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Ronnie O'Sullivan has agreed to compete in new snooker events.

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Former FIA race director Niels Wittich has rejected the motorsport governing body's version regarding his departure.

The software used to control a robot is normally highly adapted to its specific physical set up. But now researchers have created a single general-purpose robotic control policy that can operate robotic arms, wheeled robots, quadrupeds, and even drones.

One of the biggest challenges when it comes to applying machine learning to robotics is the paucity of data. While computer vision and natural language processing can piggyback off the vast quantities of image and text data found on the Internet, collecting robot data is costly and time-consuming.

To get around this, there have been growing efforts to pool data collected by different groups on different kinds of robots, including the Open X-Embodiment and DROID datasets. The hope is that training on diverse robotics data will lead to “positive transfer,” which refers to when skills learned from training on one task help to boost performance on another.

The problem is that robots often have very different embodiments—a term used to describe their physical layout and suite of sensors and actuators—so the data they collect can vary significantly. For instance, a robotic arm might be static, have a complex arrangement of joints and fingers, and collect video from a camera on its wrist. In contrast, a quadruped robot is regularly on the move and relies on force feedback from its legs to maneuver. The kinds of tasks and actions these machines are trained to carry out are also diverse: The arm may pick and place objects, while the quadruped needs keen navigation.

That makes training a single AI model for robots on these large collections of data challenging, says Homer Walke, a Ph.D. student at the University of California, Berkeley. So far, most attempts have either focused on data from a narrower selection of similar robots or researchers have manually tweaked data to make observations from different robots more similar. But in research to be presented at the Conference on Robot Learning (CoRL) in Munich in November, they unveiled a new model called CrossFormer that can train on data from a diverse set of robots and control them just as well as specialized control policies.

“We want to be able to train on all of this data to get the most capable robot,” says Walke. “The main advance in this paper is working out what kind of architecture works the best for accommodating all these varying inputs and outputs.”

How to control diverse robots with the same AI model

The team used the same model architecture that powers large language model, known as a transformer. In many ways, the challenge the researchers were trying to solve is not dissimilar to that facing a chatbot, says Walke. In language modeling, the AI has to to pick out similar patterns in sentences with different lengths and word orders. Robot data can also be arranged in a sequence much like a written sentence, but depending on the particular embodiment, observations and actions vary in length and order too.

“Words might appear in different locations in a sentence, but they still mean the same thing,” says Walke. “In our task, an observation image might appear in different locations in the sequence, but it’s still fundamentally an image and we still want to treat it like an image.”

UC Berkeley/Carnegie Mellon University

Most machine learning approaches work through a sequence one element at a time, but transformers can process the entire stream of data at once. This allows them to analyze the relationship between different elements and makes them better at handling sequences that are not standardized, much like the diverse data found in large robotics datasets.

Walke and his colleagues aren’t the first to train transformers on large-scale robotics data. But previous approaches have either trained solely on data from robotic arms with broadly similar embodiments or manually converted input data to a common format to make it easier to process. In contrast, CrossFormer can process images from cameras positioned above a robot, at head height or on a robotic arms wrist, as well as joint position data from both quadrupeds and robotic arms, without any tweaks.

The result is a single control policy that can operate single robotic arms, pairs of robotic arms, quadrupeds, and wheeled robots on tasks as varied as picking and placing objects, cutting sushi, and obstacle avoidance. Crucially, it matched the performance of specialized models tailored for each robot and outperformed previous approaches trained on diverse robotic data. The team even tested whether the model could control an embodiment not included in the dataset—a small quadcopter. While they simplified things by making the drone fly at a fixed altitude, CrossFormer still outperformed the previous best method.

“That was definitely pretty cool,” says Ria Doshi, an undergraduate student at Berkeley. “I think that as we scale up our policy to be able to train on even larger sets of diverse data, it’ll become easier to see this kind of zero shot transfer onto robots that have been completely unseen in the training.”

The limitations of one AI model for all robots

The team admits there’s still work to do, however. The model is too big for any of the robots’ embedded chips and instead has to be run from a server. Even then, processing times are only just fast enough to support real-time operation, and Walke admits that could break down if they scale up the model. “When you pack so much data into a model it has to be very big and that means running it for real-time control becomes difficult.”

One potential workaround would be to use an approach called distillation, says Oier Mees, a postdoctoral research at Berkley and part of the CrossFormer team. This essentially involves training a smaller model to mimic the larger model, and if successful can result in similar performance for a much smaller computational budget.

But of more importance than the computing resource problem is that the team failed to see any positive transfer in their experiments, as CrossFormer simply matched previous performance rather than exceeding it. Walke thinks progress in computer vision and natural language processing suggests that training on more data could be the key.

Others say it might not be that simple. Jeannette Bohg, a professor of robotics at Stanford University, says the ability to train on such a diverse dataset is a significant contribution. But she wonders whether part of the reason why the researchers didn’t see positive transfer is their insistence on not aligning the input data. Previous research that trained on robots with similar observation and action data has shown evidence of such cross-overs. “By getting rid of this alignment, they may have also gotten rid of this significant positive transfer that we’ve seen in other work,” Bohg says.

It’s also not clear if the approach will boost performance on tasks specific to particular embodiments or robotic applications, says Ram Ramamoorthy, a robotics professor at Edinburgh University. The work is a promising step towards helping robots capture concepts common to most robots, like “avoid this obstacle,” he says. But it may be less useful for tackling control problems specific to a particular robot, such as how to knead dough or navigate a forest, which are often the hardest to solve.

Four decades after the first IEEE International Conference on Robotics and Automation (ICRA) in Atlanta, robotics is bigger than ever. Next week in Rotterdam is the IEEE ICRA@40 conference, “a celebration of 40 years of pioneering research and technological advancements in robotics and automation.” There’s an ICRA every year, of course. Arguably the largest robotics research conference in the world, the 2024 edition was held in Yokohama, Japan back in May.

ICRA@40 is not just a second ICRA conference in 2024. Next week’s conference is a single track that promises “a journey through the evolution of robotics and automation,” through four days of short keynotes from prominent roboticists from across the entire field. You can see for yourself, the speaker list is nuts. There are also debates and panels tackling big ideas, like: “What progress has been made in different areas of robotics and automation over the past decades, and what key challenges remain?” Personally, I’d say “lots” and “most of them,” but that’s probably why I’m not going to be up on stage.

There will also be interactive research presentations, live demos, an expo, and more—the conference schedule is online now, and the abstracts are online as well. I’ll be there to cover it all, but if you can make it in person, it’ll be worth it.

Forty years ago is a long time, but it’s not that long, so just for fun, I had a look at the proceedings of ICRA 1984 which are available on IEEE Xplore, if you’re curious. Here’s an excerpt of the forward from the organizers, which included folks from International Business Machines and Bell Labs:

The proceedings of the first IEEE Computer Society International Conference on Robotics contains papers covering practically all aspects of robotics. The response to our call for papers has been overwhelming, and the number of papers submitted by authors outside the United States indicates the strong international interest in robotics.
The Conference program includes papers on: computer vision; touch and other local sensing; manipulator kinematics, dynamics, control and simulation; robot programming languages, operating systems, representation, planning, man-machine interfaces; multiple and mobile robot systems.
The technical level of the Conference is high with papers being presented by leading researchers in robotics. We believe that this conference, the first of a series to be sponsored by the IEEE, will provide a forum for the dissemination of fundamental research results in this fast developing field.

Technically, this was “ICR,” not “ICRA,” and it was put on by the IEEE Computer Society’s Technical Committee on Robotics, since there was no IEEE Robotics and Automation Society at that time; RAS didn’t get off the ground until 1987.

Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.

ICRA@40: 23–26 September 2024, ROTTERDAM, NETHERLANDS

IROS 2024: 14–18 October 2024, ABU DHABI, UAE

ICSR 2024: 23–26 October 2024, ODENSE, DENMARK

Cybathlon 2024: 25–27 October 2024, ZURICH

Enjoy today’s videos!

Zipline has (finally) posted some real live footage of its new Platform 2 drone, and while it’s just as weird looking as before, it seems to actually work really well.

[ Zipline ]

I appreciate Disney Research’s insistence on always eventually asking, “okay, but can we get this to work on a real robot in the real world?”

[ Paper from ETH Zurich and Disney Research [PDF] ]

In this video, we showcase our humanoid robot, Nadia, being remotely controlled for boxing training using a simple VR motion capture setup. A remote user takes charge of Nadia’s movements, demonstrating the power of our advanced teleoperation system. Watch as Nadia performs precise boxing moves, highlighting the potential for humanoid robots in dynamic, real-world tasks.

[ IHMC ]

Guide dogs are expensive to train and maintain—if available at all. Because of these limiting factors, relatively few blind people use them. Computer science assistant professor Donghyun Kim and Ph.D candidate Hochul Hwang are hoping to change that with the help of UMass database analyst Gail Gunn and her guide dog, Brawny.

[ University of Massachusetts, Amherst ]

Thanks Julia!

The current paradigm for motion planning generates solutions from scratch for every new problem, which consumes significant amounts of time and computational resources. Our approach builds a large number of complex scenes in simulation, collects expert data from a motion planner, then distills it into a reactive generalist policy. We then combine this with lightweight optimization to obtain a safe path for real world deployment.

[ Neural MP ]

A nice mix of NAO and AI for embodied teaching.

[ Aldebaran ]

When retail and logistics giant Otto Group set out to strengthen its operational efficiency and safety, it turned to robotics and automation. The Otto Group has become the first company in Europe to deploy the mobile case handling robot Stretch, which unloads floor-loaded trailers and containers.

[ Boston Dynamics ]

From groceries to last-minute treats, Wing is here to make sure deliveries arrive quickly and safely. Our latest aircraft design features a larger, more standardized box and can carry a higher payload which came directly from customer and partner feedback.

[ Wing ]

It’s the jacket that gets me.

[ Devanthro ]

In this video, we introduce Rotograb, a robotic hand that merges the dexterity of human hands with the strength and efficiency of industrial grippers. Rotograb features a new rotating thumb mechanism, allowing for precision in-hand manipulation and power grasps while being adaptable. The robotic hand was developed by students during “Real World Robotics”, a master course by the Soft Robotics Lab at ETH Zurich.

[ ETH Zurich ]

A small scene where Rémi, our distinguished professor, is teaching chess to the person remotely operating Reachy! The grippers allow for easy and precise handling of chess pieces, even the small ones! The robot shown in this video is the Beta version of Reachy 2, our new robot coming very soon!

[ Pollen ]

Enhancing the adaptability and versatility of unmanned micro aerial vehicles (MAVs) is crucial for expanding their application range. In this article, we present a bimodal reconfigurable robot capable of operating in both regular quadcopter flight mode and a unique revolving flight mode, which allows independent control of the vehicle’s position and roll-pitch attitude.

[ City University Hong Kong ]

The Parallel Continuum Manipulator (PACOMA) is an advanced robotic system designed to replace traditional robotic arms in space missions, such as exploration, in-orbit servicing, and docking. Its design emphasizes robustness against misalignments and impacts, high precision and payload capacity, and sufficient mechanical damping for stable, controlled movements.

[ DFKI Robotics Innovation Center ]

Even the FPV pros from Team BlackSheep do, very occasionally, crash.

[ Team BlackSheep ]

This is a one-hour uninterrupted video of a robot cleaning bathrooms in real time. I’m not sure if it’s practical, but I am sure that it’s impressive, honestly.

[ Somatic ]

Thirteen years since a massive earthquake and tsunami struck the Fukushima Dai-ichi nuclear power plant in northern Japan, causing a loss of power, meltdowns and a major release of radioactive material, operator Tokyo Electric Power Co. (TEPCO) finally seems to be close to extracting the first bit of melted fuel from the complex—thanks to a special telescopic robotic device.

Despite Japan’s prowess in industrial robotics, TEPCO had no robots to deploy in the immediate aftermath of the disaster. Since then, however, robots have been used to measure radiation levels, clear building debris, and survey the exterior and interior of the plant overlooking the Pacific Ocean.

It will take decades to decommission Fukushima Dai-ichi, and one of the most dangerous, complex tasks is the removal and storage of about 880 tons of highly radioactive molten fuel in three reactor buildings that were operating when the tsunami hit. TEPCO believes mixtures of uranium, zirconium and other metals accumulated around the bottom of the primary containment vessels (PCVs) of the reactors—but the exact composition of the material is unknown. The material is “fuel debris,” which TEPCO defines as overheated fuel that has melted with fuel rods and in-vessel structures, then cooled and re-solidified. The extraction was supposed to begin in 2021 but ran into development delays and obstacles in the extraction route; the coronavirus pandemic also slowed work.

While TEPCO wants a molten fuel sample to analyze for exact composition, getting just a teaspoon of the stuff has proven so tricky that the job is years behind schedule. That may change soon as crews have deployed the telescoping device to target the 237 tons of fuel debris in Unit 2, which suffered less damage than the other reactor buildings and no hydrogen explosion, making it an easier and safer test bed.

“We plan to retrieve a small amount of fuel debris from Unit 2, analyze it to evaluate its properties and the process of its formation, and then move on to large-scale retrieval,” says Tatsuya Matoba, a spokesperson for TEPCO. “We believe that extracting as much information as possible from the retrieved fuel debris will likely contribute greatly to future decommissioning work.”

How TEPCO Plans to Retrieve a Fuel Sample

Getting to the fuel is easier said than done. Shaped like an inverted light bulb, the damaged PCV is a 33-meter-tall steel structure that houses the reactor pressure vessel where nuclear fission took place. A 2-meter-long isolation valve designed to block the release of radioactive material sits at the bottom of the PCV, and that’s where the robot will go in. The fuel debris itself is partly underwater.

The robot arm is being preceded by a smaller telescopic device. The telescopic device, which is trying to retrieve 3 grams of the fuel debris without further contamination to the outside environment, is similar to the larger robot arm, which is better suited for the retrieval of larger bits of debris.

Mitsubishi Heavy Industries, the International Research Institute for Nuclear Decommissioning and UK-based Veolia Nuclear Solutions developed the robot arm to enter small openings in the PCV, where it can survey the interior and grab the fuel. Mostly made of stainless steel and aluminum, the arm measures 22 meters long, weighs 4.6 tons and can move along 18 degrees of freedom. It’s a boom-style arm, not unlike the robotic arms on the International Space Station, that rests in a sealed enclosure box when not extended.

The arm consists of four main elements: a carriage that pushes the assembly through the openings, arm links that can fold up like a ream of dot matrix printer paper, an arm that has three telescopic stages, and a “wand” (an extendable pipe-shaped component) with cameras and a gripper on its tip. Both the arm and the wand can tilt downward toward the target area.

After the assembly is pushed through the PCV’s isolation valve, it angles downward over a 7.2-meter-long rail heading toward the base of the reactor. It continues through existing openings in the pedestal, a concrete structure supporting the reactor, and the platform, which is a flat surface under the reactor.

Then, the tip is lowered on a cable like the grabber in a claw machine toward the debris field at the bottom of the pedestal. The gripper tool at the end of the component has two delicate pincers (only 5 square millimeters), that can pinch a small pebble of debris. The debris is transferred to a container and, if all goes well, is brought back up through the openings and placed in a glovebox: A sealed, negative-pressure container in the reactor building where initial testing can be performed. It will then be moved to a Japan Atomic Energy Agency facility in nearby Ibaraki Prefecture for detailed analysis.

While the gripper on the telescopic device currently being used was able to reach the debris field and grasp a piece of rubble—it’s unknown if it was actually melted fuel—last month, two of the four cameras on the device stopped working a few days later, and the device was eventually reeled back into the enclosure box. Crews confirmed there were no problems with signal wiring from the control panel in the reactor building, and proceeded to perform oscilloscope testing. TEPCO speculates that radiation passing through camera semiconductor elements caused electrical charge to build up, and that the charge will drain if the cameras are left on in a relatively low-dose environment. It was the latest setback in a very long project.

“Retrieving fuel debris from Fukushima Daiichi Nuclear Power Station is an extremely difficult task, and a very important part of decommissioning,” says Matoba. “With the goal of completing the decommissioning in 30 to 40 years, we believe it is important to proceed strategically and systematically with each step of the work at hand.”

This story was updated on 15 October, 2024 to clarify that TEPCO is using two separate tools (a smaller telescopic device and a larger robot arm) in the process of retrieving fuel debris samples.

Simone Giertz came to fame in the 2010s by becoming the self-proclaimed “queen of shitty robots.” On YouTube she demonstrated a hilarious series of self-built mechanized devices that worked perfectly for ridiculous applications, such as a headboard-mounted alarm clock with a rubber hand to slap the user awake.

But Giertz has parlayed her Internet renown into Yetch, a design company that makes commercial consumer products. (The company name comes from how Giertz’s Swedish name is properly pronounced.) Her first release, a daily habit-tracking calendar, was picked up by prestigious outlets such as the Museum of Modern Art design store in New York City. She has continued to make commercial products since, as well as one-off strange inventions for her online audience.

Where did the motivation for your useless robots come from?

Simone Giertz: I just thought that robots that failed were really funny. It was also a way for me to get out of creating from a place of performance anxiety and perfection. Because if you set out to do something that fails, that gives you a lot of creative freedom.

You built up a big online following. A lot of people would be happy with that level of success. But you moved into inventing commercial products. Why?

Giertz: I like torturing myself, I guess! I’d been creating things for YouTube and for social media for a long time. I wanted to try something new and also find longevity in my career. I’m not super motivated to constantly try to get people to give me attention. That doesn’t feel like a very good value to strive for. So I was like, “Okay, what do I want to do for the rest of my career?” And developing products is something that I’ve always been really, really interested in. And yeah, it is tough, but I’m so happy to be doing it. I’m enjoying it thoroughly, as much as there’s a lot of face-palm moments.

Giertz’s every day goal calendar was picked up by the Museum of Modern Art’s design store. Yetch

What role does failure play in your invention process?

Giertz: I think it’s inevitable. Before, obviously, I wanted something that failed in the most unexpected or fun way possible. And now when I’m developing products, it’s still a part of it. You make so many different versions of something and each one fails because of something. But then, hopefully, what happens is that you get smaller and smaller failures. Product development feels like you’re going in circles, but you’re actually going in a spiral because the circles are taking you somewhere.

What advice do you have for aspiring inventors?

Giertz: Make things that you want. A lot of people make things that they think that other people want, but the main target audience, at least for myself, is me. I trust that if I find something interesting, there are probably other people who do too. And then just find good people to work with and collaborate with. There is no such thing as the lonely genius, I think. I’ve worked with a lot of different people and some people made me really nervous and anxious. And some people, it just went easy and we had a great time. You’re just like, “Oh, what if we do this? What if we do this?” Find those people.

This article appears in the November 2024 print issue as “The Queen of Useless Robots.”

Donald Trump will embrace the truth Joe Biden has refused to countenance: Israel's enemies are America's enemies. And when Israel defeats its enemies, America wins.

As we share our truths and witness each other's, we build unity and community.

Most people work just hard enough not to get fired and get paid just enough money not to quit. -George Carlin Every four years, presidential opinion polling reliably causes regime media to misplace...

Even though that perception is partly the creation of right-wing media, the Democrats surely need to hone their identity.

Upon further review, analyst Sean Trend may be right: There may be an emerging GOP majority nationwide.

Senate Republicans cannot be led by someone who is openly hostile to the agenda of their party's president and the base who elected him.

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Researchers calculated that if 30 per cent of vehicles in Chicago currently running on combustion engines were converted to electric, the reduction in pollution would save billions in health care costs every year. 

Scientists have recreated the scent of the embalming fluid used to preserve a noblewoman more than 3,500 years ago — and they say it's quite lovely, indeed.

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President-elect Donald Trump vowed on his first day in office to sign an executive order that seeks to end birthright citizenship for the children of illegal immigrants and kick-start the largest deportation effort in the nation's history.

President-elect Donald Trump has chosen lawyer Willian McGinley to serve as his White House counsel.

President-elect Donald Trump announced Tuesday that John Ratcliffe, a former Texas congressman and director of national intelligence, will serve as the director of the Central Intelligence Agency in his administration.