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#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.
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This article originally appeared on Diamandis.com
Image Credit: Funky Focus / Pixabay Continue reading →
Posted in Human Robots
#435757 Robotic Animal Agility
An off-shore wind power platform, somewhere in the North Sea, on a freezing cold night, with howling winds and waves crashing against the impressive structure. An imperturbable ANYmal is quietly conducting its inspection.
ANYmal, a medium sized dog-like quadruped robot, walks down the stairs, lifts a “paw” to open doors or to call the elevator and trots along corridors. Darkness is no problem: it knows the place perfectly, having 3D-mapped it. Its laser sensors keep it informed about its precise path, location and potential obstacles. It conducts its inspection across several rooms. Its cameras zoom in on counters, recording the measurements displayed. Its thermal sensors record the temperature of machines and equipment and its ultrasound microphone checks for potential gas leaks. The robot also inspects lever positions as well as the correct positioning of regulatory fire extinguishers. As the electronic buzz of its engines resumes, it carries on working tirelessly.
After a little over two hours of inspection, the robot returns to its docking station for recharging. It will soon head back out to conduct its next solitary patrol. ANYmal played alongside Mulder and Scully in the “X-Files” TV series*, but it is in no way a Hollywood robot. It genuinely exists and surveillance missions are part of its very near future.
Off-shore oil platforms, the first test fields and probably the first actual application of ANYmal. ©ANYbotics
This quadruped robot was designed by ANYbotics, a spinoff of the Swiss Federal Institute of Technology in Zurich (ETH Zurich). Made of carbon fibre and aluminium, it weighs about thirty kilos. It is fully ruggedised, water- and dust-proof (IP-67). A kevlar belly protects its main body, carrying its powerful brain, batteries, network device, power management system and navigational systems.
ANYmal was designed for all types of terrain, including rubble, sand or snow. It has been field tested on industrial sites and is at ease with new obstacles to overcome (and it can even get up after a fall). Depending on its mission, its batteries last 2 to 4 hours.
On its jointed legs, protected by rubber pads, it can walk (at the speed of human steps), trot, climb, curl upon itself to crawl, carry a load or even jump and dance. It is the need to move on all surfaces that has driven its designers to choose a quadruped. “Biped robots are not easy to stabilise, especially on irregular terrain” explains Dr Péter Fankhauser, co-founder and chief business development officer of ANYbotics. “Wheeled or tracked robots can carry heavy loads, but they are bulky and less agile. Flying drones are highly mobile, but cannot carry load, handle objects or operate in bad weather conditions. We believe that quadrupeds combine the optimal characteristics, both in terms of mobility and versatility.”
What served as a source of inspiration for the team behind the project, the Robotic Systems Lab of the ETH Zurich, is a champion of agility on rugged terrain: the mountain goat. “We are of course still a long way” says Fankhauser. “However, it remains our objective on the longer term.
The first prototype, ALoF, was designed already back in 2009. It was still rather slow, very rigid and clumsy – more of a proof of concept than a robot ready for application. In 2012, StarlETH, fitted with spring joints, could hop, jump and climb. It was with this robot that the team started participating in 2014 in ARGOS, a full-scale challenge, launched by the Total oil group. The idea was to present a robot capable of inspecting an off-shore drilling station autonomously.
Up against dozens of competitors, the ETH Zurich team was the only team to enter the competition with such a quadrupedal robot. They didn’t win, but the multiple field tests were growing evermore convincing. Especially because, during the challenge, the team designed new joints with elastic actuators made in-house. These joints, inspired by tendons and muscles, are compact, sealed and include their own custom control electronics. They can regulate joint torque, position and impedance directly. Thanks to this innovation, the team could enter the same competition with a new version of its robot, ANYmal, fitted with three joints on each leg.
The ARGOS experience confirms the relevance of the selected means of locomotion. “Our robot is lighter, takes up less space on site and it is less noisy” says Fankhauser. “It also overcomes bigger obstacles than larger wheeled or tracked robots!” As ANYmal generated public interest and its transformation into a genuine product seemed more than possible, the startup ANYbotics was launched in 2016. It sold not only its robot, but also its revolutionary joints, called ANYdrive.
Today, ANYmal is not yet ready for sale to companies. However, ANYbotics has a growing number of partnerships with several industries, testing the robot for a few days or several weeks, for all types of tasks. Last October, for example, ANYmal navigated its way through the dark sewage system of the city of Zurich in order to test its capacity to help workers in similar difficult, repetitive and even dangerous tasks.
Why such an early interest among companies? “Because many companies want to integrate robots into their maintenance tasks” answers Fankhauser. “With ANYmal, they can actually evaluate its feasibility and plan their strategy. Eventually, both the architecture and the equipment of buildings could be rethought to be adapted to these maintenance robots”.
ANYmal requires ruggedised, sealed and extremely reliable interconnection solutions, such as LEMO. ©ANYbotics
Through field demonstrations and testing, ANYbotics can gather masses of information (up to 50,000 measurements are recorded every second during each test!) “It helps us to shape the product.” In due time, the startup will be ready to deliver a commercial product which really caters for companies’ needs.
Inspection and surveillance tasks on industrial sites are not the only applications considered. The startup is also thinking of agricultural inspections – with its onboard sensors, ANYmal is capable of mapping its environment, measuring bio mass and even taking soil samples. In the longer term, it could also be used for search and rescue operations. By the way, the robot can already be switched to “remote control” mode at any time and can be easily tele-operated. It is also capable of live audio and video transmission.
The transition from the prototype to the marketed product stage will involve a number of further developments. These include increasing ANYmal’s agility and speed, extending its capacity to map large-scale environments, improving safety, security, user handling and integrating the system with the customer’s data management software. It will also be necessary to enhance the robot’s reliability “so that it can work for days, weeks, or even months without human supervision.” All required certifications will have to be obtained. The locomotion system, which had triggered the whole business, is only one of a number of considerations of ANYbotics.
Designed for extreme environments, for ANYmal smoke is not a problem and it can walk in the snow, through rubble or in water. ©ANYbotics
The startup is not all alone. In fact, it has sold ANYmal robots to a dozen major universities who use them to develop their know-how in robotics. The startup has also founded ANYmal Research, a community including members such as Toyota Research Institute, the German Aerospace Center and the computer company Nvidia. Members have full access to ANYmal’s control software, simulations and documentation. Sharing has boosted both software and hardware ideas and developments (built on ROS, the open-source Robot Operating System). In particular, payload variations, providing for expandability and scalability. For instance, one of the universities uses a robotic arm which enables ANYmal to grasp or handle objects and open doors.
Among possible applications, ANYbotics mentions entertainment. It is not only about playing in more films or TV series, but rather about participating in various attractions (trade shows, museums, etc.). “ANYmal is so novel that it attracts a great amount of interest” confirms Fankhauser with a smile. “Whenever we present it somewhere, people gather around.”
Videos of these events show a fascinated and sometimes slightly fearful audience, when ANYmal gets too close to them. Is it fear of the “bad robot”? “This fear exists indeed and we are happy to be able to use ANYmal also to promote public awareness towards robotics and robots.” Reminiscent of a young dog, ANYmal is truly adapted for the purpose.
However, Péter Fankhauser softens the image of humans and sophisticated robots living together. “These coming years, robots will continue to work in the background, like they have for a long time in factories. Then, they will be used in public places in a selective and targeted way, for instance for dangerous missions. We will need to wait another ten years before animal-like robots, such as ANYmal will share our everyday lives!”
At the Consumer Electronics Show (CES) in Las Vegas in January, Continental, the German automotive manufacturing company, used robots to demonstrate a last-mile delivery. It showed ANYmal getting out of an autonomous vehicle with a parcel, climbing onto the front porch, lifting a paw to ring the doorbell, depositing the parcel before getting back into the vehicle. This futuristic image seems very close indeed.
*X-Files, season 11, episode 7, aired in February 2018 Continue reading →
Posted in Human Robots
#435738 Boing Goes the Trampoline Robot
There are a handful of quadrupedal robots out there that are highly dynamic, with the ability to run and jump, but those robots tend to be rather expensive and complicated, requiring powerful actuators and legs with elasticity. Boxing Wang, a Ph.D. student in the College of Control Science and Engineering at Zhejiang University in China, contacted us to share a project he’s been working to investigate quadruped jumping with simple, affordable hardware.
“The motivation for this project is quite simple,” Boxing says. “I wanted to study quadrupedal jumping control, but I didn’t have custom-made powerful actuators, and I didn’t want to have to design elastic legs. So I decided to use a trampoline to make a normal servo-driven quadruped robot to jump.”
Boxing and his colleagues had wanted to study quadrupedal running and jumping, so they built this robot with the most powerful servos they had access to: Kondo KRS6003RHV actuators, which have a maximum torque of 6 Nm. After some simple testing, it became clear that the servos were simply not fast or powerful enough to get the robot to jump, and that an elastic element was necessary to store energy to help the robot get off the ground.
“Normally, people would choose elastic legs,” says Boxing. “But nobody in my lab knew for sure how to design them. If we tried making elastic legs and we failed to make the robot jump, we couldn’t be sure whether the problem was the legs or the control algorithms. For hardware, we decided that it’s better to start with something reliable, something that definitely won’t be the source of the problem.”
As it turns out, all you need is a trampoline, an inertial measurement unit (IMU), and little tactile switches on the end of each foot to detect touch-down and lift-off events, and you can do some useful jumping research without a jumping robot. And the trampoline has other benefits as well—because it’s stiffer at the edges than at the center, for example, the robot will tend to center itself on the trampoline, and you get some warning before things go wrong.
“I can’t say that it’s a breakthrough to make a quadruped robot jump on a trampoline,” Boxing tells us. “But I believe this is useful for prototype testing, especially for people who are interested in quadrupedal jumping control but without a suitable robot at hand.”
To learn more about the project, we emailed him some additional questions.
IEEE Spectrum: Where did this idea come from?
Boxing Wang: The idea of the trampoline came while we were drinking milk tea. I don’t know why it came up, maybe someone saw a trampoline in a gym recently. And I don’t remember who proposed it exactly. It was just like someone said it unintentionally. But I realized that a trampoline would be a perfect choice. It’s reliable, easy to buy, and should have a similar dynamic model with the one of jumping with springy legs (we have briefly analyzed this in a paper). So I decided to try the trampoline.
How much do you think you can learn using a quadruped on a trampoline, instead of using a jumping quadruped?
Generally speaking, no contact surfaces are strictly rigid. They all have elasticity. So there are no essential differences between jumping on a trampoline and jumping on a rigid surface. However, using a quadruped on a trampoline can give you more information on how to make use of elasticity to make jumping easier and more efficient. You can use quadruped robots with springy legs to address the same problem, but that usually requires much more time on hardware design.
We prefer to treat the trampoline experiment as a kind of early test for further real jumping quadruped design. Unless you’re interested in designing an acrobatic robot on a trampoline, a real jumping quadruped is probably a more useful application, and that is our ultimate goal. The point of the trampoline tests is to develop the control algorithms first, and to examine the stability of the general hardware structure. Due to the similarity between jumping on a trampoline with rigid legs and jumping on hard surfaces with springy legs, the control algorithms you develop could be transferred to hard-surface jumping robots.
“Unless you’re interested in designing an acrobatic robot on a trampoline, a real jumping quadruped is probably a more useful application, and that is our ultimate goal. The point of the trampoline tests is to develop the control algorithms first, and to examine the stability of the general hardware structure”
Do you think that this idea can be beneficial for other kinds of robotics research?
Yes. For jumping quadrupeds with springy legs, the control algorithms could be first designed through trampoline tests using simple rigid legs. And the hardware design for elastic legs could be accelerated with the help of the control algorithms you design. In addition, we believe our work could be a good example of using a position-control robot to realize dynamic motions such as jumping, or even running.
Unlike other dynamic robots, every active joint in our robot is controlled through commercial position-control servos and not custom torque control motors. Most people don’t think that a position-control robot could perform highly dynamic motions such as jumping, because position-control motors usually mean high a gear ratio and slow response. However, our work indicates that, with the help of elasticity, stable jumping could be realized through position-control servos. So for those who already have a position-control robot at hand, they could explore the potential of their robot through trampoline tests.
Why is teaching a robot to jump important?
There are many scenarios where a jumping robot is needed. For example, a real jumping quadruped could be used to design a running quadruped. Both experience moments when all four legs are in the air, and it is easier to start from jumping and then move to running. Specifically, hopping or pronking can easily transform to bounding if the pitch angle is not strictly controlled. A bounding quadruped is similar to a running rabbit, so for now it can already be called a running quadruped.
To the best of our knowledge, a practical use of jumping quadrupeds could be planet exploration, just like what SpaceBok was designed for. In a low-gravity environment, jumping is more efficient than walking, and it’s easier to jump over obstacles. But if I had a jumping quadruped on Earth, I would teach it to catch a ball that I throw at it by jumping. It would be fantastic!
That would be fantastic.
Since the whole point of the trampoline was to get jumping software up and running with a minimum of hardware, the next step is to add some springy legs to the robot so that the control system the researchers developed can be tested on hard surfaces. They have a journal paper currently under revision, and Boxing Wang is joined as first author by his adviser Chunlin Zhou, undergrads Ziheng Duan and Qichao Zhu, and researchers Jun Wu and Rong Xiong. Continue reading →
Posted in Human Robots
#435681 Video Friday: This NASA Robot Uses ...
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
Let us know if you have suggestions for next week, and enjoy today’s videos.
Robots can land on the Moon and drive on Mars, but what about the places they can’t reach? Designed by engineers as NASA’s Jet Propulsion Laboratory in Pasadena, California, a four-limbed robot named LEMUR (Limbed Excursion Mechanical Utility Robot) can scale rock walls, gripping with hundreds of tiny fishhooks in each of its 16 fingers and using artificial intelligence to find its way around obstacles. In its last field test in Death Valley, California, in early 2019, LEMUR chose a route up a cliff, scanning the rock for ancient fossils from the sea that once filled the area.
The LEMUR project has since concluded, but it helped lead to a new generation of walking, climbing and crawling robots. In future missions to Mars or icy moons, robots with AI and climbing technology derived from LEMUR could discover similar signs of life. Those robots are being developed now, honing technology that may one day be part of future missions to distant worlds.
[ NASA ]
This video demonstrates the autonomous footstep planning developed by IHMC. Robots in this video are the Atlas humanoid robot (DRC version) and the NASA Valkyrie. The operator specifies a goal location in the world, which is modeled as planar regions using the robot’s perception sensors. The planner then automatically computes the necessary steps to reach the goal using a Weighted A* algorithm. The algorithm does not reject footholds that have a certain amount of support, but instead modifies them after the plan is found to try and increase that support area.
Currently, narrow terrain has a success rate of about 50%, rough terrain is about 90%, whereas flat ground is near 100%. We plan on increasing planner speed and the ability to plan through mazes and to unseen goals by including a body-path planner as the first step. Control, Perception, and Planning algorithms by IHMC Robotics.
[ IHMC ]
I’ve never really been able to get into watching people play poker, but throw an AI from CMU and Facebook into a game of no-limit Texas hold’em with five humans, and I’m there.
[ Facebook ]
In this video, Cassie Blue is navigating autonomously. Right now, her world is very small, the Wavefield at the University of Michigan, where she is told to turn left at intersections. You’re right, that is not a lot of independence, but it’s a first step away from a human and an RC controller!
Using a RealSense RGBD Camera, an IMU, and our version of an InEKF with contact factors, Cassie Blue is building a 3D semantic map in real time that identifies sidewalks, grass, poles, bicycles, and buildings. From the semantic map, occupancy and cost maps are built with the sidewalk identified as walk-able area and everything else considered as an obstacle. A planner then sets a goal to stay approximately 50 cm to the right of the sidewalk’s left edge and plans a path around obstacles and corners using D*. The path is translated into way-points that are achieved via Cassie Blue’s gait controller.
[ University of Michigan ]
Thanks Jesse!
Dave from HEBI Robotics wrote in to share some new actuators that are designed to get all kinds of dirty: “The R-Series takes HEBI’s X-Series to the next level, providing a sealed robotics solution for rugged, industrial applications and laying the groundwork for industrial users to address challenges that are not well met by traditional robotics. To prove it, we shot some video right in the Allegheny River here in Pittsburgh. Not a bad way to spend an afternoon :-)”
The R-Series Actuator is a full-featured robotic component as opposed to a simple servo motor. The output rotates continuously, requires no calibration or homing on boot-up, and contains a thru-bore for easy daisy-chaining of wiring. Modular in nature, R-Series Actuators can be used in everything from wheeled robots to collaborative robotic arms. They are sealed to IP67 and designed with a lightweight form factor for challenging field applications, and they’re packed with sensors that enable simultaneous control of position, velocity, and torque.
[ HEBI Robotics ]
Thanks Dave!
If your robot hands out karate chops on purpose, that’s great. If it hands out karate chops accidentally, maybe you should fix that.
COVR is short for “being safe around collaborative and versatile robots in shared spaces”. Our mission is to significantly reduce the complexity in safety certifying cobots. Increasing safety for collaborative robots enables new innovative applications, thus increasing production and job creation for companies utilizing the technology. Whether you’re an established company seeking to deploy cobots or an innovative startup with a prototype of a cobot related product, COVR will help you analyze, test and validate the safety for that application.
[ COVR ]
Thanks Anna!
EPFL startup Flybotix has developed a novel drone with just two propellers and an advanced stabilization system that allow it to fly for twice as long as conventional models. That fact, together with its small size, makes it perfect for inspecting hard-to-reach parts of industrial facilities such as ducts.
[ Flybotix ]
SpaceBok is a quadruped robot designed and built by a Swiss student team from ETH Zurich and ZHAW Zurich, currently being tested using Automation and Robotics Laboratories (ARL) facilities at our technical centre in the Netherlands. The robot is being used to investigate the potential of ‘dynamic walking’ and jumping to get around in low gravity environments.
SpaceBok could potentially go up to 2 m high in lunar gravity, although such a height poses new challenges. Once it comes off the ground the legged robot needs to stabilise itself to come down again safely – like a mini-spacecraft. So, like a spacecraft. SpaceBok uses a reaction wheel to control its orientation.
[ ESA ]
A new video from GITAI showing progress on their immersive telepresence robot for space.
[ GITAI ]
Tech United’s HERO robot (a Toyota HSR) competed in the RoboCup@Home competition, and it had a couple of garbage-related hiccups.
[ Tech United ]
Even small drones are getting better at autonomous obstacle avoidance in cluttered environments at useful speeds, as this work from the HKUST Aerial Robotics Group shows.
[ HKUST ]
DelFly Nimbles now come in swarms.
[ DelFly Nimble ]
This is a very short video, but it’s a fairly impressive look at a Baxter robot collaboratively helping someone put a shirt on, a useful task for folks with disabilities.
[ Shibata Lab ]
ANYmal can inspect the concrete in sewers for deterioration by sliding its feet along the ground.
[ ETH Zurich ]
HUG is a haptic user interface for teleoperating advanced robotic systems as the humanoid robot Justin or the assistive robotic system EDAN. With its lightweight robot arms, HUG can measure human movements and simultaneously display forces from the distant environment. In addition to such teleoperation applications, HUG serves as a research platform for virtual assembly simulations, rehabilitation, and training.
[ DLR ]
This video about “image understanding” from CMU in 1979 (!) is amazing, and even though it’s long, you won’t regret watching until 3:30. Or maybe you will.
[ ARGOS (pdf) ]
Will Burrard-Lucas’ BeetleCam turned 10 this month, and in this video, he recounts the history of his little robotic camera.
[ BeetleCam ]
In this week’s episode of Robots in Depth, Per speaks with Gabriel Skantze from Furhat Robotics.
Gabriel Skantze is co-founder and Chief Scientist at Furhat Robotics and Professor in speech technology at KTH with a specialization in conversational systems. He has a background in research into how humans use spoken communication to interact.
In this interview, Gabriel talks about how the social robot revolution makes it necessary to communicate with humans in a human ways through speech and facial expressions. This is necessary as we expand the number of people that interact with robots as well as the types of interaction. Gabriel gives us more insight into the many challenges of implementing spoken communication for co-bots, where robots and humans work closely together. They need to communicate about the world, the objects in it and how to handle them. We also get to hear how having an embodied system using the Furhat robot head helps the interaction between humans and the system.
[ Robots in Depth ] Continue reading →
Posted in Human Robots
#435660 Toyota Research Developing New ...
With the Olympics taking place next year in Japan, Toyota is (among other things) stepping up its robotics game to help provide “mobility for all.” We know that Toyota’s HSR will be doing work there, along with a few other mobile systems, but the Toyota Research Institute (TRI) has just announced a new telepresence robot called the T-TR1, featuring an absolutely massive screen designed to give you a near-lifesize virtual presence.
T-TR1 is a virtual mobility/tele-presence robot developed by Toyota Research Institute in the United States. It is equipped with a camera atop a large, near-lifesize display.
By projecting an image of a user from a remote location, the robot will help that person feel more physically present at the robot’s location.
With T-TR1, Toyota will give people that are physically unable to attend the events such as the Games a chance to virtually attend, with an on-screen presence capable of conversation between the two locations.
TRI isn’t ready to share much more detail on this system yet (we asked, of course), but we can infer some things from the video and the rest of the info that’s out there. For example, that ball on top is a 360-degree camera (that looks a lot like an Insta360 Pro), giving the remote user just as good of an awareness of their surroundings as they would if they were there in person. There are multiple 3D-sensing systems, including at least two depth cameras plus a lidar at the base. It’s not at all clear whether the robot is autonomous or semi-autonomous (using the sensors for automated obstacle avoidance, say), and since the woman on the other end of the robot does not seem to be controlling it at all for the demo, it’s hard to make an educated guess about the level of autonomy, or even how it’s supposed to be controlled.
We really like that enormous screen—despite the fact that telepresence now requires pants. It adds to the embodiment that makes independent telepresence robots useful.
We really like that enormous screen—despite the fact that telepresence now requires pants. It adds to the embodiment that makes independent telepresence robots useful. It’s also nice that the robot can move fast enough to keep up a person walking briskly. Hopefully, it’s safe for it to move at that speed in an environment more realistic than a carpeted, half-empty conference room, although it’ll probably have to leverage all of those sensors to do so. The other challenge for the T-TR1 will be bandwidth—even assuming that all of the sensor data processing and stuff is done on-robot, 360 cameras are huge bandwidth hogs, plus there’s the primary (presumably high quality) feed from the main camera, and then the video of the user coming the other way. It’s a lot of data in a very latency-sensitive application, and it’ll presumably be operating in places where connectivity is going to be a challenge due to crowds. This has always been a problem for telepresence robots—no matter how amazing your robot is, the experience will often for better or worse be defined by Internet connections that you may have no control over.
We should emphasize that Toyota has only released the bare minimum of information about the T-TR1, although we’re told that we can expect more as the 2020 Olympics approach: opening ceremonies are one year from today.
[ TRI ] Continue reading →
Posted in Human Robots