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#435605 All of the Winners in the DARPA ...

Posted on September 12, 2019 by Android

The first competitive event in the DARPA Subterranean Challenge concluded last week—hopefully you were able to follow along on the livestream, on Twitter, or with some of the articles that we’ve posted about the event. We’ll have plenty more to say about how things went for the SubT teams, but while they take a bit of a (well earned) rest, we can take a look at the winning teams as well as who won DARPA’s special superlative awards for the competition.

First Place: Team Explorer (25/40 artifacts found)
With their rugged, reliable robots featuring giant wheels and the ability to drop communications nodes, Team Explorer was in the lead from day 1, scoring in double digits on every single run.

Second Place: Team CoSTAR (11/40 artifacts found)
Team CoSTAR had one of the more diverse lineups of robots, and they switched up which robots they decided to send into the mine as they learned more about the course.

Third Place: Team CTU-CRAS (10/40 artifacts found)
While many teams came to SubT with DARPA funding, Team CTU-CRAS was self-funded, making them eligible for a special $200,000 Tunnel Circuit prize.

DARPA also awarded a bunch of “superlative awards” after SubT:

Most Accurate Artifact: Team Explorer

To score a point, teams had to submit the location of an artifact that was correct to within 5 meters of the artifact itself. However, DARPA was tracking the artifact locations with much higher precision—for example, the “zero” point on the backpack artifact was the center of the label on the front, which DARPA tracked to the millimeter. Team Explorer managed to return the location of a backpack with an error of just 0.18 meter, which is kind of amazing.

Down to the Wire: Team CSIRO Data61

With just an hour to find as many artifacts as possible, teams had to find the right balance between sending robots off to explore and bringing them back into communication range to download artifact locations. Team CSIRO Data61 cut their last point pretty close, sliding their final point in with a mere 22 seconds to spare.

Most Distinctive Robots: Team Robotika

Team Robotika had some of the quirkiest and most recognizable robots, which DARPA recognized with the “Most Distinctive” award. Robotika told us that part of the reason for that distinctiveness was practical—having a robot that was effectively in two parts meant that they could disassemble it so that it would fit in the baggage compartment of an airplane, very important for a team based in the Czech Republic.

Most Robots Per Person: Team Coordinated Robotics

Kevin Knoedler, who won NASA’s Space Robotics Challenge entirely by himself, brought his own personal swarm of drones to SubT. With a ratio of seven robots to one human, Kevin was almost certainly the hardest working single human at the challenge.

Fan Favorite: Team NCTU

Photo: Evan Ackerman/IEEE Spectrum

The Fan Favorite award went to the team that was most popular on Twitter (with the #SubTChallenge hashtag), and it may or may not be the case that I personally tweeted enough about Team NCTU’s blimp to win them this award. It’s also true that whenever we asked anyone on other teams what their favorite robot was (besides their own, of course), the blimp was overwhelmingly popular. So either way, the award is well deserved.

DARPA shared this little behind-the-scenes clip of the blimp in action (sort of), showing what happened to the poor thing when the mine ventilation system was turned on between runs and DARPA staff had to chase it down and rescue it:

The thing to keep in mind about the results of the Tunnel Circuit is that unlike past DARPA robotics challenges (like the DRC), they don’t necessarily indicate how things are going to go for the Urban or Cave circuits because of how different things are going to be. Explorer did a great job with a team of rugged wheeled vehicles, which turned out to be ideal for navigating through mines, but they’re likely going to need to change things up substantially for the rest of the challenges, where the terrain will be much more complex.

DARPA hasn’t provided any details on the location of the Urban Circuit yet; all we know is that it’ll be sometime in February 2020. This gives teams just six months to take all the lessons that they learned from the Tunnel Circuit and update their hardware, software, and strategies. What were those lessons, and what do teams plan to do differently next year? Check back next week, and we’ll tell you.

[ DARPA SubT ] Continue reading →

Posted in Human Robots

#435601 New Double 3 Robot Makes Telepresence ...

Posted on September 12, 2019 by Android

Today, Double Robotics is announcing Double 3, the latest major upgrade to its line of consumer(ish) telepresence robots. We had a (mostly) fantastic time testing out Double 2 back in 2016. One of the things that we found out back then was that it takes a lot of practice to remotely drive the robot around. Double 3 solves this problem by leveraging the substantial advances in 3D sensing and computing that have taken place over the past few years, giving their new robot a level of intelligence that promises to make telepresence more accessible for everyone.

Double 2’s iPad has been replaced by “a fully integrated solution”—which is a fancy way of saying a dedicated 9.7-inch touchscreen and a whole bunch of other stuff. That other stuff includes an NVIDIA Jetson TX2 AI computing module, a beamforming six-microphone array, an 8-watt speaker, a pair of 13-megapixel cameras (wide angle and zoom) on a tilting mount, five ultrasonic rangefinders, and most excitingly, a pair of Intel RealSense D430 depth sensors.

It’s those new depth sensors that really make Double 3 special. The D430 modules each uses a pair of stereo cameras with a pattern projector to generate 1280 x 720 depth data with a range of between 0.2 and 10 meters away. The Double 3 robot uses all of this high quality depth data to locate obstacles, but at this point, it still doesn’t drive completely autonomously. Instead, it presents the remote operator with a slick, augmented reality view of drivable areas in the form of a grid of dots. You just click where you want the robot to go, and it will skillfully take itself there while avoiding obstacles (including dynamic obstacles) and related mishaps along the way.

This effectively offloads the most stressful part of telepresence—not running into stuff—from the remote user to the robot itself, which is the way it should be. That makes it that much easier to encourage people to utilize telepresence for the first time. The way the system is implemented through augmented reality is particularly impressive, I think. It looks like it’s intuitive enough for an inexperienced user without being restrictive, and is a clever way of mitigating even significant amounts of lag.

Otherwise, Double 3’s mobility system is exactly the same as the one featured on Double 2. In fact, that you can stick a Double 3 head on a Double 2 body and it instantly becomes a Double 3. Double Robotics is thoughtfully offering this to current Double 2 owners as a significantly more affordable upgrade option than buying a whole new robot.

For more details on all of Double 3's new features, we spoke with the co-founders of Double Robotics, Marc DeVidts and David Cann.

IEEE Spectrum: Why use this augmented reality system instead of just letting the user click on a regular camera image? Why make things more visually complicated, especially for new users?

Marc DeVidts and David Cann: One of the things that we realized about nine months ago when we got this whole thing working was that without the mixed reality for driving, it was really too magical of an experience for the customer. Even us—we had a hard time understanding whether the robot could really see obstacles and understand where the floor is and that kind of thing. So, we said “What would be the best way of communicating this information to the user?” And the right way to do it ended up drawing the graphics directly onto the scene. It’s really awesome—we have a full, real time 3D scene with the depth information drawn on top of it. We’re starting with some relatively simple graphics, and we’ll be adding more graphics in the future to help the user understand what the robot is seeing.

How robust is the vision system when it comes to obstacle detection and avoidance? Does it work with featureless surfaces, IR absorbent surfaces, in low light, in direct sunlight, etc?

We’ve looked at all of those cases, and one of the reasons that we’re going with the RealSense is the projector that helps us to see blank walls. We also found that having two sensors—one facing the floor and one facing forward—gives us a great coverage area. Having ultrasonic sensors in there as well helps us to detect anything that we can't see with the cameras. They're sort of a last safety measure, especially useful for detecting glass.

It seems like there’s a lot more that you could do with this sensing and mapping capability. What else are you working on?

We're starting with this semi-autonomous driving variant, and we're doing a private beta of full mapping. So, we’re going to do full SLAM of your environment that will be mapped by multiple robots at the same time while you're driving, and then you'll be able to zoom out to a map and click anywhere and it will drive there. That's where we're going with it, but we want to take baby steps to get there. It's the obvious next step, I think, and there are a lot more possibilities there.

Do you expect developers to be excited for this new mapping capability?

We're using a very powerful computer in the robot, a NVIDIA Jetson TX2 running Ubuntu. There's room to grow. It’s actually really exciting to be able to see, in real time, the 3D pose of the robot along with all of the depth data that gets transformed in real time into one view that gives you a full map. Having all of that data and just putting those pieces together and getting everything to work has been a huge feat in of itself.

We have an extensive API for developers to do custom implementations, either for telepresence or other kinds of robotics research. Our system isn't running ROS, but we're going to be adding ROS adapters for all of our hardware components.

Telepresence robots depend heavily on wireless connectivity, which is usually not something that telepresence robotics companies like Double have direct control over. Have you found that connectivity has been getting significantly better since you first introduced Double?

When we started in 2013, we had a lot of customers that didn’t have WiFi in their hallways, just in the conference rooms. We very rarely hear about customers having WiFi connectivity issues these days. The bigger issue we see is when people are calling into the robot from home, where they don't have proper traffic management on their home network. The robot doesn't need a ton of bandwidth, but it does need consistent, low latency bandwidth. And so, if someone else in the house is watching Netflix or something like that, it’s going to saturate your connection. But for the most part, it’s gotten a lot better over the last few years, and it’s no longer a big problem for us.

Do you think 5G will make a significant difference to telepresence robots?

We’ll see. We like the low latency possibilities and the better bandwidth, but it's all going to be a matter of what kind of reception you get. LTE can be great, if you have good reception; it’s all about where the tower is. I’m pretty sure that WiFi is going to be the primary thing for at least the next few years.

DeVidts also mentioned that an unfortunate side effect of the new depth sensors is that hanging a t-shirt on your Double to give it some personality will likely render it partially blind, so that's just something to keep in mind. To make up for this, you can switch around the colorful trim surrounding the screen, which is nowhere near as fun.

When the Double 3 is ready for shipping in late September, US $2,000 will get you the new head with all the sensors and stuff, which seamlessly integrates with your Double 2 base. Buying Double 3 straight up (with the included charging dock) will run you $4,ooo. This is by no means an inexpensive robot, and my impression is that it’s not really designed for individual consumers. But for commercial, corporate, healthcare, or education applications, $4k for a robot as capable as the Double 3 is really quite a good deal—especially considering the kinds of use cases for which it’s ideal.

[ Double Robotics ] Continue reading →

Posted in Human Robots

#435597 Water Jet Powered Drone Takes Off With ...

Posted on September 12, 2019 by Android

At ICRA 2015, the Aerial Robotics Lab at the Imperial College London presented a concept for a multimodal flying swimming robot called AquaMAV. The really difficult thing about a flying and swimming robot isn’t so much the transition from the first to the second, since you can manage that even if your robot is completely dead (thanks to gravity), but rather the other way: going from water to air, ideally in a stable and repetitive way. The AquaMAV concept solved this by basically just applying as much concentrated power as possible to the problem, using a jet thruster to hurl the robot out of the water with quite a bit of velocity to spare.

In a paper appearing in Science Robotics this week, the roboticists behind AquaMAV present a fully operational robot that uses a solid-fuel powered chemical reaction to generate an explosion that powers the robot into the air.

The 2015 version of AquaMAV, which was mostly just some very vintage-looking computer renderings and a little bit of hardware, used a small cylinder of CO2 to power its water jet thruster. This worked pretty well, but the mass and complexity of the storage and release mechanism for the compressed gas wasn’t all that practical for a flying robot designed for long-term autonomy. It’s a familiar challenge, especially for pneumatically powered soft robots—how do you efficiently generate gas on-demand, especially if you need a lot of pressure all at once?

An explosion propels the drone out of the water
There’s one obvious way of generating large amounts of pressurized gas all at once, and that’s explosions. We’ve seen robots use explosive thrust for mobility before, at a variety of scales, and it’s very effective as long as you can both properly harness the explosion and generate the fuel with a minimum of fuss, and this latest version of AquaMAV manages to do both:

The water jet coming out the back of this robot aircraft is being propelled by a gas explosion. The gas comes from the reaction between a little bit of calcium carbide powder stored inside the robot, and water. Water is mixed with the powder one drop at a time, producing acetylene gas, which gets piped into a combustion chamber along with air and water. When ignited, the acetylene air mixture explodes, forcing the water out of the combustion chamber and providing up to 51 N of thrust, which is enough to launch the 160-gram robot 26 meters up and over the water at 11 m/s. It takes just 50 mg of calcium carbide (mixed with 3 drops of water) to generate enough acetylene for each explosion, and both air and water are of course readily available. With 0.2 g of calcium carbide powder on board, the robot has enough fuel for multiple jumps, and the jump is powerful enough that the robot can get airborne even under fairly aggressive sea conditions.

Image: Science Robotics

The robot can transition from a floating state to an airborne jetting phase and back to floating (A). A 3D model render of the underside of the robot (B) shows the electronics capsule. The capsule contains the fuel tank (C), where calcium carbide reacts with air and water to propel the vehicle.

Next step: getting the robot to fly autonomously
Providing adequate thrust is just one problem that needs to be solved when attempting to conquer the water-air transition with a fixed-wing robot. The overall design of the robot itself is a challenge as well, because the optimal design and balance for the robot is quite different in each phase of operation, as the paper describes:

For the vehicle to fly in a stable manner during the jetting phase, the center of mass must be a significant distance in front of the center of pressure of the vehicle. However, to maintain a stable floating position on the water surface and the desired angle during jetting, the center of mass must be located behind the center of buoyancy. For the gliding phase, a fine balance between the center of mass and the center of pressure must be struck to achieve static longitudinal flight stability passively. During gliding, the center of mass should be slightly forward from the wing’s center of pressure.

The current version is mostly optimized for the jetting phase of flight, and doesn’t have any active flight control surfaces yet, but the researchers are optimistic that if they added some they’d have no problem getting the robot to fly autonomously. It’s just a glider at the moment, but a low-power propeller is the obvious step after that, and to get really fancy, a switchable gearbox could enable efficient movement on water as well as in the air. Long-term, the idea is that robots like these would be useful for tasks like autonomous water sampling over large areas, but I’d personally be satisfied with a remote controlled version that I could take to the beach.

“Consecutive aquatic jump-gliding with water-reactive fuel,” by R. Zufferey, A. Ortega Ancel, A. Farinha, R. Siddall, S. F. Armanini, M. Nasr, R. V. Brahmal, G. Kennedy, and M. Kovac from Imperial College in London, is published in the current issue of Science Robotics. Continue reading →

Posted in Human Robots

#435593 AI at the Speed of Light

Posted on September 11, 2019 by Android

Neural networks shine for solving tough problems such as facial and voice recognition, but conventional electronic versions are limited in speed and hungry for power. In theory, optics could beat digital electronic computers in the matrix calculations used in neural networks. However, optics had been limited by their inability to do some complex calculations that had required electronics. Now new experiments show that all-optical neural networks can tackle those problems.

The key attraction of neural networks is their massive interconnections among processors, comparable to the complex interconnections among neurons in the brain. This lets them perform many operations simultaneously, like the human brain does when looking at faces or listening to speech, making them more efficient for facial and voice recognition than traditional electronic computers that execute one instruction at a time.

Today's electronic neural networks have reached eight million neurons, but their future use in artificial intelligence may be limited by their high power usage and limited parallelism in connections. Optical connections through lenses are inherently parallel. The lens in your eye simultaneously focuses light from across your field of view onto the retina in the back of your eye, where an array of light-detecting nerve cells detects the light. Each cell then relays the signal it receives to neurons in the brain that process the visual signals to show us an image.

Glass lenses process optical signals by focusing light, which performs a complex mathematical operation called a Fourier transform that preserves the information in the original scene but rearranges is completely. One use of Fourier transforms is converting time variations in signal intensity into a plot of the frequencies present in the signal. The military used this trick in the 1950s to convert raw radar return signals recorded by an aircraft in flight into a three-dimensional image of the landscape viewed by the plane. Today that conversion is done electronically, but the vacuum-tube computers of the 1950s were not up to the task.

Development of neural networks for artificial intelligence started with electronics, but their AI applications have been limited by their slow processing and need for extensive computing resources. Some researchers have developed hybrid neural networks, in which optics perform simple linear operations, but electronics perform more complex nonlinear calculations. Now two groups have demonstrated simple all-optical neural networks that do all processing with light.

In May, Wolfram Pernice of the Institute of Physics at the University of Münster in Germany and colleagues reported testing an all-optical “neuron” in which signals change target materials between liquid and solid states, an effect that has been used for optical data storage. They demonstrated nonlinear processing, and produced output pulses like those from organic neurons. They then produced an integrated photonic circuit that incorporated four optical neurons operating at different wavelengths, each of which connected to 15 optical synapses. The photonic circuit contained more than 140 components and could recognize simple optical patterns. The group wrote that their device is scalable, and that the technology promises “access to the high speed and high bandwidth inherent to optical systems, thus enabling the direct processing of optical telecommunication and visual data.”

Now a group at the Hong Kong University of Science and Technology reports in Optica that they have made an all-optical neural network based on a different process, electromagnetically induced transparency, in which incident light affects how atoms shift between quantum-mechanical energy levels. The process is nonlinear and can be triggered by very weak light signals, says Shengwang Du, a physics professor and coauthor of the paper.

In their demonstration, they illuminated rubidium-85 atoms cooled by lasers to about 10 microKelvin (10 microdegrees above absolute zero). Although the technique may seem unusually complex, Du said the system was the most accessible one in the lab that could produce the desired effects. “As a pure quantum atomic system [it] is ideal for this proof-of-principle experiment,” he says.

Next, they plan to scale up the demonstration using a hot atomic vapor center, which is less expensive, does not require time-consuming preparation of cold atoms, and can be integrated with photonic chips. Du says the major challenges are reducing cost of the nonlinear processing medium and increasing the scale of the all-optical neural network for more complex tasks.

“Their demonstration seems valid,” says Volker Sorger, an electrical engineer at George Washington University in Washington who was not involved in either demonstration. He says the all-optical approach is attractive because it offers very high parallelism, but the update rate is limited to about 100 hertz because of the liquid crystals used in their test, and he is not completely convinced their approach can be scaled error-free. Continue reading →

Posted in Human Robots

#435589 Construction Robots Learn to Excavate by ...

Posted on September 11, 2019 by Android

Pavel Savkin remembers the first time he watched a robot imitate his movements. Minutes earlier, the engineer had finished “showing” the robotic excavator its new goal by directing its movements manually. Now, running on software Savkin helped design, the robot was reproducing his movements, gesture for gesture. “It was like there was something alive in there—but I knew it was me,” he said.

Savkin is the CTO of SE4, a robotics software project that styles itself the “driver” of a fleet of robots that will eventually build human colonies in space. For now, SE4 is focused on creating software that can help developers communicate with robots, rather than on building hardware of its own.
The Tokyo-based startup showed off an industrial arm from Universal Robots that was running SE4’s proprietary software at SIGGRAPH in July. SE4’s demonstration at the Los Angeles innovation conference drew the company’s largest audience yet. The robot, nicknamed Squeezie, stacked real blocks as directed by SE4 research engineer Nathan Quinn, who wore a VR headset and used handheld controls to “show” Squeezie what to do.

As Quinn manipulated blocks in a virtual 3D space, the software learned a set of ordered instructions to be carried out in the real world. That order is essential for remote operations, says Quinn. To build remotely, developers need a way to communicate instructions to robotic builders on location. In the age of digital construction and industrial robotics, giving a computer a blueprint for what to build is a well-explored art. But operating on a distant object—especially under conditions that humans haven’t experienced themselves—presents challenges that only real-time communication with operators can solve.

The problem is that, in an unpredictable setting, even simple tasks require not only instruction from an operator, but constant feedback from the changing environment. Five years ago, the Swedish fiber network provider umea.net (part of the private Umeå Energy utility) took advantage of the virtual reality boom to promote its high-speed connections with the help of a viral video titled “Living with Lag: An Oculus Rift Experiment.” The video is still circulated in VR and gaming circles.

In the experiment, volunteers donned headgear that replaced their real-time biological senses of sight and sound with camera and audio feeds of their surroundings—both set at a 3-second delay. Thus equipped, volunteers attempt to complete everyday tasks like playing ping-pong, dancing, cooking, and walking on a beach, with decidedly slapstick results.

At outer-orbit intervals, including SE4’s dream of construction projects on Mars, the limiting factor in communication speed is not an artificial delay, but the laws of physics. The shifting relative positions of Earth and Mars mean that communications between the planets—even at the speed of light—can take anywhere from 3 to 22 minutes.

A long-distance relationship

Imagine trying to manage a construction project from across an ocean without the benefit of intelligent workers: sending a ship to an unknown world with a construction crew and blueprints for a log cabin, and four months later receiving a letter back asking how to cut down a tree. The parallel problem in long-distance construction with robots, according to SE4 CEO Lochlainn Wilson, is that automation relies on predictability. “Every robot in an industrial setting today is expecting a controlled environment.”
Platforms for applying AR and VR systems to teach tasks to artificial intelligences, as SE4 does, are already proliferating in manufacturing, healthcare, and defense. But all of the related communications systems are bound by physics and, specifically, the speed of light.
The same fundamental limitation applies in space. “Our communications are light-based, whether they’re radio or optical,” says Laura Seward Forczyk, a planetary scientist and consultant for space startups. “If you’re going to Mars and you want to communicate with your robot or spacecraft there, you need to have it act semi- or mostly-independently so that it can operate without commands from Earth.”

Semantic control
That’s exactly what SE4 aims to do. By teaching robots to group micro-movements into logical units—like all the steps to building a tower of blocks—the Tokyo-based startup lets robots make simple relational judgments that would allow them to receive a full set of instruction modules at once and carry them out in order. This sidesteps the latency issue in real-time bilateral communications that could hamstring a project or at least make progress excruciatingly slow.
The key to the platform, says Wilson, is the team’s proprietary operating software, “Semantic Control.” Just as in linguistics and philosophy, “semantics” refers to meaning itself, and meaning is the key to a robot’s ability to make even the smallest decisions on its own. “A robot can scan its environment and give [raw data] to us, but it can’t necessarily identify the objects around it and what they mean,” says Wilson.

That’s where human intelligence comes in. As part of the demonstration phase, the human operator of an SE4-controlled machine “annotates” each object in the robot’s vicinity with meaning. By labeling objects in the VR space with useful information—like which objects are building material and which are rocks—the operator helps the robot make sense of its real 3D environment before the building begins.

Giving robots the tools to deal with a changing environment is an important step toward allowing the AI to be truly independent, but it’s only an initial step. “We’re not letting it do absolutely everything,” said Quinn. “Our robot is good at moving an object from point A to point B, but it doesn’t know the overall plan.” Wilson adds that delegating environmental awareness and raw mechanical power to separate agents is the optimal relationship for a mixed human-robot construction team; it “lets humans do what they’re good at, while robots do what they do best.”

This story was updated on 4 September 2019. Continue reading →

Posted in Human Robots