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#437645 How Robots Became Essential Workers in ...

Photo: Sivaram V/Reuters

A robot, developed by Asimov Robotics to spread awareness about the coronavirus, holds a tray with face masks and sanitizer.

As the coronavirus emergency exploded into a full-blown pandemic in early 2020, forcing countless businesses to shutter, robot-making companies found themselves in an unusual situation: Many saw a surge in orders. Robots don’t need masks, can be easily disinfected, and, of course, they don’t get sick.

An army of automatons has since been deployed all over the world to help with the crisis: They are monitoring patients, sanitizing hospitals, making deliveries, and helping frontline medical workers reduce their exposure to the virus. Not all robots operate autonomously—many, in fact, require direct human supervision, and most are limited to simple, repetitive tasks. But robot makers say the experience they’ve gained during this trial-by-fire deployment will make their future machines smarter and more capable. These photos illustrate how robots are helping us fight this pandemic—and how they might be able to assist with the next one.

DROID TEAM

Photo: Clement Uwiringiyimana/Reuters

A squad of robots serves as the first line of defense against person-to-person transmission at a medical center in Kigali, Rwanda. Patients walking into the facility get their temperature checked by the machines, which are equipped with thermal cameras atop their heads. Developed by UBTech Robotics, in China, the robots also use their distinctive appearance—they resemble characters out of a Star Wars movie—to get people’s attention and remind them to wash their hands and wear masks.

Photo: Clement Uwiringiyimana/Reuters

SAY “AAH”
To speed up COVID-19 testing, a team of Danish doctors and engineers at the University of Southern Denmark and at Lifeline Robotics is developing a fully automated swab robot. It uses computer vision and machine learning to identify the perfect target spot inside the person’s throat; then a robotic arm with a long swab reaches in to collect the sample—all done with a swiftness and consistency that humans can’t match. In this photo, one of the creators, Esben Østergaard, puts his neck on the line to demonstrate that the robot is safe.

Photo: University of Southern Denmark

GERM ZAPPER
After six of its doctors became infected with the coronavirus, the Sassarese hospital in Sardinia, Italy, tightened its safety measures. It also brought in the robots. The machines, developed by UVD Robots, use lidar to navigate autonomously. Each bot carries an array of powerful short-wavelength ultraviolet-C lights that destroy the genetic material of viruses and other pathogens after a few minutes of exposure. Now there is a spike in demand for UV-disinfection robots as hospitals worldwide deploy them to sterilize intensive care units and operating theaters.

Photo: UVD Robots

RUNNING ERRANDS

In medical facilities, an ideal role for robots is taking over repetitive chores so that nurses and physicians can spend their time doing more important tasks. At Shenzhen Third People’s Hospital, in China, a robot called Aimbot drives down the hallways, enforcing face-mask and social-distancing rules and spraying disinfectant. At a hospital near Austin, Texas, a humanoid robot developed by Diligent Robotics fetches supplies and brings them to patients’ rooms. It repeats this task day and night, tirelessly, allowing the hospital staff to spend more time interacting with patients.

Photos, left: Diligent Robotics; Right: UBTech Robotics

THE DOCTOR IS IN
Nurses and doctors at Circolo Hospital in Varese, in northern Italy—the country’s hardest-hit region—use robots as their avatars, enabling them to check on their patients around the clock while minimizing exposure and conserving protective equipment. The robots, developed by Chinese firm Sanbot, are equipped with cameras and microphones and can also access patient data like blood oxygen levels. Telepresence robots, originally designed for offices, are becoming an invaluable tool for medical workers treating highly infectious diseases like COVID-19, reducing the risk that they’ll contract the pathogen they’re fighting against.

Photo: Miguel Medina/AFP/Getty Images

HELP FROM ABOVE

Photo: Zipline

Authorities in several countries attempted to use drones to enforce lockdowns and social-distancing rules, but the effectiveness of such measures remains unclear. A better use of drones was for making deliveries. In the United States, startup Zipline deployed its fixed-wing autonomous aircraft to connect two medical facilities 17 kilometers apart. For the staff at the Huntersville Medical Center, in North Carolina, masks, gowns, and gloves literally fell from the skies. The hope is that drones like Zipline’s will one day be able to deliver other kinds of critical materials, transport test samples, and distribute drugs and vaccines.

Photos: Zipline

SPECIAL DELIVERY
It’s not quite a robot takeover, but the streets and sidewalks of dozens of cities around the world have seen a proliferation of hurrying wheeled machines. Delivery robots are now in high demand as online orders continue to skyrocket.

In Hamburg, the six-wheeled robots developed by Starship Technologies navigate using cameras, GPS, and radar to bring groceries to customers.

Photo: Christian Charisius/Picture Alliance/Getty Images

In Medellín, Colombia, a startup called Rappi deployed a fleet of robots, built by Kiwibot, to deliver takeout to people in lockdown.

Photo: Joaquin Sarmiento/AFP/Getty Images

China’s JD.com, one of the country’s largest e-commerce companies, is using 20 robots to transport goods in Changsha, Hunan province; each vehicle has 22 separate compartments, which customers unlock using face authentication.

Photos: TPG/Getty Images

LIFE THROUGH ROBOTS
Robots can’t replace real human interaction, of course, but they can help people feel more connected at a time when meetings and other social activities are mostly on hold.

In Ostend, Belgium, ZoraBots brought one of its waist-high robots, equipped with cameras, microphones, and a screen, to a nursing home, allowing residents like Jozef Gouwy to virtually communicate with loved ones despite a ban on in-person visits.

Photo: Yves Herman/Reuters

In Manila, nearly 200 high school students took turns “teleporting” into a tall wheeled robot, developed by the school’s robotics club, to walk on stage during their graduation ceremony.

Photo: Ezra Acayan/Getty Images

And while Japan’s Chiba Zoological Park was temporarily closed due to the pandemic, the zoo used an autonomous robotic vehicle called RakuRo, equipped with 360-degree cameras, to offer virtual tours to children quarantined at home.

Photo: Tomohiro Ohsumi/Getty Images

SENTRY ROBOTS
Offices, stores, and medical centers are adopting robots as enforcers of a new coronavirus code.

At Fortis Hospital in Bangalore, India, a robot called Mitra uses a thermal camera to perform a preliminary screening of patients.

Photo: Manjunath Kiran/AFP/Getty Images

In Tunisia, the police use a tanklike robot to patrol the streets of its capital city, Tunis, verifying that citizens have permission to go out during curfew hours.

Photo: Khaled Nasraoui/Picture Alliance/Getty Images

And in Singapore, the Bishan-Ang Moh Kio Park unleashed a Spot robot dog, developed by Boston Dynamics, to search for social-distancing violators. Spot won’t bark at them but will rather play a recorded message reminding park-goers to keep their distance.

Photo: Roslan Rahman/AFP/Getty Images

This article appears in the October 2020 print issue as “How Robots Became Essential Workers.” Continue reading

Posted in Human Robots

#437639 Boston Dynamics’ Spot Is Helping ...

In terms of places where you absolutely want a robot to go instead of you, what remains of the utterly destroyed Chernobyl Reactor 4 should be very near the top of your list. The reactor, which suffered a catastrophic meltdown in 1986, has been covered up in almost every way possible in an effort to keep its nuclear core contained. But eventually, that nuclear material is going to have to be dealt with somehow, and in order to do that, it’s important to understand which bits of it are just really bad, and which bits are the actual worst. And this is where Spot is stepping in to help.

The big open space that Spot is walking through is right next to what’s left of Reactor 4. Within six months of the disaster, Reactor 4 was covered in a sarcophagus made of concrete and steel to try and keep all the nasty nuclear fuel from leaking out more than it already had, and it still contains “30 tons of highly contaminated dust, 16 tons of uranium and plutonium, and 200 tons of radioactive lava.” Oof. Over the next 10 years, the sarcophagus slowly deteriorated, and despite the addition of that gigantic network of steel support beams that you can see in the video, in the late 1990s it was decided to erect an enormous building over the entire mess to try and stabilize it for as long as possible.

Reactor 4 is now snugly inside the massive New Safe Confinement (NSC) structure, and the idea is that eventually, the structure will allow for the safe disassembly of what’s left of the reactor, although nobody is quite sure how to do that. This is all just to say that the area inside of the containment structure offers a lot of good opportunities for robots to take over from humans.

This particular Spot is owned by the U.K. Atomic Energy Authority, and was packed off to Russia with the assistance of the Robotics and Artificial Intelligence in Nuclear (RAIN) initiative and the National Centre for Nuclear Robotics. Dr. Dave Megson-Smith, who is a researcher at the University of Bristol, in the U.K., and part of the Hot Robotics Facility at the National Nuclear User Facility, was one of the scientists lucky enough to accompany Spot on its adventure. Megson-Smith specializes in sensor development, and he equipped Spot with a collimated radiation sensor in addition to its mapping payload. “We actually built a map of the radiation coming out of the front wall of Chernobyl power plant as we were in there with it,” Megson-Smith told us, and was able to share this picture, which shows a map of gamma photon count rate:

Image: University of Bristol

Researchers equipped Spot with a collimated radiation sensor and use one of the data readings (gamma photon count rate) to create a map of the radiation coming out of the front wall of the Chernobyl power plant.

So what’s the reason you’d want to use a very expensive legged robot to wander around what looks like a very flat and robot friendly floor? As it turns out, the floor is very dusty in there, and a priority inside the NSC is to keep dust down as much as possible, since the dust is radioactive and gets on everything and is consequently the easiest way for radioactivity to escape the NSC. “You want to minimize picking up material, so we consider the total contact surface area,” says Megson-Smith. “If you use a legged system rather than a wheeled or tracked system, you have a much smaller footprint and you disturb the environment a lot less.” While it’s nice that Spot is nimble and can climb stairs and stuff, tracked vehicles can do that as well, so in this case, the primary driving factor of choosing a robot to work inside Chernobyl is minimizing those contact points.

Right now, routine weekly measurements in contaminated spaces at Chernobyl are done by humans, which puts those humans at risk. Spot, or a robot like it, could potentially take over from those humans, as a sort of “automated safety checker”

Right now, routine weekly measurements in contaminated spaces at Chernobyl are done by humans, which puts those humans at risk. Spot, or a robot like it, could potentially take over from those humans, as a sort of “automated safety checker” able to work in medium level contaminated environments.” As far as more dangerous areas go, there’s a lot of uncertainty about what Spot is actually capable of, according to Megson-Smith. “What you think the problems are, and what the industry thinks the problems are, are subtly different things.

We were thinking that we’d have to make robots incredibly radiation proof to go into these contaminated environments, but they said, “can you just give us a system that we can send into places where humans already can go, but where we just don’t want to send humans.” Making robots incredibly radiation proof is challenging, and without extensive testing and ruggedizing, failures can be frequent, as many robots discovered at Fukushima. Indeed, Megson-Smith that in Fukushima there’s a particular section that’s known as a “robot graveyard” where robots just go to die, and they’ve had to up their standards again and again to keep the robots from failing. “So the thing they’re worried about with Spot is, what is its tolerance? What components will fail, and what can we do to harden it?” he says. “We’re approaching Boston Dynamics at the moment to see if they’ll work with us to address some of those questions.

There’s been a small amount of testing of how robots fair under harsh radiation, Megson-Smith told us, including (relatively recently) a KUKA LBR800 arm, which “stopped operating after a large radiation dose of 164.55(±1.09) Gy to its end effector, and the component causing the failure was an optical encoder.” And in case you’re wondering how much radiation that is, a 1 to 2 Gy dose to the entire body gets you acute radiation sickness and possibly death, while 8 Gy is usually just straight-up death. The goal here is not to kill robots (I mean, it sort of is), but as Megson-Smith says, “if we can work out what the weak points are in a robotic system, can we address those, can we redesign those, or at least understand when they might start to fail?” Now all he has to do is convince Boston Dynamics to send them a Spot that they can zap until it keels over.

The goal for Spot in the short term is fully autonomous radiation mapping, which seems very possible. It’ll also get tested with a wider range of sensor packages, and (happily for the robot) this will all take place safely back at home in the U.K. As far as Chernobyl is concerned, robots will likely have a substantial role to play in the near future. “Ultimately, Chernobyl has to be taken apart and decommissioned. That’s the long-term plan for the facility. To do that, you first need to understand everything, which is where we come in with our sensor systems and robotic platforms,” Megson-Smith tells us. “Since there are entire swathes of the Chernobyl nuclear plant where people can’t go in, we’d need robots like Spot to do those environmental characterizations.” Continue reading

Posted in Human Robots

#437635 Toyota Research Demonstrates ...

Over the last several years, Toyota has been putting more muscle into forward-looking robotics research than just about anyone. In addition to the Toyota Research Institute (TRI), there’s that massive 175-acre robot-powered city of the future that Toyota still plans to build next to Mount Fuji. Even Toyota itself acknowledges that it might be crazy, but that’s just how they roll—as TRI CEO Gill Pratt told me a while back, when Toyota decides to do something, they really do go all-in on it.

TRI has been focusing heavily on home robots, which is reflective of the long-term nature of what TRI is trying to do, because home robots are both the place where we’ll need robots the most at the same time as they’re the place where it’s going to be hardest to deploy them. The unpredictable nature of homes, and the fact that homes tend to have squishy fragile people in them, are robot-unfriendly characteristics, but as the population continues to age (an increasingly acute problem in Japan), homes offer an enormous amount of potential for helping us maintain our independence.

Today, Toyota is showing off some of the research that it’s been working on recently, in the form of a virtual reality presentation in lieu of an in-person press event. For journalists, TRI pre-loaded the recording onto a VR headset, which was FedEx’ed to my house. You can watch the entire 40-minute presentation in 360 video on YouTube (or in VR if you have a headset of your own), but if you don’t watch the whole thing, you should at least check out the full-on GLaDOS (with arms) that TRI thinks belongs in your home.

The presentation features an introduction from Gill Pratt, who looks entirely too comfortable embedded inside of one of TRI’s telepresence robots. The event also covers a lot of territory, but the highlight is almost certainly the new hardware that TRI demonstrates.

Soft bubble gripper

Photo: TRI

This is a “soft bubble gripper,” under development at TRI’s Cambridge, Mass., branch. These passively-compliant, air-filled grippers make it easier to grasp many different kinds of objects safely, but the nifty thing is that they’ve got cameras inside of them watching a pattern of dots on the interior of the soft membrane.

When the outside of the bubble makes contact with an object, the bubble deforms, and the deformation of the dot pattern on the inside can be tracked by the camera to determine both directions and magnitudes of forces. This is a concept that we’ve seen elsewhere before, but TRI’s implementation is a clever way of making an inherently safe end effector that can still perform all the sensing you need it to do for relatively complex manipulation tasks.

The bubble gripper was presented at ICRA this year, and you can read the technical paper here.

Ceiling-mounted home robot

Photo: TRI

I don’t know whether robots dangling from the ceiling was somehow sinister pre-Portal, but it sure as heck is for me having played through that game a couple of times, and it’s since been reinforced by AUTO from WALL-E.

The reason that we generally see robots mounted on the floor or on tables or on mobile bases is that we’re bipeds, not bats, and giving a robot access to a human-like workspace is easiest to do if you also give that robot a human-like position and orientation. And if you want to be able to reach stuff high up, you do what TRI did with their previous generation of kitchen manipulator, and just give it the ability to make itself super tall. But TRI is convinced it’s a good place to put our future home robots:

One innovative concept is a “gantry robot” that would descend from an overhead framework to perform tasks such as loading the dishwasher, wiping surfaces, and clearing clutter. By traveling on the ceiling, the robot avoids the problems of navigating household floor clutter and navigating cramped spaces. When not in use, the robot would tuck itself up out of the way. To further investigate this idea, the team has built a laboratory prototype robot that can do all the same tasks as a floor-based mobile robot but with the innovative overhead mobility system.

Another obvious problem with the gantry robot is that you have to install all kinds of stuff in your ceiling for this to work, which makes it very impractical (if not totally impossible) to introduce a system like this into a home that wasn’t built specifically for it. If, however, you do build a home with a robot like this in mind, the animation below from TRI shows how it could be extra useful. Suddenly, stairs are a non-issue. Payload is presumably also a non-issue, since loads can be transferred to the ceiling. Batteries become unnecessary, so the whole robot can be much lighter weight, which in turn makes it safer. Sensors get a fantastic view, and obstacle avoidance becomes trivial.

Robots as “time machines”

Photo: TRI

TRI’s presentation covered more than what we’ve highlighted here—our focus has been on the hardware prototypes, but TRI had more to talk about, including learning through demonstration, scaling learning through simulation, and how TRI has been working with users to figure out what research directions should be explored. It’s all available right now on YouTube, and it’s well worth 40 minutes of your time.

“What we’re really focused on is this principle idea of amplifying, rather than replacing, human beings”
—Gill Pratt, TRI

It’s only been five years since Toyota announced the $1 billion investment that established TRI, and it feels like the progress that’s been made since then has been substantial. It’s not often that vision, resources, and long-term commitment come together like this, and TRI’s emphasis on making life better for people is one of the things that helps to keep us optimistic about the future of robotics.

“What we’re really focused on is this principle idea of amplifying, rather than replacing, human beings,” Gill Pratt told us. “And what it means to amplify a person, particularly as they’re aging—what we’re really trying to do is build a time machine. This may sound fanciful, and of course we can’t build a real time machine, but maybe we can build robotic assistants to make our lives as we age seem as if we are actually using a time machine.” He explains that it doesn’t mean building robots for convenience or to do our jobs for us. “It means building technology that enables us to continue to live and to work and to relate to each other as if we were younger,” he says. “And that’s really what our main goal is.” Continue reading

Posted in Human Robots

#437630 How Toyota Research Envisions the Future ...

Yesterday, the Toyota Research Institute (TRI) showed off some of the projects that it’s been working on recently, including a ceiling-mounted robot that could one day help us with household chores. That system is just one example of how TRI envisions the future of robotics and artificial intelligence. As TRI CEO Gill Pratt told us, the company is focusing on robotics and AI technology for “amplifying, rather than replacing, human beings.” In other words, Toyota wants to develop robots not for convenience or to do our jobs for us, but rather to allow people to continue to live and work independently even as we age.

To better understand Toyota’s vision of robotics 15 to 20 years from now, it’s worth watching the 20-minute video below, which depicts various scenarios “where the application of robotic capabilities is enabling members of an aging society to live full and independent lives in spite of the challenges that getting older brings.” It’s a long video, but it helps explains TRI’s perspective on how robots will collaborate with humans in our daily lives over the next couple of decades.

Those are some interesting conceptual telepresence-controlled bipeds they’ve got running around in that video, right?

For more details, we sent TRI some questions on how it plans to go from concepts like the ones shown in the video to real products that can be deployed in human environments. Below are answers from TRI CEO Gill Pratt, who is also chief scientist for Toyota Motor Corp.; Steffi Paepcke, senior UX designer at TRI; and Max Bajracharya, VP of robotics at TRI.

IEEE Spectrum: TRI seems to have a more explicit focus on eventual commercialization than most of the robotics research that we cover. At what point TRI starts to think about things like reliability and cost?

Photo: TRI

Toyota is exploring robots capable of manipulating dishes in a sink and a dishwasher, performing experiments and simulations to make sure that the robots can handle a wide range of conditions.

Gill Pratt: It’s a really interesting question, because the normal way to think about this would be to say, well, both reliability and cost are product development tasks. But actually, we need to think about it at the earliest possible stage with research as well. The hardware that we use in the laboratory for doing experiments, we don’t worry about cost there, or not nearly as much as you’d worry about for a product. However, in terms of what research we do, we very much have to think about, is it possible (if the research is successful) for it to end up in a product that has a reasonable cost. Because if a customer can’t afford what we come up with, maybe it has some academic value but it’s not actually going to make a difference in their quality of life in the real world. So we think about cost very much from the beginning.

The same is true with reliability. Right now, we’re working very hard to make our control techniques robust to wide variations in the environment. For instance, in work that Russ Tedrake is doing with manipulating dishes in a sink and a dishwasher, both in physical testing and in simulation, we’re doing thousands and now millions of different experiments to make sure that we can handle the edge cases and it works over a very wide range of conditions.

A tremendous amount of work that we do is trying to bring robotics out of the age of doing demonstrations. There’s been a history of robotics where for some time, things have not been reliable, so we’d catch the robot succeeding just once and then show that video to the world, and people would get the mis-impression that it worked all of the time. Some researchers have been very good about showing the blooper reel too, to show that some of the time, robots don’t work.

“A tremendous amount of work that we do is trying to bring robotics out of the age of doing demonstrations. There’s been a history of robotics where for some time, things have not been reliable, so we’d catch the robot succeeding just once and then show that video to the world, and people would get the mis-impression that it worked all of the time.”
—Gill Pratt, TRI

In the spirit of sharing things that didn’t work, can you tell us a bit about some of the robots that TRI has had under development that didn’t make it into the demo yesterday because they were abandoned along the way?

Steffi Paepcke: We’re really looking at how we can connect people; it can be hard to stay in touch and see our loved ones as much as we would like to. There have been a few prototypes that we’ve worked on that had to be put on the shelf, at least for the time being. We were exploring how to use light so that people could be ambiently aware of one another across distances. I was very excited about that—the internal name was “glowing orb.” For a variety of reasons, it didn’t work out, but it was really fascinating to investigate different modalities for keeping in touch.

Another prototype we worked on—we found through our research that grocery shopping is obviously an important part of life, and for a lot of older adults, it’s not necessarily the right answer to always have groceries delivered. Getting up and getting out of the house keeps you physically active, and a lot of people prefer to continue doing it themselves. But it can be challenging, especially if you’re purchasing heavy items that you need to transport. We had a prototype that assisted with grocery shopping, but when we pivoted our focus to Japan, we found that the inside of a Japanese home really needs to stay inside, and the outside needs to stay outside, so a robot that traverses both domains is probably not the right fit for a Japanese audience, and those were some really valuable lessons for us.

Photo: TRI

Toyota recently demonstrated a gantry robot that would hang from the ceiling to perform tasks like wiping surfaces and clearing clutter.

I love that TRI is exploring things like the gantry robot both in terms of near-term research and as part of its long-term vision, but is a robot like this actually worth pursuing? Or more generally, what’s the right way to compromise between making an environment robot friendly, and asking humans to make changes to their homes?

Max Bajracharya: We think a lot about the problems that we’re trying to address in a holistic way. We don’t want to just give people a robot, and assume that they’re not going to change anything about their lifestyle. We have a lot of evidence from people who use automated vacuum cleaners that people will adapt to the tools you give them, and they’ll change their lifestyle. So we want to think about what is that trade between changing the environment, and giving people robotic assistance and tools.

We certainly think that there are ways to make the gantry system plausible. The one you saw today is obviously a prototype and does require significant infrastructure. If we’re going to retrofit a home, that isn’t going to be the way to do it. But we still feel like we’re very much in the prototype phase, where we’re trying to understand whether this is worth it to be able to bypass navigation challenges, and coming up with the pros and cons of the gantry system. We’re evaluating whether we think this is the right approach to solving the problem.

To what extent do you think humans should be either directly or indirectly in the loop with home and service robots?

Bajracharya: Our goal is to amplify people, so achieving this is going to require robots to be in a loop with people in some form. One thing we have learned is that using people in a slow loop with robots, such as teaching them or helping them when they make mistakes, gives a robot an important advantage over one that has to do everything perfectly 100 percent of the time. In unstructured human environments, robots are going to encounter corner cases, and are going to need to learn to adapt. People will likely play an important role in helping the robots learn. Continue reading

Posted in Human Robots

#437624 AI-Powered Drone Learns Extreme ...

Quadrotors are among the most agile and dynamic machines ever created. In the hands of a skilled human pilot, they can do some astonishing series of maneuvers. And while autonomous flying robots have been getting better at flying dynamically in real-world environments, they still haven’t demonstrated the same level of agility of manually piloted ones.

Now researchers from the Robotics and Perception Group at the University of Zurich and ETH Zurich, in collaboration with Intel, have developed a neural network training method that “enables an autonomous quadrotor to fly extreme acrobatic maneuvers with only onboard sensing and computation.” Extreme.

There are two notable things here: First, the quadrotor can do these extreme acrobatics outdoors without any kind of external camera or motion-tracking system to help it out (all sensing and computing is onboard). Second, all of the AI training is done in simulation, without the need for an additional simulation-to-real-world (what researchers call “sim-to-real”) transfer step. Usually, a sim-to-real transfer step means putting your quadrotor into one of those aforementioned external tracking systems, so that it doesn’t completely bork itself while trying to reconcile the differences between the simulated world and the real world, where, as the researchers wrote in a paper describing their system, “even tiny mistakes can result in catastrophic outcomes.”

To enable “zero-shot” sim-to-real transfer, the neural net training in simulation uses an expert controller that knows exactly what’s going on to teach a “student controller” that has much less perfect knowledge. That is, the simulated sensory input that the student ends up using as it learns to follow the expert has been abstracted to present the kind of imperfect, imprecise data it’s going to encounter in the real world. This can involve things like abstracting away the image part of the simulation until you’d have no way of telling the difference between abstracted simulation and abstracted reality, which is what allows the system to make that sim-to-real leap.

The simulation environment that the researchers used was Gazebo, slightly modified to better simulate quadrotor physics. Meanwhile, over in reality, a custom 1.5-kilogram quadrotor with a 4:1 thrust to weight ratio performed the physical experiments, using only a Nvidia Jetson TX2 computing board and an Intel RealSense T265, a dual fisheye camera module optimized for V-SLAM. To challenge the learning system, it was trained to perform three acrobatic maneuvers plus a combo of all of them:

Image: University of Zurich/ETH Zurich/Intel

Reference trajectories for acrobatic maneuvers. Top row, from left: Power Loop, Barrel Roll, and Matty Flip. Bottom row: Combo.

All of these maneuvers require high accelerations of up to 3 g’s and careful control, and the Matty Flip is particularly challenging, at least for humans, because the whole thing is done while the drone is flying backwards. Still, after just a few hours of training in simulation, the drone was totally real-world competent at these tricks, and could even extrapolate a little bit to perform maneuvers that it was not explicitly trained on, like doing multiple loops in a row. Where humans still have the advantage over drones is (as you might expect since we’re talking about robots) is quickly reacting to novel or unexpected situations. And when you’re doing this sort of thing outdoors, novel and unexpected situations are everywhere, from a gust of wind to a jealous bird.

For more details, we spoke with Antonio Loquercio from the University of Zurich’s Robotics and Perception Group.

IEEE Spectrum: Can you explain how the abstraction layer interfaces with the simulated sensors to enable effective sim-to-real transfer?

Antonio Loquercio: The abstraction layer applies a specific function to the raw sensor information. Exactly the same function is applied to the real and simulated sensors. The result of the function, which is “abstracted sensor measurements,” makes simulated and real observation of the same scene similar. For example, suppose we have a sequence of simulated and real images. We can very easily tell apart the real from the simulated ones given the difference in rendering. But if we apply the abstraction function of “feature tracks,” which are point correspondences in time, it becomes very difficult to tell which are the simulated and real feature tracks, since point correspondences are independent of the rendering. This applies for humans as well as for neural networks: Training policies on raw images gives low sim-to-real transfer (since images are too different between domains), while training on the abstracted images has high transfer abilities.

How useful is visual input from a camera like the Intel RealSense T265 for state estimation during such aggressive maneuvers? Would using an event camera substantially improve state estimation?

Our end-to-end controller does not require a state estimation module. It shares however some components with traditional state estimation pipelines, specifically the feature extractor and the inertial measurement unit (IMU) pre-processing and integration function. The input of the neural networks are feature tracks and integrated IMU measurements. When looking at images with low features (for example when the camera points to the sky), the neural net will mainly rely on IMU. When more features are available, the network uses to correct the accumulated drift from IMU. Overall, we noticed that for very short maneuvers IMU measurements were sufficient for the task. However, for longer ones, visual information was necessary to successfully address the IMU drift and complete the maneuver. Indeed, visual information reduces the odds of a crash by up to 30 percent in the longest maneuvers. We definitely think that event camera can improve even more the current approach since they could provide valuable visual information during high speed.

“The Matty Flip is probably one of the maneuvers that our approach can do very well … It is super challenging for humans, since they don’t see where they’re going and have problems in estimating their speed. For our approach the maneuver is no problem at all, since we can estimate forward velocities as well as backward velocities.”
—Antonio Loquercio, University of Zurich

You describe being able to train on “maneuvers that stretch the abilities of even expert human pilots.” What are some examples of acrobatics that your drones might be able to do that most human pilots would not be capable of?

The Matty Flip is probably one of the maneuvers that our approach can do very well, but human pilots find very challenging. It basically entails doing a high speed power loop by always looking backward. It is super challenging for humans, since they don’t see where they’re going and have problems in estimating their speed. For our approach the maneuver is no problem at all, since we can estimate forward velocities as well as backward velocities.

What are the limits to the performance of this system?

At the moment the main limitation is the maneuver duration. We never trained a controller that could perform maneuvers longer than 20 seconds. In the future, we plan to address this limitation and train general controllers which can fly in that agile way for significantly longer with relatively small drift. In this way, we could start being competitive against human pilots in drone racing competitions.

Can you talk about how the techniques developed here could be applied beyond drone acrobatics?

The current approach allows us to do acrobatics and agile flight in free space. We are now working to perform agile flight in cluttered environments, which requires a higher degree of understanding of the surrounding with respect to this project. Drone acrobatics is of course only an example application. We selected it because it makes a stress test of the controller performance. However, several other applications which require fast and agile flight can benefit from our approach. Examples are delivery (we want our Amazon packets always faster, don’t we?), search and rescue, or inspection. Going faster allows us to cover more space in less time, saving battery costs. Indeed, agile flight has very similar battery consumption of slow hovering for an autonomous drone.

“Deep Drone Acrobatics,” by Elia Kaufmann, Antonio Loquercio, René Ranftl, Matthias Müller, Vladlen Koltun, and Davide Scaramuzza from the Robotics and Perception Group at the University of Zurich and ETH Zurich, and Intel’s Intelligent Systems Lab, was presented at RSS 2020. Continue reading

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