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.

The water column is hazy as an unusual remotely operated vehicle glides over the seafloor in search of a delicate tilt meter deployed three years ago off the west side of Vancouver Island. The sensor measures shaking and shifting in continental plates that will eventually unleash another of the region’s 9.0-scale earthquakes (the last was in 1700), and dwindling charge in the instruments’ data loggers threatens the continuity of the data.

The 4-metric-ton, C$8-million (US $5.8-million) remotely operated vehicle (ROV) is 50 meters from its target when one of the seismic science platforms appears on its SONAR imaging system, the platform’s hard edges crystallizing from the grainy background like a surgical implant jumping out of an ultrasound image. After easing the ROV to the platform, operators 2,575 meters up at the Pacific’s surface instruct its electromechanical arms and pincer hands to deftly unplug a data logger, then plug in a replacement with a fresh battery.

This mission, executed in early October, marked an exciting moment for Josh Tetarenko, director of ROV operations at North Vancouver, BC-based Canpac Marine Services. Tetarenko is the lead designer behind the new science submersible and recently dubbed it “Jenny” in homage to Forrest Gump, because the fictional character named all of his boats Jenny. Swapping out the data loggers west of Vancouver Island’s Clayoquot Sound was part of a week-long shakedown to test Jenny’s unique combination of dexterity, visualization chops, power, and pressure resistance.

Jenny is only the third science ROV designed for subsea work to a depth of 6,000 meters.

By all accounts Jenny sailed through. Tetarenko says the worst they saw was a leaky o-ring and the need to add some spring to a few bumpers. “Usually you see more things come up the first time you dive a vehicle to those depths,” says Tetarenko.

Jenny’s successful maiden cruise is just as important for Victoria, B.C.-based Ocean Networks Canada (ONC), which operates the NEPTUNE undersea observatory. Short for North-East Pacific Time-series Undersea Networked Experiments, the array boasts thousands of sensors and instruments, including deep-sea video cameras, seismometers, and robotic rovers sprawled across this corner of Pacific. Most of these are connected to shore via an 812-kilometer power and communications cable. Jenny was custom-designed to perform the annual maintenance and equipment swaps that have kept live data streaming from that cabled observatory nearly continuously for the past 15 years, despite trawler strikes, a fault on its backbone cable, and insults from corrosion, crushing pressures and fouling.

NEPTUNE remains one of the world’s largest installation for oceanographic science despite a proliferation of such cabled observatories since it went live in 2009. ONC’s open data portal has over 37,000 registered users tapping over 1.5 petabytes of ocean data—information that’s growing in importance with the intensification of climate change and the collapse of marine ecosystems.

Over the course of Jenny’s maiden cruise her operators swapped devices in and out at half a dozen ONC sites, including at several of Neptune’s five nodes and at one of Neptune’s smaller sister observatories closer to Vancouver.

ROV ‘Jenny’ aboard the Valour, Canpac’s 50-meter offshore workhorse, ahead of October’s Neptune observatory maintenance cruise.Ocean Networks Canada

What makes Jenny so special?

• Jenny is only the third science ROV designed for subsea work to a depth of 6,000 meters.
• Motion sensors actively adjust her 7,000-meter-long umbilical cable to counteract topside wave action that would otherwise yank the ROV around at depth and, in rough seas, could damage or snap the cable.
• Dual high-dexterity manipulator arms are controlled by topside operators via a pair of replica mini-manipulators that mirror the movements.
• Each arm is capable of picking up objects weighing about 275 kilograms, and the ROV itself can transport equipment weighing up to 3,000 kg.
• 11 high resolution cameras deliver 4K video, supported by 300,000 lumens of lighting that can be tuned to deliver the soft red light needed to observe bioluminescence.
• Dual multi-beam SONAR systems maximize visibility in turbid water.

Meghan Paulson, ONC’s executive director for observatory operations, says the sonar imaging system will be particularly invaluable during dives to shallower sites where sediments stirred up by waves and weather can cut visibility from meters to centimeters. “It really reduces the risk of running into things accidentally,” says Paulson.

To experience the visibility conditions for yourself, check out recordings of the live video broadcast from the NEPTUNE Maintenance Cruise. Tetarenko says that next year they hope to broadcast not only the main camera feed but also one of the sonar images.

3D video could be next, according to Canpac ROV pilot and Jenny co-designer, James Barnett. He says they would need to boost the computing power installed topside, to process that “firehose of data,” but insists that real-time 3D is “definitely not impossible.” Tetarenko says the science ROV community is collaborating on software to help make that workable: “3D imagining is kind of the very latest thing that’s being tested on lots of ROV systems right now, but nobody’s really there yet.”

Expansion of the cabled observatory concept is the more certain technological legacy for ONC and Neptune. In fact, the technology has evolved beyond just oceanography applications.

ONC tapped Alcatel Submarine Networks (ASN) to design and build the Neptune backbone and the French firm delivered a system that has reliably delivered multigigabit ethernet plus 10-kilovolts of direct-current electricity to the deep sea. Today ASN deploys a second-generation subsea power and communications networking solution, developed with Norwegian oil and gas major Equinor.

ASN’s ‘Direct Current / Fiber Optic‘ or DC/FO system provides the 100-km backbone for the ARCA subsea neutrino observatory near Sicily, in addition to providing control systems for a growing number of offshore oil and gas installations. The latter include projects led by Equinor and BP where DC/FO networks drive the subsea injection of captured carbon dioxide and monitor its storage below the seabed. Future oil and gas projects will increasingly rely on the cables’ power supply to replace the hydraulic lines that have traditionally been used to operate machinery on the seafloor, according to Ronan Michel, ASN’s product line manager for oil and gas solutions.

Michel says DC/FO incorporates important lessons learned from the Neptune installation. And the latter’s existence was a crucial prerequisite. “The DC/FO solution would probably not exist if Neptune Canada would not have been developed,” says Michel. “It probably gave confidence to Equinor that ASN was capable to develop subsea power & coms infrastructure.”

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.

Enjoy today’s videos!

One of the most venerable (and recognizable) mobile robots ever made, the Husky, has just gotten a major upgrade.

Shipping early next year.

[ Clearpath Robotics ]

[ MAB Robotics ]

Thanks, Jakub!

[ Unitree Robotics ]

Apparently, you can print out a functional four-fingered hand on an inkjet.

[ UC Berkeley ]

[ Robot Perception Lab, University College London ]

You had me at “3D desk octopus.”

[ UIST 2024 ACM Symposium on User Interface Software and Technology ]

Top-notch swag from Dusty Robotics

[ Dusty Robotics ]

I’m not sure how serious this shoes-versus-no-shoes test is, but it’s an interesting result nonetheless.

[ Robot Era ]

Thanks, Ni Tao!

[ LimX Dynamics ]

[ AnyCar ]

[ Paper ] via [ ETH Zurich Soft Robotics Laboratory ]

[ Paper ] via [ ETH Zurich Robotic Systems Lab ]

[ Engineered Arts ]

[ Aldebaran ]

This UPenn GRASP Lab seminar is from Antonio Loquercio, on “Simulation: What made us intelligent will make our robots intelligent.”

[ University of Pennsylvania ]

Today, Boston Dynamics and the Toyota Research Institute (TRI) announced a new partnership “to accelerate the development of general-purpose humanoid robots utilizing TRI’s Large Behavior Models and Boston Dynamics’ Atlas robot.” Committing to working towards a general purpose robot may make this partnership sound like a every other commercial humanoid company right now, but that’s not at all that’s going on here: BD and TRI are talking about fundamental robotics research, focusing on hard problems, and (most importantly) sharing the results.

The broader context here is that Boston Dynamics has an exceptionally capable humanoid platform capable of advanced and occasionally painful-looking whole-body motion behaviors along with some relatively basic and brute force-y manipulation. Meanwhile, TRI has been working for quite a while on developing AI-based learning techniques to tackle a variety of complicated manipulation challenges. TRI is working toward what they’re calling large behavior models (LBMs), which you can think of as analogous to large language models (LLMs), except for robots doing useful stuff in the physical world. The appeal of this partnership is pretty clear: Boston Dynamics gets new useful capabilities for Atlas, while TRI gets Atlas to explore new useful capabilities on.

Here’s a bit more from the press release:

For more details, we spoke with Scott Kuindersma (Senior Director of Robotics Research at Boston Dynamics) and Russ Tedrake (VP of Robotics Research at TRI).

How did this partnership happen?

Russ Tedrake: We have a ton of respect for the Boston Dynamics team and what they’ve done, not only in terms of the hardware, but also the controller on Atlas. They’ve been growing their machine learning effort as we’ve been working more and more on the machine learning side. On TRI’s side, we’re seeing the limits of what you can do in tabletop manipulation, and we want to explore beyond that.

Scott Kuindersma: The combination skills and tools that TRI brings the table with the existing platform capabilities we have at Boston Dynamics, in addition to the machine learning teams we’ve been building up for the last couple years, put us in a really great position to hit the ground running together and do some pretty amazing stuff with Atlas.

What will your approach be to communicating your work, especially in the context of all the craziness around humanoids right now?

Tedrake: There’s a ton of pressure right now to do something new and incredible every six months or so. In some ways, it’s healthy for the field to have that much energy and enthusiasm and ambition. But I also think that there are people in the field that are coming around to appreciate the slightly longer and deeper view of understanding what works and what doesn’t, so we do have to balance that.

The other thing that I’d say is that there’s so much hype out there. I am incredibly excited about the promise of all this new capability; I just want to make sure that as we’re pushing the science forward, we’re being also honest and transparent about how well it’s working.

Kuindersma: It’s not lost on either of our organizations that this is maybe one of the most exciting points in the history of robotics, but there’s still a tremendous amount of work to do.

What are some of the challenges that your partnership will be uniquely capable of solving?

Kuindersma: One of the things that we’re both really excited about is the scope of behaviors that are possible with humanoids—a humanoid robot is much more than a pair of grippers on a mobile base. I think the opportunity to explore the full behavioral capability space of humanoids is probably something that we’re uniquely positioned to do right now because of the historical work that we’ve done at Boston Dynamics. Atlas is a very physically capable robot—the most capable humanoid we’ve ever built. And the platform software that we have allows for things like data collection for whole body manipulation to be about as easy as it is anywhere in the world.

Tedrake: In my mind, we really have opened up a brand new science—there’s a new set of basic questions that need answering. Robotics has come into this era of big science where it takes a big team and a big budget and strong collaborators to basically build the massive data sets and train the models to be in a position to ask these fundamental questions.

Fundamental questions like what?

Tedrake: Nobody has the beginnings of an idea of what the right training mixture is for humanoids. Like, we want to do pre-training with language, that’s way better, but how early do we introduce vision? How early do we introduce actions? Nobody knows. What’s the right curriculum of tasks? Do we want some easy tasks where we get greater than zero performance right out of the box? Probably. Do we also want some really complicated tasks? Probably. We want to be just in the home? Just in the factory? What’s the right mixture? Do we want backflips? I don’t know. We have to figure it out.

There are more questions too, like whether we have enough data on the Internet to train robots, and how we could mix and transfer capabilities from Internet data sets into robotics. Is robot data fundamentally different than other data? Should we expect the same scaling laws? Should we expect the same long-term capabilities?

The other big one that you’ll hear the experts talk about is evaluation, which is a major bottleneck. If you look at some of these papers that show incredible results, the statistical strength of their results section is very weak and consequently we’re making a lot of claims about things that we don’t really have a lot of basis for. It will take a lot of engineering work to carefully build up empirical strength in our results. I think evaluation doesn’t get enough attention.

What has changed in robotics research in the last year or so that you think has enabled the kind of progress that you’re hoping to achieve?

Kuindersma: From my perspective, there are two high-level things that have changed how I’ve thought about work in this space. One is the convergence of the field around repeatable processes for training manipulation skills through demonstrations. The pioneering work of diffusion policy (which TRI was a big part of) is a really powerful thing—it takes the process of generating manipulation skills that previously were basically unfathomable, and turned it into something where you just collect a bunch of data, you train it on an architecture that’s more or less stable at this point, and you get a result.

The second thing is everything that’s happened in robotics-adjacent areas of AI showing that data scale and diversity are really the keys to generalizable behavior. We expect that to also be true for robotics. And so taking these two things together, it makes the path really clear, but I still think there are a ton of open research challenges and questions that we need to answer.

Do you think that simulation is an effective way of scaling data for robotics?

Tedrake: I think generally people underestimate simulation. The work we’ve been doing has made me very optimistic about the capabilities of simulation as long as you use it wisely. Focusing on a specific robot doing a specific task is asking the wrong question; you need to get the distribution of tasks and performance in simulation to be predictive of the distribution of tasks and performance in the real world. There are some things that are still hard to simulate well, but even when it comes to frictional contact and stuff like that, I think we’re getting pretty good at this point.

Is there a commercial future for this partnership that you’re able to talk about?

Kuindersma: For Boston Dynamics, clearly we think there’s long-term commercial value in this work, and that’s one of the main reasons why we want to invest in it. But the purpose of this collaboration is really about fundamental research—making sure that we do the work, advance the science, and do it in a rigorous enough way so that we actually understand and trust the results and we can communicate that out to the world. So yes, we see tremendous value in this commercially. Yes, we are commercializing Atlas, but this project is really about fundamental research.

What happens next?

Tedrake: There are questions at the intersection of things that BD has done and things that TRI has done that we need to do together to start, and that’ll get things going. And then we have big ambitions—getting a generalist capability that we’re calling LBM (large behavior models) running on Atlas is the goal. In the first year we’re trying to focus on these fundamental questions, push boundaries, and write and publish papers.

I want people to be excited about watching for our results, and I want people to trust our results when they see them. For me, that’s the most important message for the robotics community: Through this partnership we’re trying to take a longer view that balances our extreme optimism with being critical in our approach.

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.

Enjoy today’s videos!

At ICRA 2024, we sat down with Pollen Robotics to talk about Reachy 2 O_o

[ Pollen Robotics ]

[ Plantolin ]

[ Throwing Manipulation, multimedia extension for IEEE Robotics and Automation Letters ]

Thanks, Nagamanikandan!

[ Paper ]

Thanks, Sorina!

[ Corvus Robotics ]

Thanks, Jackie!

The first public walking demo of a new humanoid from Under Control Robotics.

[ Under Control Robotics ]

[ DARPA ]

That is also how I do pull-ups.

[ Jouhou System Kougaku Laboratory, University of Tokyo ]

Thanks, Kento!

[ Agility Robotics ]

[ JSK Robotics Laboratory, University of Tokyo GitHub ]

Thanks, Naoaki!

[ AxLab Actuated Experience Lab, University of Chicago ]

[ Engineered Arts ]

[ NASA Ames Research Center ]

[ HEBI Robotics ]

[ EPFL Reconfigurable Robotics Lab ]

[ Human Robot Interaction Podcast ]

[ Ingenuity Labs RAIS2024 Robotics Debate ]

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.”

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.

Approved for use by Japan’s Nuclear Regulation Authority on 31 July, a robot arm is trying to retrieve 3 grams of the fuel debris without further contamination to the outside environment. So what exactly is this robot and how does it work?

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 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.”

I’ve been reviewing robot vacuums for more than a decade, and robot mops for just as long. It’s been astonishing how the technology has evolved, from the original iRobot Roomba bouncing off of walls and furniture to robots that use lidar and vision to map your entire house and intelligently keep it clean.

As part of this evolution, cleaning robots have become more and more hands-off, and most of them are now able to empty themselves into occasionally enormous docks with integrated vacuums and debris bags. This means that your robot can vacuum your house, empty itself, recharge, and repeat this process until the dock’s dirt bag fills up.

But this all breaks down when it comes to robots that both vacuum and mop. Mopping, which is a capability that you definitely want if you have hard floors, requires a significant amount of clean water and generates an equally significant amount of dirty water. One approach is to make docks that are even more enormous—large enough to host tanks for clean and dirty water that you have to change out on a weekly basis.

SwitchBot, a company that got its start with a stick-on robotic switch that can make dumb things with switches into smart things, has been doing some clever things in the robotic vacuum space as well, and we’ve been taking a look at the SwitchBot S10, which hooks up to your home plumbing to autonomously manage all of its water needs. And I have to say, it works so well that it feels inevitable: this is the future of home robots.

The giant dock can collect debris from the robot for months, and also includes a hot air dryer for the roller mop.Evan Ackerman/IEEE Spectrum

The SwitchBot S10 is a hybrid robotic vacuum and mop that uses a Neato-style lidar system for localization and mapping. It’s also got a camera on the front to help it with obstacle avoidance. The mopping function uses a cloth-covered spinning roller that adds clean water and sucks out dirty water on every rotation. The roller lifts automatically when the robot senses that it’s about to move onto carpet. The S10 comes with a charging dock with an integrated vacuum and dust collection system, and there’s also a heated mop cleaner underneath, which is a nice touch.

I’m not going to spend a lot of time analyzing the S10’s cleaning performance. From what I can tell, it does a totally decent job vacuuming, and the mopping is particularly good thanks to the roller mop that exerts downward pressure on the floor while spinning. Just about any floor cleaning robot is going to do a respectable job with the actual floor cleaning—it’s all the other stuff, like software and interface and ease of use, that have become more important differentiators.

The water dock, seen here hooked up to my toilet and sink, exchanges dirty water out of the robot and includes an option to add cleaning fluid.Evan Ackerman/IEEE Spectrum

The S10’s primary differentiator is that it integrates with your home plumbing. It does this through a secondary dock—there’s the big charging dock, which you can put anywhere, and then the much smaller water dock, which is small enough to slide underneath an average toe-kick in a kitchen.

The dock includes a pumping system that accesses clean water through a pressurized water line, and then squirts dirty water out into a drain. The best place to find this combination of fixtures is near a sink with a p-trap, and if this is already beyond the limits of your plumbing knowledge, well, that’s the real challenge with the S10. The S10 is very much not plug-and-play; to install the water dock, you should be comfortable with basic tool use and, more importantly, have some faith in the integrity of your existing plumbing.

My house was built in the early 1960s, which means that a lot of my plumbing consists of old copper with varying degrees of corrosion and mineral infestation, along with slightly younger but somewhat brittle PVC. Installing the clean water line for the dock involves temporarily shutting off the cold water line feeding a sink or a toilet—that is, turning off a valve that may not have been turned for a decade or more. This is risky, and the potential consequences of any uncontrolled water leak are severe, so know where your main water shutoff is before futzing with the dock installation.

To SwitchBot’s credit, the actual water dock installation process was very easy, thanks to a suite of connectors and adapters that come included. I installed my dock in between a toilet and a pedestal sink, with access to the toilet’s water valve for clean water and the sink’s p-trap for dirty water. The water dock is battery powered, and cleverly charges from the robot itself, so it doesn’t need a power outlet. Even so, this one spot was pretty much the only place in my entire house where the water dock could easily go: my other bathrooms have cabinet sinks, which would have meant drilling holes for the water lines, and neither of them had floor space where the dock could live without being kicked all the time. It’s not like the water dock is all that big, but it really needs to be out of the way, and it can be hard to find a compatible space.

With the dock set up, the next step is mapping. The mapping process with the S10 was a bit finicky. I spent a bunch of time prepping my house—that is, moving as much furniture as possible off of the floor to give the robot the best chance at making a solid map. I know this isn’t something that most people probably do for their robots, but knowing robots like I do, I figure that getting a really good map is worth the hassle in the long run.

The first mapping run completed in about 20 minutes, but the robot got “stuck” on the way back to its dock thanks to a combination of a bit of black carpet and black coffee table legs. I rescued it, but it promptly forgot its map, and I had to start again. The second time, the robot failed to map my kitchen, dining room, laundry room, and one bathroom by not going through a wide open doorway off of the living room. This was confusing, because I could see the unexplored area on the map, and I’m not sure why the robot decided to call it a day rather than investigating that pretty obvious frontier region.

SwitchBot is not terrible at mapping, but it’s definitely sub-par relative to the experiences that I’ve had with older generations of other robots. The S10 also intermittently freaked out on the black patterned carpet that I have: moving very cautiously, spinning in circles, and occasionally stopping completely while complaining about malfunctioning cliff sensors, presumably because my carpet was absorbing all of the infrared from its cliff sensors while it was trying to map.

Black carpet, terror of robots everywhere.Evan Ackerman/IEEE Spectrum

Part of my frustration here is that I feel like I should be able to tell the robot “it’s a black carpet in that spot, you’re fine,” rather than taking such drastic measures as taping over all of the cliff sensors with tin foil, which I’ve had to do on occasion. And let me tell you how overjoyed I was to discover that the S10’s map editor has that exact option. You can also segment rooms by hand, and even position furniture to give the robot a clue on what kind of obstacles to expect. What’s missing is some way of asking the robot to explore a particular area over again, which would have made the initial process a lot easier.

Would a smarter robot be able to figure out all of this stuff on its own? Sure. But robots are dumb, and being able to manually add carpets and furniture and whatnot is an incredibly useful feature, I just wish I could do that during the mapping run somehow instead of having to spend a couple of hours getting that first map to work. Oh well.

When you ask the S10 to vacuum and mop, it leaves its charging dock and goes to the water dock. Once it docks there, it will extract any dirty water, clean its roller mop, extract the dirty water, wash its filter, and then finally refill itself with clean water before heading off to start mopping. It may do this several times over the course of a cleaning run, depending on how much water you ask it to use, but it’s quite good at managing all of this by itself. If you would like your floor to be extra clean, you can have the robot make two passes over the same area, which it does in a crosshatch pattern. And the app helpfully clues you in to everything that the robot is doing, including real-time position.

The app does and excellent job of showing where the robot has cleaned. You can also add furniture and floor types to help the robot clean better.Evan Ackerman/IEEE Spectrum

I’m pleasantly surprised by my experience with the S10 and the water dock. It was relatively easy to install and works exactly as it should. This is getting very close to the dream for robot vacuums, right? I will never have to worry about clean water tanks or dirty water tanks. The robot can mop every day if I want it to, and I don’t ever have to think about it, short of emptying the charging dock’s dustbin every few months and occasionally doing some basic robot maintenance.

Being able to access water on-demand for mopping is pretty great, but the S10’s water dock is about more than that. SwitchBot already has plans for a humidifier and dehumidifier, which can be filled and emptied with the S10 acting as a water shuttle. And the dehumidifier can even pull water out of the air and then the S10 can use that water to mop, which is pretty cool. I can think of two other applications for a water shuttle that are immediately obvious: pets, and plants.

SwitchBot is already planning for more ways of using the S10’s water transporting capability.SwitchBot

What about a water bowl for your pets that you can put anywhere in your house, and it’s always full of fresh water, thanks to a robot that not only tops the water off, but changes it completely? Or a little plant-sized dock that lives on the floor with a tube up to the pot of your leafy friend for some botanical thirst quenching? Heck, I have an entire fleet of robotic gardens that would love to be tended by a mobile water delivery system.

SwitchBot is not the only company to offer plumbing integration for home robots. Narwal and Roborock also have options for plumbing add-on kits to their existing docks, although they seem to be designed more for European or Asian homes where home plumbing tends to be designed a bit differently. And besides the added complication of systems like these, you’ll pay a premium for them: the SwitchBot S10 can cost as much as $1200, although it’s frequently on sale for less. As with all new features for floor care robots, though, you can expect the price to drop precipitously over the next several years as new features become standard, and I hope plumbing integration gets there soon, because I’m sold.

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.

Enjoy today’s videos!

Not even ladders can keep you safe from quadruped robots anymore.

[ ETH Zürich Robot Systems Lab ]

[ Engineered Arts ]

[ MEVIUS from JSK Robotics Laboratory ]

Thanks Kento!

[ EPFL Laboratory of Intelligent Systems ]

The only thing I want to know is whether Spot is programmed to actually do that cute little tilt when using its acoustic sensors.

[ Boston Dynamics ]

[ Agility Robotics ]

I don’t think “IP67” really does this justice.

[ ANYbotics ]

[ EPFL ]

It seems to be just renderings for now, but here’s the next version of Fourier’s humanoid.

[ Fourier ]

Happy Oktoberfest from Dino Robotics!

[ Dino Robotics ]

[ HiPeR Lab ]

[ SoloParkour ]

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.

Enjoy today’s videos!

By Jana Jost, Sebastian Hoose, Nils Gramse, Benedikt Pschera, and Jan Emmerich from Fraunhofer IML

[ Fraunhofer IML ]

By Adam Hung, Uksang Yoo, Jonathan Francis, Jean Oh, and Jeffrey Ichnowski from CMU Robotics Insittute

[ Carnegie Mellon University Robotics Institute ]

By Patrick O’Brien, Jakub F. Kowalewski, Chad C. Kessens, and Jeffrey Ian Lipton from Northeastern University Transformative Robotics Lab

[ Northeastern University ]

By Ning Yang , Amit Rogel , and Gil Weinberg from Georgia Tech

[ Georgia Tech ]

[ Istituto Italiano di Tecnologia ]

By Yusuke Tanaka, Alexander Schperberg, Alvin Zhu, and Dennis Hong from UCLA

[ Robotics Mechanical Laboratory at UCLA ]

By Jia-Yeu Lin and Atsuo Takanishi from Waseda University

[ Waseda University ]

By Peter Schaldenbrand, Gerry Chen, Vihaan Misra, Lorie Chen, Ken Goldberg, and Jean Oh from CMU

[ Carnegie Mellon University ]

[ DLR Institute of Robotics andMechatronics ]

By Hyoung-Rock Kim, DongKi Noh and Seung-Min Baek from LG

[ LG ]

By Aurelie Clodic, Raja Chatila, Marc Vaisset, Matthieu Herrb, Stephy Le Foll, Jerome Lamy, and Simon Lacroix from LAAS/CNRS (Note that the video narration is in French with English subtitles.)

[ LAAS/CNRS ]

By Vidyasagar Rajendran, William Thibault, Francisco Javier Andrade Chavez, and Katja Mombaur from University of Waterloo

[ University of Waterloo ]

By Dror Livnat, Yuval Lavi, Michael M. Bilevich, Tomer Buber, and Dan Halperin from Tel Aviv University

[ Tel Aviv University ]

By Fabio Zuliani, Neil Chennoufi, Alihan Bakir, Francesco Bruno, and Jamie Paik from EPFL

[ EPFL Reconfigurable Robotics Lab ]

By Merihan Alhafnawi, Lucia Stein-Montalvo, Jad Bendarkawi, Yenet Tafesse, Vicky Chow, Sigrid Adriaenssens, and Radhika Nagpal from Princeton University

[ Princeton University ]

By Emad Ebeid from University of southern Denmark

[ University of Southern Denmark (SDU) ]

By Yasuto Nakanishi from Keio University

[ Keio University ]

When we think of grasping robots, we think of manipulators of some sort on the ends of arms of some sort. Because of course we do—that’s how (most of us) are built, and that’s the mindset with which we have consequently optimized the world around us. But one of the great things about robots is that they don’t have to be constrained by our constraints, and at ICRA@40 in Rotterdam this week, we saw a novel new Thing: a robotic hand that can detach from its arm and then crawl around to grasp objects that would be otherwise out of reach, designed by roboticists from EPFL in Switzerland.

Fundamentally, robot hands and crawling robots share a lot of similarities, including a body along with some wiggly bits that stick out and do stuff. But most robotic hands are designed to grasp rather than crawl, and as far as I’m aware, no robotic hands have been designed to do both of those things at the same time. Since both capabilities are important, you don’t necessarily want to stick with a traditional grasping-focused hand design. The researchers employed a genetic algorithm and simulation to test a bunch of different configurations in order to optimize for the ability to hold things and to move.

You’ll notice that the fingers bend backwards as well as forwards, which effectively doubles the ways in which the hand (or, “Handcrawler”) can grasp objects. And it’s a little bit hard to tell from the video, but the Handcrawler attaches to the wrist using magnets for alignment along with a screw that extends to lock the hand into place.

“Although you see it in scary movies, I think we’re the first to introduce this idea to robotics.” —Xiao Gao, EPFL

The whole system is controlled manually in the video, but lead author Xiao Gao tells us that they already have an autonomous version (with external localization) working in the lab. In fact, they’ve managed to run an entire grasping sequence autonomously, with the Handcrawler detaching from the arm, crawling to a location the arm can’t reach, picking up an object, and then returning and reattaching itself to the arm again.

This is a sponsored article brought to you by Khalifa University of Science and Technology.

A total of eight intense competitions to inspire creativity and innovation along with 13 forums dedicated to diverse segments of robotics and artificial intelligence will be part of the 36th edition of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024) in Abu Dhabi.

These competitions at the Middle East and North Africa (MENA) region’s first-ever global conference and exhibition from 14-18 October 2024 at the Abu Dhabi National Exhibition Center (ADNEC) will highlight some of the key aspects of robotics. These include physical or athletic intelligence of robots, remote robot navigation, robot manipulation, underwater robotics, perception and sensing as well as challenges for wildlife preservation.

This edition of IROS is one of the largest of its kind globally in this category because of active participation across all levels, with 5,740 authors, 16 keynote speakers, 46 workshops, 11 tutorials, as well as 28 exhibitors and 12 startups. The forums at IROS will explore the rapidly evolving role of robotics in many industry sectors as well as policy-making and regulatory areas. Several leading corporate majors, and industry professionals from across the globe are gathering for IROS 2024 which is themed “Robotics for Sustainable Development.”

“The intense robotics competitions will inspire creativity, while the products on display as well as keynotes will pave the way for more community-relevant solutions.” —Jorge Dias, IROS 2024 General Chair

Dr. Jorge Dias, IROS 2024 General Chair, said: “Such a large gathering of scientists, researchers, industry leaders and government stakeholders in Abu Dhabi for IROS 2024 also demonstrates the role of UAE in pioneering new technologies and in providing an international platform for knowledge exchange and sharing of expertise. The intense robotics competitions will inspire creativity, while the products on display as well as keynotes will pave the way for more community-relevant solutions.”

The competitions are:

• 2nd AI Olympics With RealAIGym: Is AI Ready for Athletic Intelligence in the Real World? focusing on the physical or athletic intelligence of robots

• The Earth Rovers Challenge on remote robot navigation

• Robotic Construction Challenge and the euROBIN Manipulation Skill Versatility Challenge (MSVC) on robot manipulation

• Underwater Robotics Challenges on innovation and problem-solving in underwater robotics and artificial intelligence

• ROAD-IROS: Automatic Data Annotation Challenge for ROAD Dataset on perception and sensing

• 21st F1Tenth Autonomous Racing Competition

• IEEE RAS Quadruped Robot Challenge (QRC), focusing on navigation

In addition to these competitions, the Falcon Monitoring Challenge (FMC) will focus on advancing the field of wildlife tracking and conservation through the development of sophisticated, noninvasive monitoring systems.

Khalifa University

IROS 2024 will also include three keynote talks on ‘Robotic Competitions’ that will be moderated by Professor Lakmal Seneviratne, Director, Center for Autonomous Robotic Systems (KU-CARS), Khalifa University. The keynotes will be delivered by Professor Pedro Lima, Institute for Systems and Robotics, Instituto Superior Técnico, University of. Lisbon, Portugal; Dr. Timothy Chung, General Manager, Autonomy and Robotics, Microsoft, US; and Dr. Ubbo Visser, President of the RoboCup Federation, Director of Graduate Studies, and Associate Professor of Computer Science, University of Miami, US.

The forums at IROS 2024 will include:

• Robotics in Africa Forum

• Marine Robotics in Ocean Decade Initiative for Sustainable Development

• Robots for Sustainability and Sustainable Robots

• Government Forum: Funding for Robotics Research, Human-Avatars Symbiosis: Can you imagine a future society where you can remotely control multiple avatars?

• Moonshot R&D Program Goal 3 Forum. Envisioning a Future of Human-Robot Co-living: Potential for Robotics to Transform Human Lives

Other forums include:

• Sustainable Medical and Surgical Robotics

• Empowering Diverse Voices in Robotics, Robotics & AI in the UAE: Research Innovation and Entrepreneurship

• Europe Regulates Artificial Intelligence: the Challenge for Robotics, Industrial Opportunities and Socio-Economic Impact of Medical Robotics

• The Future of Work: AI-Enhanced Robotics and Human Interaction Research in M3S

• Robots for a Better Tomorrow: Wellbeing Through Advanced Technology.

One of the largest and most important robotics research conferences in the world, IROS 2024 provides a platform for the international robotics community to exchange knowledge and ideas about the latest advances in intelligent robots and smart machines. A total of 3,344 paper submissions representing 60 countries, have been received from researchers and scientists across the world. China tops the list with more than 1,000 papers, the US with 777, Germany with 302, Japan with 253, and the UK and South Korea with 173 each. The UAE remains top in the Arab region with 68 papers.

One of the largest and most important robotics research conferences in the world, IROS 2024 provides a platform for the international robotics community to exchange knowledge and ideas.

For eight consecutive years since 2017, Abu Dhabi has remained first on the world’s safest cities list, according to online database Numbeo, which assessed 329 global cities for the 2024 listing. This reflects the emirate’s ongoing efforts to ensure a good quality of life for citizens and residents. With a multicultural community, Abu Dhabi is home to people from more than 200 nationalities, and draws a large number of tourists to some of the top art galleries in the city such as Louvre Abu Dhabi and the Guggenheim Abu Dhabi, as well as other destinations such as Ferrari World Abu Dhabi and Warner Bros. World™ Abu Dhabi.

Because of its listing as one of the safest cities, Abu Dhabi continues to host several international conferences and exhibitions. Abu Dhabi is set to host the UNCTAD World Investment Forum, the 13th World Trade Organization (WTO) Ministerial Conference (MC13), the 12th World Environment Education Congress in 2024, and the IUCN World Conservation Congress in 2025.

IROS 2024 is sponsored by IEEE Robotics and Automation Society, Abu Dhabi Convention and Exhibition Bureau, the Robotics Society of Japan (RSJ), the Society of Instrument and Control Engineers (SICE), the New Technology Foundation, and the IEEE Industrial Electronics Society (IES).

More information at https://iros2024-abudhabi.org/

Where’s your flying car? I’m sorry to say that I have no idea. But here’s something that is somewhat similar, in that it flies, transports things, and has “car” in the name: it’s a flying cart, called the Palletrone (pallet+drone), designed for human-robot interaction-based aerial cargo transportation.

The way this thing works is fairly straightforward. The Palletrone will try to keep its roll and pitch at zero, to make sure that there’s a flat and stable platform for your preciouses, even if you don’t load those preciouses onto the drone evenly. Once loaded up, the drone relies on you to tell it where to go and what to do, using its IMU to respond to the slightest touch and translating those forces into control over the Palletrone’s horizontal, vertical, and yaw trajectories. This is particularly tricky to do, because the system has to be able to differentiate between the force exerted by cargo, and the force exerted by a human, since if the IMU senses a force moving the drone downward, it could be either. But professor Seung Jae Lee tells us that they developed “a simple but effective method to distinguish between them.”

Since the drone has to do all of this sensing and movement without pitching or rolling (since that would dump its cargo directly onto the floor) it’s equipped with internal propeller arms that can be rotated to vector thrust in any direction. We were curious about how having a bunch of unpredictable stuff sitting right above those rotors might affect the performance of the drone. But Seung Jae Lee says that the drone’s porous side structures allow for sufficient airflow and that even when the entire top of the drone is covered, thrust is only decreased by about 5 percent.

The current incarnation of the Palletrone is not particularly smart, and you need to remain in control of it, although if you let it go it will do its best to remain stationary (until it runs out of batteries). The researchers describe the experience of using this thing as “akin to maneuvering a shopping cart,” although I would guess that it’s somewhat noisier. In the video, the Palletrone is loaded down with just under 3 kilograms of cargo, which is respectable enough for testing. The drone is obviously not powerful enough to haul your typical grocery bag up the stairs to your apartment. But, it’s a couple of steps in the right direction, at least.

We also asked Seung Jae Lee about how he envisions the Palletrone being used, besides as just a logistics platform for either commercial or industrial use. “By attaching a camera to the platform, it could serve as a flying tripod or even act as a dolly, allowing for flexible camera movements and angles,” he says. “This would be particularly useful in environments where specialized filming equipment is difficult to procure.”

And for those of you about to comment something along the lines of, “this can’t possibly have enough battery life to be real-world useful,” they’re already working to solve that, with a docking system that allows one Palletrone to change the battery of another in-flight:

One Palletrone swaps out the battery of a second Palletrone.Seoul Tech

“The Palletrone Cart: Human-Robot Interaction-Based Aerial Cargo Transportation,” by Geonwoo Park, Hyungeun Park, Wooyong Park, Dongjae Lee, Murim Kim, and Seung Jae Lee from Seoul National University of Science and Technology in Korea, is published in IEEE Robotics And Automation Letters.

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.

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] ]

[ IHMC ]

[ University of Massachusetts, Amherst ]

Thanks Julia!

[ Neural MP ]

A nice mix of NAO and AI for embodied teaching.

[ Aldebaran ]

[ Boston Dynamics ]

[ Wing ]

It’s the jacket that gets me.

[ Devanthro ]

[ ETH Zurich ]

[ Pollen ]

[ City University Hong Kong ]

[ 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 ]

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:

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.

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 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.”

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 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.

This is a sponsored article brought to you by Khalifa University of Science and Technology.

Abu Dhabi-based Khalifa University of Science and Technology in the United Arab Emirates (UAE) will be hosting the 36th edition of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024) to highlight the Middle East and North Africa (MENA) region’s rapidly advancing capabilities in the robotics and intelligent transport systems.

aspect_ratio

Themed “Robotics for Sustainable Development,” the IROS 2024 will be held from 14-18 October 2024 at the Abu Dhabi National Exhibition Center (ADNEC) in the UAE’s capital city. It will offer a platform for universities and research institutions to display their research and innovation activities and initiatives in robotics, gathering researchers, academics, leading corporate majors, and industry professionals from around the globe.

A total of 13 forums, nine global-level competitions and challenges covering various aspects of robotics and AI, an IROS Expo, as well as an exclusive Career Fair will also be part of IROS 2024. The challenges and competitions will focus on physical or athletic intelligence of robots, remote robot navigation, robot manipulation, underwater robotics, as well as perception and sensing.

Delegates for the event will represent sectors including manufacturing, healthcare, logistics, agriculture, defense, security, and mining sectors with 60 percent of the talent pool having over six years of experience in robotics. A major component of the conference will be the poster sessions, keynotes, panel discussions by researchers and scientists, and networking events.

Khalifa University will be hosting IROS 2024 to highlight the Middle East and North Africa (MENA) region’s rapidly advancing capabilities in the robotics and intelligent transport systems.Khalifa University

Abu Dhabi ranks first on the world’s safest cities list in 2024, according to online database Numbeo, out of 329 global cities in the 2024 standings, holding the title for eight consecutive years since 2017, reflecting the emirate’s ongoing efforts to ensure a good quality of life for citizens and residents.

With a multicultural community, Abu Dhabi is home to people from more than 200 nationalities and draws a large number of tourists to some of the top art galleries in the city such as Louvre Abu Dhabi and the Guggenheim Abu Dhabi, as well as other destinations such as Ferrari World Abu Dhabi and Warner Bros. World Abu Dhabi.

The UAE and Abu Dhabi have increasingly become a center for creative skillsets, human capital and advanced technologies, attracting several international and regional events such as the global COP28 UAE climate summit, in which more than 160 countries participated.

Abu Dhabi city itself has hosted a number of association conventions such as the 34th International Nursing Research Congress and is set to host the UNCTAD World Investment Forum, the 13th World Trade Organization (WTO) Ministerial Conference (MC13), the 12th World Environment Education Congress in 2024, and the IUCN World Conservation Congress in 2025.

Khalifa University’s Center for Robotics and Autonomous Systems (KU-CARS) includes a vibrant multidisciplinary environment for conducting robotics and autonomous vehicle-related research and innovation.Khalifa University

Dr. Jorge Dias, IROS 2024 General Chair, said: “Khalifa University is delighted to bring the Intelligent Robots and Systems 2024 to Abu Dhabi in the UAE and highlight the innovations in line with the theme Robotics for Sustainable Development. As the region’s rapidly advancing capabilities in robotics and intelligent transport systems gain momentum, this event serves as a platform to incubate ideas, exchange knowledge, foster collaboration, and showcase our research and innovation activities. By hosting IROS 2024, Khalifa University aims to reaffirm the UAE’s status as a global innovation hub and destination for all industry stakeholders to collaborate on cutting-edge research and explore opportunities for growth within the UAE’s innovation ecosystem.”

“This event serves as a platform to incubate ideas, exchange knowledge, foster collaboration, and showcase our research and innovation activities” —Dr. Jorge Dias, IROS 2024 General Chair

Dr. Dias added: “The organizing committee of IROS 2024 has received over 4000 submissions representing 60 countries, with China leading with 1,029 papers, followed by the U.S. (777), Germany (302), and Japan (253), as well as the U.K. and South Korea (173 each). The UAE with a total of 68 papers comes atop the Arab region.”

Driving innovation at Khalifa University is the Center for Robotics and Autonomous Systems (KU-CARS) with around 50 researchers and state-of-the-art laboratory facilities, including a vibrant multidisciplinary environment for conducting robotics and autonomous vehicle-related research and innovation.

IROS 2024 is sponsored by IEEE Robotics and Automation Society, Abu Dhabi Convention and Exhibition Bureau, the Robotics Society of Japan (RSJ), the Society of Instrument and Control Engineers (SICE), the New Technology Foundation, and the IEEE Industrial Electronics Society (IES).

More information at https://iros2024-abudhabi.org/

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.

Enjoy today’s videos!

[ Nature ] via [ MPI ]

Thanks, Toshi!

[ ETH Zurich ]

How could you not be excited to see this happen for real?

[ arXiv paper ]

Can we all agree that sanding, grinding, deburring, and polishing tasks are really best done by robots, for the most part?

[ Cohesive Robotics ]

Thanks, David!

[ arXiv paper ]

Fall of 2025 is a long enough time from now that I’m not even going to speculate about it.

[ Weave Robotics ]

[ Science Robotics ] via [ Yale ]

[ NASA ]

I love this and want to burn it with fire.

[ Carpentopod ]

[ Pollen Robotics ]

[ ScalAR Lab ]

[ Astorino ]

I guess fish-fillet-shaping robots need to exist because otherwise customers will freak out if all their fish fillets are not identical, or something?

[ Flexiv ]

[ ESA ]

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.

Enjoy today’s videos!

[ HAND ]

The Environmental Robotics Lab at ETH Zurich, in partnership with Wilderness International (and some help from DJI and Audi), is using drones to sample DNA from the tops of trees in the Peruvian rainforest. Somehow, the treetops are where 60 to 90 percent of biodiversity is found, and these drones can help researchers determine what the heck is going on up there.

[ ERL ]

Thanks, Steffen!

1X introduces NEO Beta, “the pre-production build of our home humanoid.”

[ 1X ]

We love MangDang’s fun and affordable approach to robotics with Mini Pupper. The next generation of the little legged robot has just launched on Kickstarter, featuring new and updated robots that make it easy to explore embodied AI.

The Kickstarter is already fully funded after just a day or two, but there are still plenty of robots up for grabs.

[ Kickstarter ]

Quadrupeds in space can use their legs to reorient themselves. Or, if you throw one off a roof, it can learn to land on its feet.

To be presented at CoRL 2024.

[ ARL ]

HEBI Robotics, which apparently was once headquartered inside a Pittsburgh public bus, has imbued a table with actuators and a mind of its own.

[ HEBI Robotics ]

Carcinization is a concept in evolutionary biology where a crustacean that isn’t a crab eventually becomes a crab. So why not do the same thing with robots? Crab robots solve all problems!

[ KAIST ]

Waymo is smart, but also humans are really, really dumb sometimes.

[ Waymo ]

[ Michigan Robotics ]

I appreciate that UBTECH’s humanoid is doing an actual job, but why would you use a humanoid for this?

[ UBTECH ]

I’m sure most actuators go through some form of lifecycle testing. But if you really want to test an electric motor, put it into a BattleBot and see what happens.

[ Hardcore Robotics ]

Yes, but have you tried fighting a BattleBot?

[ AgileX ]

[ Hybrid Robotics ]

I had not realized that the floretizing of broccoli was so violent.

[ Oxipital ]

[ B-Human ]

At ICRA 2024, Spectrum editor Evan Ackerman sat down with Unitree founder and CEO Xingxing Wang and Tony Yang, VP of Business Development, to talk about the company’s newest humanoid, the G1 model.

Smaller, more flexible, and elegant, the G1 robot is designed for general use in service and industry, and is one of the cheapest—if not the cheapest—humanoid around.

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.

Enjoy today’s videos!

Imbuing robots with “human-level performance” in anything is an enormous challenge, but it’s worth it when you see a robot with the skill to interact with a human on a (nearly) human level. Google DeepMind has managed to achieve amateur human-level competence at table tennis, which is much harder than it looks, even for humans. Pannag Sanketi, a tech-lead manager in the robotics team at DeepMind, shared some interesting insights about performing the research. But first, video!

Some behind the scenes detail from Pannag:

• The robot had not seen any participants before. So we knew we had a cool agent, but we had no idea how it was going to fare in a full match with real humans. To witness it outmaneuver even some of the most advanced players was such a delightful moment for team!
• All the participants had a lot of fun playing against the robot, irrespective of who won the match. And all of them wanted to play more. Some of them said it will be great to have the robot as a playing partner. From the videos, you can even see how much fun the user study hosts sitting there (who are not authors on the paper) are having watching the games!
• Barney, who is a professional coach, was an advisor on the project, and our chief evaluator of robot’s skills the way he evaluates his students. He also got surprised by how the robot is always able to learn from the last few weeks’ sessions.
• We invested a lot in remote and automated 24x7 operations. So not the setup in this video, but there are other cells that we can run 24x7 with a ball thrower.
• We even tried robot-vs-robot, i.e. 2 robots playing against each other! :) The line between collaboration and competition becomes very interesting when they try to learn by playing with each other.

[ DeepMind ]

Thanks, Heni!

Yoink.

[ MIT ]

Considering how their stability and recovery is often tested, teaching robot dogs to be shy of humans is an excellent idea.

[ Deep Robotics ]

Yes, quadruped robots need tow truck hooks.

[ Paper ]

Earthworm-inspired robots require novel actuators, and Ayato Kanada at Kyushu University has come up with a neat one.

[ Paper ]

Thanks, Ayato!

[ MIT ]

Kengoro has a new forearm that mimics the human radioulnar joint giving it an even more natural badminton swing.

[ JSK Lab ]

Thanks, Kento!

[ Wallace and Gromit ]

[ Kawasaki ]

[ IceNode ]

[ LANL ]

Looks like Fourier has been working on something new, although this is still at the point of “looks like” rather than something real.

[ Fourier ]

[ Altus Dexterity ]

[ AgileX ]

[ Zoox ]

Traditionally, blind juggling robots use plates that are slightly concave to help them with ball control, but it’s also possible to make a blind juggler the hard way. Which, honestly, is much more impressive.

[ Jugglebot ]