Tag Archives: movements

#435779 This Robot Ostrich Can Ride Around on ...

Posted on September 20, 2019 by Android

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 ...

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

#435681 Video Friday: This NASA Robot Uses ...

Posted on September 16, 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!):

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

#435669 Watch World Champion Soccer Robots Take ...

Posted on September 15, 2019 by Android

RoboCup 2019 took place earlier this month down in Sydney, Australia. While there are many different events including RoboCup@Home, RoboCup Rescue, and a bunch of different soccer leagues, one of the most compelling events is middle-size league (MSL), where mobile robots each about the size of a fire hydrant play soccer using a regular size FIFA soccer ball. The robots are fully autonomous, making their own decisions in real time about when to dribble, pass, and shoot.

The long-term goal of RoboCup is this:

By the middle of the 21st century, a team of fully autonomous humanoid robot soccer players shall win a soccer game, complying with the official rules of FIFA, against the winner of the most recent World Cup.

While the robots are certainly not there yet, they're definitely getting closer.

Even if you’re not a particular fan of soccer, it’s impressive to watch the robots coordinate with each other, setting up multiple passes and changing tactics on the fly in response to the movements of the other team. And the ability of these robots to shoot accurately is world-class (like, human world-class), as they’re seemingly able to put the ball in whatever corner of the goal they choose with split-second timing.

The final match was between Tech United from Eindhoven University of Technology in the Netherlands (whose robots are called TURTLE), and Team Water from Beijing Information Science & Technology University. Without spoiling it, I can tell you that the game was tied within just the last few seconds, meaning that it had to go to overtime. You can watch the entire match on YouTube, or a 5-minute commentated highlight video here:

It’s become a bit of a tradition to have the winning MSL robots play a team of what looks to be inexperienced adult humans wearing long pants and dress shoes.

The fact that the robots managed to score even once is pretty awesome, and it also looks like the robots are playing very conservatively (more so than the humans) so as not to accidentally injure any of us fragile meatbags with our spindly little legs. I get that RoboCup wants its first team of robots that can beat a human World Cup winning team to be humanoids, but at the moment, the MSL robots are where all the skill is.

To get calibrated on the state of the art for humanoid soccer robots, here’s the adult size final, Team Nimbro from the University of Bonn in Germany versus Team Sweaty from Offenburg University in Germany:

Yup, still a lot of falling over.

There’s lots more RoboCup on YouTube: Some channels to find more matches include the official RoboCup 2019 channel, and Tech United Eindhoven’s channel, which has both live English commentary and some highlight videos.

[ RoboCup 2019 ] Continue reading →

Posted in Human Robots

#435664 Swarm Robots Mimic Ant Jaws to Flip and ...

Posted on September 15, 2019 by Android

Small robots are appealing because they’re simple, cheap, and it’s easy to make a lot of them. Unfortunately, being simple and cheap means that each robot individually can’t do a whole lot. To make up for this, you can do what insects do—leverage that simplicity and low-cost to just make a huge swarm of simple robots, and together, they can cooperate to carry out relatively complex tasks.

Using insects as an example does set a bit of an unfair expectation for the poor robots, since insects are (let’s be honest) generally smarter and much more versatile than a robot on their scale could ever hope to be. Most robots with insect-like capabilities (like DASH and its family) are really too big and complex to be turned into swarms, because to make a vast amount of small robots, things like motors aren’t going to work because they’re too expensive.

The question, then, is to how to make a swarm of inexpensive small robots with insect-like mobility that don’t need motors to get around, and Jamie Paik’s Reconfigurable Robotics Lab at EPFL has an answer, inspired by trap-jaw ants.

Let’s talk about trap-jaw ants for just a second, because they’re insane. You can read this 2006 paper about them if you’re particularly interested in insane ants (and who isn’t!), but if you just want to hear the insane bit, it’s that trap-jaw ants can fire themselves into the air by biting the ground (!). In just 0.06 millisecond, their half-millimeter long mandibles can close at a top speed of 64 meters per second, which works out to an acceleration of about 100,000 g’s. Biting the ground causes the ant’s head to snap back with a force of 300 times the body weight of the ant itself, which launches the ant upwards. The ants can fly 8 centimeters vertically, and up to 15 cm horizontally—this is a lot, for an ant that’s just a few millimeters long.

Trap-jaw ants can fire themselves into the air by biting the ground, causing the ant’s head to snap back with a force of 300 times the body weight of the ant itself

EPFL’s robots, called Tribots, look nothing at all like trap-jaw ants, which personally I am fine with. They’re about 5 cm tall, weighing 10 grams each, and can be built on a flat sheet, and then folded into a tripod shape, origami-style. Or maybe it’s kirigami, because there’s some cutting involved. The Tribots are fully autonomous, meaning they have onboard power and control, including proximity sensors that allow them to detect objects and avoid them.

Photo: Marc Delachaux/EPFL

EPFL researchers Zhenishbek Zhakypov and Jamie Paik.

Avoiding objects is where the trap-jaw ants come in. Using two different shape-memory actuators (a spring and a latch, similar to how the ant’s jaw works), the Tribots can move around using a bunch of different techniques that can adapt to the terrain that they’re on, including:

Vertical jumping for height
Horizontal jumping for distance
Somersault jumping to clear obstacles
Walking on textured terrain with short hops (called “flic-flac” walking)
Crawling on flat surfaces

Here’s the robot in action:

Tribot’s maximum vertical jump is 14 cm (2.5 times its height), and horizontally it can jump about 23 cm (almost 4 times its length). Tribot is actually quite efficient in these movements, with a cost of transport much lower than similarly-sized robots, on par with insects themselves.

Working together, small groups of Tribots can complete tasks that a single robot couldn’t do alone. One example is pushing a heavy object a set distance. It turns out that you need five Tribots for this task—a leader robot, two worker robots, a monitor robot to measure the distance that the object has been pushed, and then a messenger robot to relay communications around the obstacle.

Image: EPFL

Five Tribots collaborate to move an object to a desired position, using coordination between a leader, two workers, a monitor, and a messenger robot. The leader orders the two worker robots to push the object while the monitor measures the relative position of the object. As the object blocks the two-way link between the leader and the monitor, the messenger maintains the communication link.

The researchers acknowledge that the current version of the hardware is limited in pretty much every way (mobility, sensing, and computation), but it does a reasonable job of demonstrating what’s possible with the concept. The plan going forward is to automate fabrication in order to “enable on-demand, ’push-button-manufactured’” robots.

“Designing minimal and scalable insect-inspired multi-locomotion millirobots,” by Zhenishbek Zhakypov, Kazuaki Mori, Koh Hosoda, and Jamie Paik from EPFL and Osaka University, is published in the current issue of Nature.
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