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#435779 This Robot Ostrich Can Ride Around on ...
Proponents of legged robots say that they make sense because legs are often required to go where humans go. Proponents of wheeled robots say, “Yeah, that’s great but watch how fast and efficient my robot is, compared to yours.” Some robots try and take advantage of wheels and legs with hybrid designs like whegs or wheeled feet, but a simpler and more versatile solution is to do what humans do, and just take advantage of wheels when you need them.
We’ve seen a few experiments with this. The University of Michigan managed to convince Cassie to ride a Segway, with mostly positive (but occasionally quite negative) results. A Segway, and hoverboard-like systems, can provide wheeled mobility for legged robots over flat terrain, but they can’t handle things like stairs, which is kind of the whole point of having a robot with legs anyway.
Image: UC Berkeley
From left, a Segway, a hovercraft, and hovershoes, with complexity in terms of user control increasing from left to right.
At UC Berkeley’s Hybrid Robotics Lab, led by Koushil Sreenath, researchers have taken things a step further. They are teaching their Cassie bipedal robot (called Cassie Cal) to wheel around on a pair of hovershoes. Hovershoes are like hoverboards that have been chopped in half, resulting in a pair of motorized single-wheel skates. You balance on the skates, and control them by leaning forwards and backwards and left and right, which causes each skate to accelerate or decelerate in an attempt to keep itself upright. It’s not easy to get these things to work, even for a human, but by adding a sensor package to Cassie the UC Berkeley researchers have managed to get it to zip around campus fully autonomously.
Remember, Cassie is operating autonomously here—it’s performing vSLAM (with an Intel RealSense) and doing all of its own computation onboard in real time. Watching it jolt across that cracked sidewalk is particularly impressive, especially considering that it only has pitch control over its ankles and can’t roll its feet to maintain maximum contact with the hovershoes. But you can see the advantage that this particular platform offers to a robot like Cassie, including the ability to handle stairs. Stairs in one direction, anyway.
It’s a testament to the robustness of UC Berkeley’s controller that they were willing to let the robot operate untethered and outside, and it sounds like they’re thinking long-term about how legged robots on wheels would be real-world useful:
Our feedback control and autonomous system allow for swift movement through urban environments to aid in everything from food delivery to security and surveillance to search and rescue missions. This work can also help with transportation in large factories and warehouses.
For more details, we spoke with the UC Berkeley students (Shuxiao Chen, Jonathan Rogers, and Bike Zhang) via email.
IEEE Spectrum: How representative of Cassie’s real-world performance is what we see in the video? What happens when things go wrong?
Cassie’s real-world performance is similar to what we see in the video. Cassie can ride the hovershoes successfully all around the campus. Our current controller allows Cassie to robustly ride the hovershoes and rejects various perturbations. At present, one of the failure modes is when the hovershoe rolls to the side—this happens when it goes sideways down a step or encounters a large obstacle on one side of it, causing it to roll over. Under these circumstances, Cassie doesn’t have sufficient control authority (due to the thin narrow feet) to get the hovershoe back on its wheel.
The Hybrid Robotics Lab has been working on robots that walk over challenging terrain—how do wheeled platforms like hovershoes fit in with that?
Surprisingly, this research is related to our prior work on walking on discrete terrain. While locomotion using legs is efficient when traveling over rough and discrete terrain, wheeled locomotion is more efficient when traveling over flat continuous terrain. Enabling legged robots to ride on various micro-mobility platforms will offer multimodal locomotion capabilities, improving the efficiency of locomotion over various terrains.
Our current research furthers the locomotion ability for bipedal robots over continuous terrains by using a wheeled platform. In the long run, we would like to develop multi-modal locomotion strategies based on our current and prior work to allow legged robots to robustly and efficiently locomote in our daily life.
Photo: UC Berkeley
In their experiments, the UC Berkeley researchers say Cassie proved quite capable of riding the hovershoes over rough and uneven terrain, including going down stairs.
How long did it take to train Cassie to use the hovershoes? Are there any hovershoe skills that Cassie is better at than an average human?
We spent about eight months to develop our whole system, including a controller, a path planner, and a vision system. This involved developing mathematical models of Cassie and the hovershoes, setting up a dynamical simulation, figuring out how to interface and communicate with various sensors and Cassie, and doing several experiments to slowly improve performance. In contrast, a human with a good sense of balance needs a few hours to learn to use the hovershoes. A human who has never used skates or skis will probably need a longer time.
A human can easily turn in place on the hovershoes, while Cassie cannot do this motion currently due to our algorithm requiring a non-zero forward speed in order to turn. However, Cassie is much better at riding the hovershoes over rough and uneven terrain including riding the hovershoes down some stairs!
What would it take to make Cassie faster or more agile on the hovershoes?
While Cassie can currently move at a decent pace on the hovershoes and navigate obstacles, Cassie’s ability to avoid obstacles at rapid speeds is constrained by the sensing, the controller, and the onboard computation. To enable Cassie to dynamically weave around obstacles at high speeds exhibiting agile motions, we need to make progress on different fronts.
We need planners that take into account the entire dynamics of the Cassie-Hovershoe system and rapidly generate dynamically-feasible trajectories; we need controllers that tightly coordinate all the degrees-of-freedom of Cassie to dynamically move while balancing on the hovershoes; we need sensors that are robust to motion-blur artifacts caused due to fast turns; and we need onboard computation that can execute our algorithms at real-time speeds.
What are you working on next?
We are working on enabling more aggressive movements for Cassie on the hovershoes by fully exploiting Cassie’s dynamics. We are working on approaches that enable us to easily go beyond hovershoes to other challenging micro-mobility platforms. We are working on enabling Cassie to step onto and off from wheeled platforms such as hovershoes. We would like to create a future of multi-modal locomotion strategies for legged robots to enable them to efficiently help people in our daily life.
“Feedback Control for Autonomous Riding of Hovershoes by a Cassie Bipedal Robot,” by Shuxiao Chen, Jonathan Rogers, Bike Zhang, and Koushil Sreenath from the Hybrid Robotics Lab at UC Berkeley, has been submitted to IEEE Robotics and Automation Letters with option to be presented at the 2019 IEEE RAS International Conference on Humanoid Robots. Continue reading →
Posted in Human Robots
#435765 The Four Converging Technologies Giving ...
How each of us sees the world is about to change dramatically.
For all of human history, the experience of looking at the world was roughly the same for everyone. But boundaries between the digital and physical are beginning to fade.
The world around us is gaining layer upon layer of digitized, virtually overlaid information—making it rich, meaningful, and interactive. As a result, our respective experiences of the same environment are becoming vastly different, personalized to our goals, dreams, and desires.
Welcome to Web 3.0, or the Spatial Web. In version 1.0, static documents and read-only interactions limited the internet to one-way exchanges. Web 2.0 provided quite an upgrade, introducing multimedia content, interactive web pages, and participatory social media. Yet, all this was still mediated by two-dimensional screens.
Today, we are witnessing the rise of Web 3.0, riding the convergence of high-bandwidth 5G connectivity, rapidly evolving AR eyewear, an emerging trillion-sensor economy, and powerful artificial intelligence.
As a result, we will soon be able to superimpose digital information atop any physical surrounding—freeing our eyes from the tyranny of the screen, immersing us in smart environments, and making our world endlessly dynamic.
In the third post of our five-part series on augmented reality, we will explore the convergence of AR, AI, sensors, and blockchain and dive into the implications through a key use case in manufacturing.
A Tale of Convergence
Let’s deconstruct everything beneath the sleek AR display.
It all begins with graphics processing units (GPUs)—electric circuits that perform rapid calculations to render images. (GPUs can be found in mobile phones, game consoles, and computers.)
However, because AR requires such extensive computing power, single GPUs will not suffice. Instead, blockchain can now enable distributed GPU processing power, and blockchains specifically dedicated to AR holographic processing are on the rise.
Next up, cameras and sensors will aggregate real-time data from any environment to seamlessly integrate physical and virtual worlds. Meanwhile, body-tracking sensors are critical for aligning a user’s self-rendering in AR with a virtually enhanced environment. Depth sensors then provide data for 3D spatial maps, while cameras absorb more surface-level, detailed visual input. In some cases, sensors might even collect biometric data, such as heart rate and brain activity, to incorporate health-related feedback in our everyday AR interfaces and personal recommendation engines.
The next step in the pipeline involves none other than AI. Processing enormous volumes of data instantaneously, embedded AI algorithms will power customized AR experiences in everything from artistic virtual overlays to personalized dietary annotations.
In retail, AIs will use your purchasing history, current closet inventory, and possibly even mood indicators to display digitally rendered items most suitable for your wardrobe, tailored to your measurements.
In healthcare, smart AR glasses will provide physicians with immediately accessible and maximally relevant information (parsed from the entirety of a patient’s medical records and current research) to aid in accurate diagnoses and treatments, freeing doctors to engage in the more human-centric tasks of establishing trust, educating patients and demonstrating empathy.
Image Credit: PHD Ventures.
Convergence in Manufacturing
One of the nearest-term use cases of AR is manufacturing, as large producers begin dedicating capital to enterprise AR headsets. And over the next ten years, AR will converge with AI, sensors, and blockchain to multiply manufacturer productivity and employee experience.
(1) Convergence with AI
In initial application, digital guides superimposed on production tables will vastly improve employee accuracy and speed, while minimizing error rates.
Already, the International Air Transport Association (IATA) — whose airlines supply 82 percent of air travel — recently implemented industrial tech company Atheer’s AR headsets in cargo management. And with barely any delay, IATA reported a whopping 30 percent improvement in cargo handling speed and no less than a 90 percent reduction in errors.
With similar success rates, Boeing brought Skylight’s smart AR glasses to the runway, now used in the manufacturing of hundreds of airplanes. Sure enough—the aerospace giant has now seen a 25 percent drop in production time and near-zero error rates.
Beyond cargo management and air travel, however, smart AR headsets will also enable on-the-job training without reducing the productivity of other workers or sacrificing hardware. Jaguar Land Rover, for instance, implemented Bosch’s Re’flekt One AR solution to gear technicians with “x-ray” vision: allowing them to visualize the insides of Range Rover Sport vehicles without removing any dashboards.
And as enterprise capabilities continue to soar, AIs will soon become the go-to experts, offering support to manufacturers in need of assembly assistance. Instant guidance and real-time feedback will dramatically reduce production downtime, boost overall output, and even help customers struggling with DIY assembly at home.
Perhaps one of the most profitable business opportunities, AR guidance through centralized AI systems will also serve to mitigate supply chain inefficiencies at extraordinary scale. Coordinating moving parts, eliminating the need for manned scanners at each checkpoint, and directing traffic within warehouses, joint AI-AR systems will vastly improve workflow while overseeing quality assurance.
After its initial implementation of AR “vision picking” in 2015, leading courier company DHL recently announced it would continue to use Google’s newest smart lens in warehouses across the world. Motivated by the initial group’s reported 15 percent jump in productivity, DHL’s decision is part of the logistics giant’s $300 million investment in new technologies.
And as direct-to-consumer e-commerce fundamentally transforms the retail sector, supply chain optimization will only grow increasingly vital. AR could very well prove the definitive step for gaining a competitive edge in delivery speeds.
As explained by Vital Enterprises CEO Ash Eldritch, “All these technologies that are coming together around artificial intelligence are going to augment the capabilities of the worker and that’s very powerful. I call it Augmented Intelligence. The idea is that you can take someone of a certain skill level and by augmenting them with artificial intelligence via augmented reality and the Internet of Things, you can elevate the skill level of that worker.”
Already, large producers like Goodyear, thyssenkrupp, and Johnson Controls are using the Microsoft HoloLens 2—priced at $3,500 per headset—for manufacturing and design purposes.
Perhaps the most heartening outcome of the AI-AR convergence is that, rather than replacing humans in manufacturing, AR is an ideal interface for human collaboration with AI. And as AI merges with human capital, prepare to see exponential improvements in productivity, professional training, and product quality.
(2) Convergence with Sensors
On the hardware front, these AI-AR systems will require a mass proliferation of sensors to detect the external environment and apply computer vision in AI decision-making.
To measure depth, for instance, some scanning depth sensors project a structured pattern of infrared light dots onto a scene, detecting and analyzing reflected light to generate 3D maps of the environment. Stereoscopic imaging, using two lenses, has also been commonly used for depth measurements. But leading technology like Microsoft’s HoloLens 2 and Intel’s RealSense 400-series camera implement a new method called “phased time-of-flight” (ToF).
In ToF sensing, the HoloLens 2 uses numerous lasers, each with 100 milliwatts (mW) of power, in quick bursts. The distance between nearby objects and the headset wearer is then measured by the amount of light in the return beam that has shifted from the original signal. Finally, the phase difference reveals the location of each object within the field of view, which enables accurate hand-tracking and surface reconstruction.
With a far lower computing power requirement, the phased ToF sensor is also more durable than stereoscopic sensing, which relies on the precise alignment of two prisms. The phased ToF sensor’s silicon base also makes it easily mass-produced, rendering the HoloLens 2 a far better candidate for widespread consumer adoption.
To apply inertial measurement—typically used in airplanes and spacecraft—the HoloLens 2 additionally uses a built-in accelerometer, gyroscope, and magnetometer. Further equipped with four “environment understanding cameras” that track head movements, the headset also uses a 2.4MP HD photographic video camera and ambient light sensor that work in concert to enable advanced computer vision.
For natural viewing experiences, sensor-supplied gaze tracking increasingly creates depth in digital displays. Nvidia’s work on Foveated AR Display, for instance, brings the primary foveal area into focus, while peripheral regions fall into a softer background— mimicking natural visual perception and concentrating computing power on the area that needs it most.
Gaze tracking sensors are also slated to grant users control over their (now immersive) screens without any hand gestures. Conducting simple visual cues, even staring at an object for more than three seconds, will activate commands instantaneously.
And our manufacturing example above is not the only one. Stacked convergence of blockchain, sensors, AI and AR will disrupt almost every major industry.
Take healthcare, for example, wherein biometric sensors will soon customize users’ AR experiences. Already, MIT Media Lab’s Deep Reality group has created an underwater VR relaxation experience that responds to real-time brain activity detected by a modified version of the Muse EEG. The experience even adapts to users’ biometric data, from heart rate to electro dermal activity (inputted from an Empatica E4 wristband).
Now rapidly dematerializing, sensors will converge with AR to improve physical-digital surface integration, intuitive hand and eye controls, and an increasingly personalized augmented world. Keep an eye on companies like MicroVision, now making tremendous leaps in sensor technology.
While I’ll be doing a deep dive into sensor applications across each industry in our next blog, it’s critical to first discuss how we might power sensor- and AI-driven augmented worlds.
(3) Convergence with Blockchain
Because AR requires much more compute power than typical 2D experiences, centralized GPUs and cloud computing systems are hard at work to provide the necessary infrastructure. Nonetheless, the workload is taxing and blockchain may prove the best solution.
A major player in this pursuit, Otoy aims to create the largest distributed GPU network in the world, called the Render Network RNDR. Built specifically on the Ethereum blockchain for holographic media, and undergoing Beta testing, this network is set to revolutionize AR deployment accessibility.
Alphabet Chairman Eric Schmidt (an investor in Otoy’s network), has even said, “I predicted that 90% of computing would eventually reside in the web based cloud… Otoy has created a remarkable technology which moves that last 10%—high-end graphics processing—entirely to the cloud. This is a disruptive and important achievement. In my view, it marks the tipping point where the web replaces the PC as the dominant computing platform of the future.”
Leveraging the crowd, RNDR allows anyone with a GPU to contribute their power to the network for a commission of up to $300 a month in RNDR tokens. These can then be redeemed in cash or used to create users’ own AR content.
In a double win, Otoy’s blockchain network and similar iterations not only allow designers to profit when not using their GPUs, but also democratize the experience for newer artists in the field.
And beyond these networks’ power suppliers, distributing GPU processing power will allow more manufacturing companies to access AR design tools and customize learning experiences. By further dispersing content creation across a broad network of individuals, blockchain also has the valuable potential to boost AR hardware investment across a number of industry beneficiaries.
On the consumer side, startups like Scanetchain are also entering the blockchain-AR space for a different reason. Allowing users to scan items with their smartphone, Scanetchain’s app provides access to a trove of information, from manufacturer and price, to origin and shipping details.
Based on NEM (a peer-to-peer cryptocurrency that implements a blockchain consensus algorithm), the app aims to make information far more accessible and, in the process, create a social network of purchasing behavior. Users earn tokens by watching ads, and all transactions are hashed into blocks and securely recorded.
The writing is on the wall—our future of brick-and-mortar retail will largely lean on blockchain to create the necessary digital links.
Final Thoughts
Integrating AI into AR creates an “auto-magical” manufacturing pipeline that will fundamentally transform the industry, cutting down on marginal costs, reducing inefficiencies and waste, and maximizing employee productivity.
Bolstering the AI-AR convergence, sensor technology is already blurring the boundaries between our augmented and physical worlds, soon to be near-undetectable. While intuitive hand and eye motions dictate commands in a hands-free interface, biometric data is poised to customize each AR experience to be far more in touch with our mental and physical health.
And underpinning it all, distributed computing power with blockchain networks like RNDR will democratize AR, boosting global consumer adoption at plummeting price points.
As AR soars in importance—whether in retail, manufacturing, entertainment, or beyond—the stacked convergence discussed above merits significant investment over the next decade. The augmented world is only just getting started.
Join Me
(1) A360 Executive Mastermind: Want even more context about how converging exponential technologies will transform your business and industry? Consider joining Abundance 360, a highly selective community of 360 exponentially minded CEOs, who are on a 25-year journey with me—or as I call it, a “countdown to the Singularity.” If you’d like to learn more and consider joining our 2020 membership, apply here.
Share this with your friends, especially if they are interested in any of the areas outlined above.
(2) Abundance-Digital Online Community: I’ve also created a Digital/Online community of bold, abundance-minded entrepreneurs called Abundance-Digital. Abundance-Digital is Singularity University’s ‘onramp’ for exponential entrepreneurs — those who want to get involved and play at a higher level. Click here to learn more.
This article originally appeared on Diamandis.com
Image Credit: Funky Focus / Pixabay Continue reading →
Posted in Human Robots
#435662 Video Friday: This 3D-Printed ...
Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!):
ICRES 2019 – July 29-30, 2019 – London, U.K.
DARPA SubT Tunnel Circuit – August 15-22, 2019 – Pittsburgh, Pa., USA
IEEE Africon 2019 – September 25-27, 2019 – Accra, Ghana
ISRR 2019 – October 6-10, 2019 – Hanoi, Vietnam
Ro-Man 2019 – October 14-18, 2019 – New Delhi, India
Humanoids 2019 – October 15-17, 2019 – Toronto, Canada
Let us know if you have suggestions for next week, and enjoy today’s videos.
We’re used to seeing bristle bots about the size of a toothbrush head (which is not a coincidence), but Georgia Tech has downsized them, with some interesting benefits.
Researchers have created a new type of tiny 3D-printed robot that moves by harnessing vibration from piezoelectric actuators, ultrasound sources or even tiny speakers. Swarms of these “micro-bristle-bots” might work together to sense environmental changes, move materials – or perhaps one day repair injuries inside the human body.
The prototype robots respond to different vibration frequencies depending on their configurations, allowing researchers to control individual bots by adjusting the vibration. Approximately two millimeters long – about the size of the world’s smallest ant – the bots can cover four times their own length in a second despite the physical limitations of their small size.
“We are working to make the technology robust, and we have a lot of potential applications in mind,” said Azadeh Ansari, an assistant professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. “We are working at the intersection of mechanics, electronics, biology and physics. It’s a very rich area and there’s a lot of room for multidisciplinary concepts.”
[ Georgia Tech ]
Most consumer drones are “multi-copters,” meaning that they have a series of rotors or propellers that allow them to hover like helicopters. But having rotors severely limits their energy efficiency, which means that they can’t easily carry heavy payloads or fly for long periods of time. To get the best of both worlds, drone designers have tried to develop “hybrid” fixed-wing drones that can fly as efficiently as airplanes, while still taking off and landing vertically like multi-copters.
These drones are extremely hard to control because of the complexity of dealing with their flight dynamics, but a team from MIT CSAIL aims to make the customization process easier, with a new system that allows users to design drones of different sizes and shapes that can nimbly switch between hovering and gliding – all by using a single controller.
In future work, the team plans to try to further increase the drone’s maneuverability by improving its design. The model doesn’t yet fully take into account complex aerodynamic effects between the propeller’s airflow and the wings. And lastly, their method trained the copter with “yaw velocity” set at zero, which means that it cannot currently perform sharp turns.
[ Paper ] via [ MIT ]
We’re not quite at the point where we can 3D print entire robots, but UCSD is getting us closer.
The UC San Diego researchers’ insight was twofold. They turned to a commercially available printer for the job, (the Stratasys Objet350 Connex3—a workhorse in many robotics labs). In addition, they realized one of the materials used by the 3D printer is made of carbon particles that can conduct power to sensors when connected to a power source. So roboticists used the black resin to manufacture complex sensors embedded within robotic parts made of clear polymer. They designed and manufactured several prototypes, including a gripper.
When stretched, the sensors failed at approximately the same strain as human skin. But the polymers the 3D printer uses are not designed to conduct electricity, so their performance is not optimal. The 3D printed robots also require a lot of post-processing before they can be functional, including careful washing to clean up impurities and drying.
However, researchers remain optimistic that in the future, materials will improve and make 3D printed robots equipped with embedded sensors much easier to manufacture.
[ UCSD ]
Congrats to Team Homer from the University of Koblenz-Landau, who won the RoboCup@Home world championship in Sydney!
[ Team Homer ]
When you’ve got a robot with both wheels and legs, motion planning is complicated. IIT has developed a new planner for CENTAURO that takes advantage of the different ways that the robot is able to get past obstacles.
[ Centauro ]
Thanks Dimitrios!
If you constrain a problem tightly enough, you can solve it even with a relatively simple robot. Here’s an example of an experimental breakfast robot named “Loraine” that can cook eggs, bacon, and potatoes using what looks to be zero sensing at all, just moving to different positions and actuating its gripper.
There’s likely to be enough human work required in the prep here to make the value that the robot adds questionable at best, but it’s a good example of how you can make a relatively complex task robot-compatible as long as you set it up in just the right way.
[ Connected Robotics ] via [ RobotStart ]
It’s been a while since we’ve seen a ball bot, and I’m not sure that I’ve ever seen one with a manipulator on it.
[ ETH Zurich RSL ]
Soft Robotics’ new mini fingers are able to pick up taco shells without shattering them, which as far as I can tell is 100 percent impossible for humans to do.
[ Soft Robotics ]
Yes, Starship’s wheeled robots can climb curbs, and indeed they have a pretty neat way of doing it.
[ Starship ]
Last year we posted a long interview with Christoph Bartneck about his research into robots and racism, and here’s a nice video summary of the work.
[ Christoph Bartneck ]
Canada’s contribution to the Lunar Gateway will be a smart robotic system which includes a next-generation robotic arm known as Canadarm3, as well as equipment, and specialized tools. Using cutting-edge software and advances in artificial intelligence, this highly-autonomous system will be able to maintain, repair and inspect the Gateway, capture visiting vehicles, relocate Gateway modules, help astronauts during spacewalks, and enable science both in lunar orbit and on the surface of the Moon.
[ CSA ]
An interesting demo of how Misty can integrate sound localization with other services.
[ Misty Robotics ]
The third and last period of H2020 AEROARMS project has brought the final developments in industrial inspection and maintenance tasks, such as the crawler retrieval and deployment (DLR) or the industrial validation in stages like a refinery or a cement factory.
[ Aeroarms ]
The Guardian S remote visual inspection and surveillance robot navigates a disaster training site to demonstrate its advanced maneuverability, long-range wireless communications and extended run times.
[ Sarcos ]
This appears to be a cake frosting robot and I wish I had like 3 more hours of this to share:
Also here is a robot that picks fried chicken using a curiously successful technique:
[ Kazumichi Moriyama ]
This isn’t strictly robots, but professor Hiroshi Ishii, associate director of the MIT Media Lab, gave a fascinating SIGCHI Lifetime Achievement Talk that’s absolutely worth your time.
[ Tangible Media Group ] Continue reading →
Posted in Human Robots
#435605 All of the Winners in the DARPA ...
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
#435593 AI at the Speed of Light
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 →
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