some good assets that we can harvest, but it doesn't fit one of those six business units that I've laid out.”
Oof.
We've asked Intel for additional detail, and we'll update this post if we hear anything more.
Sadly, many in the robotics community seemed unsurprised at the initial news about RealSense shutting down, which I guess makes sense, seeing as robotics has been burned in this way before—namely, with Microsoft's decision to discontinue the Kinect sensor (among other examples). What seemed different with RealSense was the extent to which Intel appeared to be interested in engaging with the robotics community and promoting RealSense to roboticists in a way that Microsoft never did with Kinect.
But even though it turns out that RealSense is still (technically) available, these statements over the last few days have created the feeling of a big company with other priorities, a company for whom robotics is a small enough market that it just doesn't really matter. I don't know if this is the reality over at Intel, but it's how things feel right now. My guess is that even roboticists who have been very happy with Intel will begin looking for alternatives.

The best and worst thing about RealSense could be that it's been just so darn ideal for robotics. Intel had the resources to make sensors with excellent performance and sell them for relatively cheap, and they've done exactly that. But in doing so, they've made it more difficult for alternative hardware to get a good foothold in the market, because for most people, RealSense is just the simple and affordable answer to stereo depth sensing. Maybe now, the other folks working on similar sensors (and there are a lot of companies doing very cool stuff) will be able to get a little more traction from researchers and companies who have abruptly been made aware of the need to diversify.
Even though it may not now be strictly necessary, within the next few weeks, we hope to take a look at other stereo depth sensing options for research and commercial robotics to get a better sense of what's out there. Continue reading →

Posted in Human Robots

#439447 Nothing Can Keep This Drone Down

Posted on July 29, 2021 by Android

When life knocks you down, you’ve got to get back up. Ladybugs take this advice seriously in the most literal sense. If caught on their backs, the insects are able to use their tough exterior wings, called elytra (of late made famous in the game Minecraft), to self-right themselves in just a fraction of a second.

Inspired by this approach, researchers have created self-righting drones with artificial elytra. Simulations and experiments show that the artificial elytra can not only help salvage fixed-wing drones from compromising positions, but also improve the aerodynamics of the vehicles during flight. The results are described in a study published July 9 in IEEE Robotics and Automation Letters.

Charalampos Vourtsis is a doctoral assistant at the Laboratory of Intelligent Systems, Ecole Polytechnique Federale de Lausanne in Switzerland who co-created the new design. He notes that beetles, including ladybugs, have existed for tens of millions of years. “Over that time, they have developed several survival mechanisms that we found to be a source of inspiration for applications in modern robotics,” he says.

His team was particularly intrigued by beetles’ elytra, which for ladybugs are their famous black-spotted, red exterior wing. Underneath the elytra is the hind wing, the semi-transparent appendage that’s actually used for flight.

When stuck on their backs, ladybugs use their elytra to stabilize themselves, and then thrust their legs or hind wings in order to pitch over and self-right. Vourtsis’ team designed Micro Aerial Vehicles (MAVs) that use a similar technique, but with actuators to provide the self-righting force. “Similar to the insect, the artificial elytra feature degrees of freedom that allow them to reorient the vehicle if it flips over or lands upside down,” explains Vourtsis.

The researchers created and tested artificial elytra of different lengths (11, 14 and 17 centimeters) and torques to determine the most effective combination for self-righting a fixed-wing drone. While torque had little impact on performance, the length of elytra was found to be influential.

On a flat, hard surface, the shorter elytra lengths yielded mixed results. However, the longer length was associated with a perfect success rate. The longer elytra were then tested on different inclines of 10°, 20° and 30°, and at different orientations. The drones used the elytra to self-right themselves in all scenarios, except for one position at the steepest incline.

The design was also tested on seven different terrains: pavement, course sand, fine sand, rocks, shells, wood chips and grass. The drones were able to self-right with a perfect success rate across all terrains, with the exception of grass and fine sand. Vourtsis notes that the current design was made from widely available materials and a simple scale model of the beetle’s elytra—but further optimization may help the drones self-right on these more difficult terrains.

As an added bonus, the elytra were found to add non-negligible lift during flight, which offsets their weight.

Vourtsis says his team hopes to benefit from other design features of the beetles’ elytra. “We are currently investigating elytra for protecting folding wings when the drone moves on the ground among bushes, stones, and other obstacles, just like beetles do,” explains Vourtsis. “That would enable drones to fly long distances with large, unfolded wings, and safely land and locomote in a compact format in narrow spaces.” Continue reading →

Posted in Human Robots

#439432 Nothing Can Keep This Drone Down

Posted on July 21, 2021 by Android

When life knocks you down, you’ve got to get back up. Ladybugs take this advice seriously in the most literal sense. If caught on their backs, the insects are able to use their tough exterior wings, called elytra (of late made famous in the game Minecraft), to self-right themselves in just a fraction of a second.

Inspired by this approach, researchers have created self-righting drones with artificial elytra. Simulations and experiments show that the artificial elytra can not only help salvage fixed-wing drones from compromising positions, but also improve the aerodynamics of the vehicles during flight. The results are described in a study published July 9 in IEEE Robotics and Automation Letters.

Charalampos Vourtsis is a doctoral assistant at the Laboratory of Intelligent Systems, Ecole Polytechnique Federale de Lausanne in Switzerland who co-created the new design. He notes that beetles, including ladybugs, have existed for tens of millions of years. “Over that time, they have developed several survival mechanisms that we found to be a source of inspiration for applications in modern robotics,” he says.

His team was particularly intrigued by beetles’ elytra, which for ladybugs are their famous black-spotted, red exterior wing. Underneath the elytra is the hind wing, the semi-transparent appendage that’s actually used for flight.

When stuck on their backs, ladybugs use their elytra to stabilize themselves, and then thrust their legs or hind wings in order to pitch over and self-right. Vourtsis’ team designed Micro Aerial Vehicles (MAVs) that use a similar technique, but with actuators to provide the self-righting force. “Similar to the insect, the artificial elytra feature degrees of freedom that allow them to reorient the vehicle if it flips over or lands upside down,” explains Vourtsis.

The researchers created and tested artificial elytra of different lengths (11, 14 and 17 centimeters) and torques to determine the most effective combination for self-righting a fixed-wing drone. While torque had little impact on performance, the length of elytra was found to be influential.

On a flat, hard surface, the shorter elytra lengths yielded mixed results. However, the longer length was associated with a perfect success rate. The longer elytra were then tested on different inclines of 10°, 20° and 30°, and at different orientations. The drones used the elytra to self-right themselves in all scenarios, except for one position at the steepest incline.

The design was also tested on seven different terrains: pavement, course sand, fine sand, rocks, shells, wood chips and grass. The drones were able to self-right with a perfect success rate across all terrains, with the exception of grass and fine sand. Vourtsis notes that the current design was made from widely available materials and a simple scale model of the beetle’s elytra—but further optimization may help the drones self-right on these more difficult terrains.

As an added bonus, the elytra were found to add non-negligible lift during flight, which offsets their weight.

Vourtsis says his team hopes to benefit from other design features of the beetles’ elytra. “We are currently investigating elytra for protecting folding wings when the drone moves on the ground among bushes, stones, and other obstacles, just like beetles do,” explains Vourtsis. “That would enable drones to fly long distances with large, unfolded wings, and safely land and locomote in a compact format in narrow spaces.” Continue reading →

Posted in Human Robots

#439105 This Robot Taught Itself to Walk in a ...

Posted on April 11, 2021 by Android

Recently, in a Berkeley lab, a robot called Cassie taught itself to walk, a little like a toddler might. Through trial and error, it learned to move in a simulated world. Then its handlers sent it strolling through a minefield of real-world tests to see how it’d fare.

And, as it turns out, it fared pretty damn well. With no further fine-tuning, the robot—which is basically just a pair of legs—was able to walk in all directions, squat down while walking, right itself when pushed off balance, and adjust to different kinds of surfaces.

It’s the first time a machine learning approach known as reinforcement learning has been so successfully applied in two-legged robots.

This likely isn’t the first robot video you’ve seen, nor the most polished.

For years, the internet has been enthralled by videos of robots doing far more than walking and regaining their balance. All that is table stakes these days. Boston Dynamics, the heavyweight champ of robot videos, regularly releases mind-blowing footage of robots doing parkour, back flips, and complex dance routines. At times, it can seem the world of iRobot is just around the corner.

This sense of awe is well-earned. Boston Dynamics is one of the world’s top makers of advanced robots.

But they still have to meticulously hand program and choreograph the movements of the robots in their videos. This is a powerful approach, and the Boston Dynamics team has done incredible things with it.

In real-world situations, however, robots need to be robust and resilient. They need to regularly deal with the unexpected, and no amount of choreography will do. Which is how, it’s hoped, machine learning can help.

Reinforcement learning has been most famously exploited by Alphabet’s DeepMind to train algorithms that thrash humans at some the most difficult games. Simplistically, it’s modeled on the way we learn. Touch the stove, get burned, don’t touch the damn thing again; say please, get a jelly bean, politely ask for another.

In Cassie’s case, the Berkeley team used reinforcement learning to train an algorithm to walk in a simulation. It’s not the first AI to learn to walk in this manner. But going from simulation to the real world doesn’t always translate.

Subtle differences between the two can (literally) trip up a fledgling robot as it tries out its sim skills for the first time.

To overcome this challenge, the researchers used two simulations instead of one. The first simulation, an open source training environment called MuJoCo, was where the algorithm drew upon a large library of possible movements and, through trial and error, learned to apply them. The second simulation, called Matlab SimMechanics, served as a low-stakes testing ground that more precisely matched real-world conditions.

Once the algorithm was good enough, it graduated to Cassie.

And amazingly, it didn’t need further polishing. Said another way, when it was born into the physical world—it knew how to walk just fine. In addition, it was also quite robust. The researchers write that two motors in Cassie’s knee malfunctioned during the experiment, but the robot was able to adjust and keep on trucking.

Other labs have been hard at work applying machine learning to robotics.

Last year Google used reinforcement learning to train a (simpler) four-legged robot. And OpenAI has used it with robotic arms. Boston Dynamics, too, will likely explore ways to augment their robots with machine learning. New approaches—like this one aimed at training multi-skilled robots or this one offering continuous learning beyond training—may also move the dial. It’s early yet, however, and there’s no telling when machine learning will exceed more traditional methods.

And in the meantime, Boston Dynamics bots are testing the commercial waters.

Still, robotics researchers, who were not part of the Berkeley team, think the approach is promising. Edward Johns, head of Imperial College London’s Robot Learning Lab, told MIT Technology Review, “This is one of the most successful examples I have seen.”

The Berkeley team hopes to build on that success by trying out “more dynamic and agile behaviors.” So, might a self-taught parkour-Cassie be headed our way? We’ll see.

Image Credit: University of California Berkeley Hybrid Robotics via YouTube Continue reading →

Posted in Human Robots

#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