Tag Archives: advanced

#436146 Video Friday: Kuka’s Robutt Is a ...

Posted on October 26, 2019 by Android

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!):

ARSO 2019 – October 31-1, 2019 – Beijing, China
ROSCon 2019 – October 31-1, 2019 – Macau
IROS 2019 – November 4-8, 2019 – Macau
Let us know if you have suggestions for next week, and enjoy today’s videos.

Kuka’s “robutt” can, according to the company, simulate “thousands of butts in the pursuit of durability and comfort.” Two of the robots are used at a Ford development center in Germany to evaluate new car seats. The tests are quite exhaustive, consisting of around 25,000 simulated sitting motions for each new seat design.” Or as Kuka puts it, “Pleasing all the butts on the planet is serious business.”

[ Kuka ]

Here’s a clever idea: 3D printing manipulators, and then using the 3D printer head to move those manipulators around and do stuff with them:

[ Paper ]

Two former soldiers performed a series of tests to see if the ONYX Exoskeleton gave them extra strength and endurance in difficult environments.

So when can I rent one of these to help me move furniture?

[ Lockheed ]

One of the defining characteristics of legged robots in general (and humanoid robots in particular) is the ability of walking on various types of terrain. In this video, we show our humanoid robot TORO walking dynamically over uneven (on grass outside the lab), rough (large gravel), and compliant terrain (a soft gym mattress). The robot can maintain its balance, even when the ground shifts rapidly under foot, such as when walking over gravel. This behaviour showcases the torque-control capability of quickly adapting the contact forces compared to position control methods.

An in-depth discussion of the current implementation is presented in the paper “Dynamic Walking on Compliant and Uneven Terrain using DCM and Passivity-based Whole-body Control”.

[ DLR RMC ]

Tsuki is a ROS-enabled quadruped designed and built by Lingkang Zhang. It’s completely position controlled, with no contact sensors on the feet, or even an IMU.

It can even do flips!

[ Tsuki ]

Thanks Lingkang!

TRI CEO Dr. Gill Pratt presents TRI’s contributions to Toyota’s New “LQ” Concept Vehicle, which includes onboard artificial intelligence agent “Yui” and LQ’s automated driving technology.

[ TRI ]

Hooman Hedayati wrote in to share some work (presented at HRI this year) on using augmented reality to make drone teleoperation more intuitive. Get a virtual drone to do what you want first, and then the real drone will follow.

[ Paper ]

Thanks Hooman!

You can now order a Sphero RVR for $250. It’s very much not spherical, but it does other stuff, so we’ll give it a pass.

[ Sphero ]

The AI Gamer Q56 robot is an expert at whatever this game is, using AI plus actual physical control manipulation. Watch until the end!

[ Bandai Namco ]

We present a swarm of autonomous flying robots for the exploration of unknown environments. The tiny robots do not make maps of their environment, but deal with obstacles on the fly. In robotics, the algorithms for navigating like this are called “bug algorithms”. The navigation of the robots involves them first flying away from the base station and later finding their way back with the help of a wireless beacon.

[ MAVLab ]

Okay Soft Robotics you successfully and disgustingly convinced us that vacuum grippers should never be used for food handling. Yuck!

[ Soft Robotics ]

Beyond the asteroid belt are “fossils of planet formation” known as the Trojan asteroids. These primitive bodies share Jupiter’s orbit in two vast swarms, and may hold clues to the formation and evolution of our solar system. Now, NASA is preparing to explore the Trojan asteroids for the first time. A mission called Lucy will launch in 2021 and visit seven asteroids over the course of twelve years – one in the main belt and six in Jupiter’s Trojan swarms.

[ NASA ]

I’m not all that impressed by this concept car from Lexus except that it includes some kind of super-thin autonomous luggage-carrying drone.

The LF-30 Electrified also carries the ‘Lexus Airporter’ drone-technology support vehicle. Using autonomous control, the Lexus Airporter is capable of such tasks as independently transporting baggage from a household doorstep to the vehicle’s luggage area.

[ Lexus ]

Vision 60 legged robot managing unstructured terrain without vision or force sensors in its legs. Using only high-transparency actuators and 2kHz algorithmic stability control… 4-limbs and 12-motors with only a velocity command.

[ Ghost Robotics ]

Tech United Eindhoven is looking good for RoboCup@Home 2020.

[ Tech United ]

Penn engineers participated in the Subterranean (SubT) Challenge hosted by DARPA, the Defense Advanced Research Projects Agency. The goal of this Challenge is for teams to develop automated systems that can work in underground environments so they could be deployed after natural disasters or on dangerous search-and-rescue missions.

[ Team PLUTO ]

It’s BeetleCam vs White Rhinos in Kenya, and the White Rhinos don’t seem to mind at all.

[ Will Burrard-Lucas ] Continue reading →

Posted in Human Robots

#435791 To Fly Solo, Racing Drones Have a Need ...

Posted on September 21, 2019 by Android

Drone racing’s ultimate vision of quadcopters weaving nimbly through obstacle courses has attracted far less excitement and investment than self-driving cars aimed at reshaping ground transportation. But the U.S. military and defense industry are betting on autonomous drone racing as the next frontier for developing AI so that it can handle high-speed navigation within tight spaces without human intervention.

The autonomous drone challenge requires split-second decision-making with six degrees of freedom instead of a car’s mere two degrees of road freedom. One research team developing the AI necessary for controlling autonomous racing drones is the Robotics and Perception Group at the University of Zurich in Switzerland. In late May, the Swiss researchers were among nine teams revealed to be competing in the two-year AlphaPilot open innovation challenge sponsored by U.S. aerospace company Lockheed Martin. The winning team will walk away with up to $2.25 million for beating other autonomous racing drones and a professional human drone pilot in head-to-head competitions.

“I think it is important to first point out that having an autonomous drone to finish a racing track at high speeds or even beating a human pilot does not imply that we can have autonomous drones [capable of] navigating in real-world, complex, unstructured, unknown environments such as disaster zones, collapsed buildings, caves, tunnels or narrow pipes, forests, military scenarios, and so on,” says Davide Scaramuzza, a professor of robotics and perception at the University of Zurich and ETH Zurich. “However, the robust and computationally efficient state estimation algorithms, control, and planning algorithms developed for autonomous drone racing would represent a starting point.”

The nine teams that made the cut—from a pool of 424 AlphaPilot applicants—will compete in four 2019 racing events organized under the Drone Racing League’s Artificial Intelligence Robotic Racing Circuit, says Keith Lynn, program manager for AlphaPilot at Lockheed Martin. To ensure an apples-to-apples comparison of each team’s AI secret sauce, each AlphaPilot team will upload its AI code into identical, specially-built drones that have the NVIDIA Xavier GPU at the core of the onboard computing hardware.

“Lockheed Martin is offering mentorship to the nine AlphaPilot teams to support their AI tech development and innovations,” says Lynn. The company “will be hosting a week-long Developers Summit at MIT in July, dedicated to workshopping and improving AlphaPilot teams’ code,” he added. He notes that each team will retain the intellectual property rights to its AI code.

The AlphaPilot challenge takes inspiration from older autonomous drone racing events hosted by academic researchers, Scaramuzza says. He credits Hyungpil Moon, a professor of robotics and mechanical engineering at Sungkyunkwan University in South Korea, for having organized the annual autonomous drone racing competition at the International Conference on Intelligent Robots and Systems since 2016.

It’s no easy task to create and train AI that can perform high-speed flight through complex environments by relying on visual navigation. One big challenge comes from how drones can accelerate sharply, take sharp turns, fly sideways, do zig-zag patterns and even perform back flips. That means camera images can suddenly appear tilted or even upside down during drone flight. Motion blur may occur when a drone flies very close to structures at high speeds and camera pixels collect light from multiple directions. Both cameras and visual software can also struggle to compensate for sudden changes between light and dark parts of an environment.

To lend AI a helping hand, Scaramuzza’s group recently published a drone racing dataset that includes realistic training data taken from a drone flown by a professional pilot in both indoor and outdoor spaces. The data, which includes complicated aerial maneuvers such as back flips, flight sequences that cover hundreds of meters, and flight speeds of up to 83 kilometers per hour, was presented at the 2019 IEEE International Conference on Robotics and Automation.

The drone racing dataset also includes data captured by the group’s special bioinspired event cameras that can detect changes in motion on a per-pixel basis within microseconds. By comparison, ordinary cameras need milliseconds (each millisecond being 1,000 microseconds) to compare motion changes in each image frame. The event cameras have already proven capable of helping drones nimbly dodge soccer balls thrown at them by the Swiss lab’s researchers.

The Swiss group’s work on the racing drone dataset received funding in part from the U.S. Defense Advanced Research Projects Agency (DARPA), which acts as the U.S. military’s special R&D arm for more futuristic projects. Specifically, the funding came from DARPA’s Fast Lightweight Autonomy program that envisions small autonomous drones capable of flying at high speeds through cluttered environments without GPS guidance or communication with human pilots.

Such speedy drones could serve as military scouts checking out dangerous buildings or alleys. They could also someday help search-and-rescue teams find people trapped in semi-collapsed buildings or lost in the woods. Being able to fly at high speed without crashing into things also makes a drone more efficient at all sorts of tasks by making the most of limited battery life, Scaramuzza says. After all, most drone battery life gets used up by the need to hover in flight and doesn’t get drained much by flying faster.

Even if AI manages to conquer the drone racing obstacle courses, that would be the end of the beginning of the technology’s development. What would still be required? Scaramuzza specifically singled out the need to handle low-visibility conditions involving smoke, dust, fog, rain, snow, fire, hail, as some of the biggest challenges for vision-based algorithms and AI in complex real-life environments.

“I think we should develop and release datasets containing smoke, dust, fog, rain, fire, etc. if we want to allow using autonomous robots to complement human rescuers in saving people lives after an earthquake or natural disaster in the future,” Scaramuzza says. Continue reading →

Posted in Human Robots

#435765 The Four Converging Technologies Giving ...

Posted on September 20, 2019 by Android

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

#435750 Video Friday: Amazon CEO Jeff Bezos ...

Posted on September 19, 2019 by Android

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):

RSS 2019 – June 22-26, 2019 – Freiburg, Germany
Hamlyn Symposium on Medical Robotics – June 23-26, 2019 – London, U.K.
ETH Robotics Summer School – June 27-1, 2019 – Zurich, Switzerland
MARSS 2019 – July 1-5, 2019 – Helsinki, Finland
ICRES 2019 – July 29-30, 2019 – London, U.K.
Let us know if you have suggestions for next week, and enjoy today’s videos.

Last week at the re:MARS conference, Amazon CEO and aspiring supervillain Jeff Bezos tried out this pair of dexterous robotic hands, which he described as “weirdly natural” to operate. The system combines Shadow Robot’s anthropomorphic robot hands with SynTouch’s biomimetic tactile sensors and HaptX’s haptic feedback gloves.

After playing with the robot, Bezos let out his trademark evil laugh.

[ Shadow Robot ]

The RoboMaster S1 is DJI’s advanced new educational robot that opens the door to limitless learning and entertainment. Develop programming skills, get familiar with AI technology, and enjoy thrilling FPV driving with games and competition. From young learners to tech enthusiasts, get ready to discover endless possibilities with the RoboMaster S1.

[ DJI ]

It’s very impressive to see DLR’s humanoid robot Toro dynamically balancing, even while being handed heavy objects, pushing things, and using multi-contact techniques to kick a fire extinguisher for some reason.

The paper is in RA-L, and you can find it at the link below.

[ RA-L ] via [ DLR ]

Thanks Maximo!

Is it just me, or does the Suzumori Endo Robotics Laboratory’s Super Dragon arm somehow just keep getting longer?

Suzumori Endo Lab, Tokyo Tech developed a 10 m-long articulated manipulator for investigation inside the primary containment vessel of the Fukushima Daiichi Nuclear Power Plants. We employed a coupled tendon-driven mechanism and a gravity compensation mechanism using synthetic fiber ropes to design a lightweight and slender articulated manipulator. This work was published in IEEE Robotics and Automation Letters and Transactions of the JSME.

[ Suzumori Endo Lab ]

From what I can make out thanks to Google Translate, this cute little robot duck (developed by Nissan) helps minimize weeds in rice fields by stirring up the water.

[ Nippon.com ]

Confidence in your robot is when you can just casually throw it off of a balcony 15 meters up.

[ SUTD ]

You had me at “we’re going to completely submerge this apple in chocolate syrup.”

[ Soft Robotics Inc ]

In the mid 2020s, the European Space Agency is planning on sending a robotic sample return mission to the Moon. It’s called Heracles, after the noted snake-strangler of Greek mythology.

[ ESA ]

Rethink Robotics is still around, they’re just much more German than before. And Sawyer is still hard at work stealing jobs from humans.

[ Rethink Robotics ]

The reason to watch this new video of the Ghost Robotics Vision 60 quadruped is for the 3 seconds worth of barrel roll about 40 seconds in.

[ Ghost Robotics ]

This is a relatively low-altitude drop for Squishy Robotics’ tensegrity scout, but it still cool to watch a robot that’s resilient enough to be able to fall and just not worry about it.

[ Squishy Robotics ]

We control here the Apptronik DRACO bipedal robot for unsupported dynamic locomotion. DRACO consists of a 10 DoF lower body with liquid cooled viscoelastic actuators to reduce weight, increase payload, and achieve fast dynamic walking. Control and walking algorithms are designed by UT HCRL Laboratory.

I think all robot videos should be required to start with two “oops” clips followed by a “for real now” clip.

[ Apptronik ]

SAKE’s EZGripper manages to pick up a wrench, and also pick up a raspberry without turning it into instajam.

[ SAKE Robotics ]

And now: the robotic long-tongued piggy, courtesy Sony Toio.

[ Toio ]

In this video the ornithopter developed inside the ERC Advanced Grant GRIFFIN project performs its first flight. This projects aims to develop a flapping wing system with manipulation and human interaction capabilities.

A flapping-wing system with manipulation and human interaction capabilities, you say? I would like to subscribe to your newsletter.

[ GRVC ]

KITECH’s robotic hands and arms can manipulate, among other things, five boxes of Elmos. I’m not sure about the conversion of Elmos to Snuffleupaguses, although it turns out that one Snuffleupagus is exactly 1,000 pounds.

[ Ji-Hun Bae ]

The Australian Centre for Field Robotics (ACFR) has been working on agricultural robots for almost a decade, and this video sums up a bunch of the stuff that they’ve been doing, even if it’s more amusing than practical at times.

[ ACFR ]

ROS 2 is great for multi-robot coordination, like when you need your bubble level to stay really, really level.

[ Acutronic Robotics ]

We don’t hear iRobot CEO Colin Angle give a lot of talks, so this recent one (from Amazon’s re:MARS conference) is definitely worth a listen, especially considering how much innovation we’ve seen from iRobot recently.

Colin Angle, founder and CEO of iRobot, has unveil a series of breakthrough innovations in home robots from iRobot. For the first time on stage, he will discuss and demonstrate what it takes to build a truly intelligent system of robots that work together to accomplish more within the home – and enable that home, and the devices within it, to work together as one.

[ iRobot ]

In the latest episode of Robots in Depth, Per speaks with Federico Pecora from the Center for Applied Autonomous Sensor Systems at Örebro University in Sweden.

Federico talks about working on AI and service robotics. In this area he has worked on planning, especially focusing on why a particular goal is the one that the robot should work on. To make robots as useful and user friendly as possible, he works on inferring the goal from the robot’s environment so that the user does not have to tell the robot everything.

Federico has also worked with AI robotics planning in industry to optimize results. Managing the relative importance of tasks is another challenging area there. In this context, he works on automating not only a single robot for its goal, but an entire fleet of robots for their collective goal. We get to hear about how these techniques are being used in warehouse operations, in mines and in agriculture.

[ Robots in Depth ] Continue reading →

Posted in Human Robots

#435712 U.S. Energy Department is First Customer ...

Posted on September 17, 2019 by Android

Argonne National Laboratory and Lawrence Livermore National Laboratory will be among the first organizations to install AI computers made from the largest silicon chip ever built. Last month, Cerebras Systems unveiled a 46,225-square millimeter chip with 1.2 trillion transistors designed to speed the training of neural networks. Today, such training is often done in large data centers using GPU-based servers. Cerebras plans to begin selling computers based on the notebook-size chip in the 4th quarter of this year.

“The opportunity to incorporate the largest and fastest AI chip ever—the Cerebras WSE—into our advanced computing infrastructure will enable us to dramatically accelerate our deep learning research in science, engineering, and health” Rick Stevens, head of computing at Argonne National Laboratory, said in a press release. “It will allow us to invent and test more algorithms, to more rapidly explore ideas, and to more quickly identify opportunities for scientific progress.”

Argonne and Lawrence Livermore are the first DOE entities to participate in what is expected to be a multi-year, multi-lab partnership. Cerebras plans to expand to other laboratories in the coming months.

Cerebras computers will be integrated into existing supercomputers at the two DOE labs to act as AI accelerators for those machines. In 2021, Argonne plans to become home to the United States’ first exascale computer, named Aurora; it will be capable of more than 1 billion billion calculations per second. Intel and Cray are the leaders on that $500 million project. The national laboratory is already home to Mira, the 24th-most powerful supercomputer in the world, and Theta, the 28th-most powerful. Lawrence Livermore is also on track to achieve exascale with El Capitan, a $600-million, 1.5-exaflop machine set to go live in late 2022. The lab is also home to the number-two-ranked Sierra supercomputer and the number-10-ranked Lassen.

The U.S. Energy Department established the Artificial Intelligence and Technology Office earlier this month to better take advantage of AI for solving the kinds of problems the U.S. national laboratories tackle. Continue reading →

Posted in Human Robots