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In the 1960s and 1970s, NASA spent a lot of time thinking about whether toroidal (donut-shaped) fuel tanks were the way to go with its spacecraft. Toroidal tanks have a bunch of potential advantages over conventional spherical fuel tanks. For example, you can fit nearly 40% more volume within a toroidal tank than if you were using multiple spherical tanks within the same space. And perhaps most interestingly, you can shove stuff (like the back of an engine) through the middle of a toroidal tank, which could lead to some substantial efficiency gains if the tanks could also handle structural loads.

Because of their relatively complex shape, toroidal tanks are much more difficult to make than spherical tanks. Even though these tanks can perform better, NASA simply doesn’t have the expertise to manufacture them anymore, since each one has to be hand-built by highly skilled humans. But a company called Machina Labs thinks that they can do this with robots instead. And their vision is to completely change how we make things out of metal.

The fundamental problem that Machina Labs is trying to solve is that if you want to build parts out of metal efficiently at scale, it’s a slow process. Large metal parts need their own custom dies, which are very expensive one-offs that are about as inflexible as it’s possible to get, and then entire factories are built around these parts. It’s a huge investment, which means that it doesn’t matter if you find some new geometry or technique or material or market, because you have to justify that enormous up-front cost by making as much of the original thing as you possibly can, stifling the potential for rapid and flexible innovation.

On the other end of the spectrum you have the also very slow and expensive process of making metal parts one at a time by hand. A few hundred years ago, this was the only way of making metal parts: skilled metalworkers using hand tools for months to make things like armor and weapons. The nice thing about an expert metalworker is that they can use their skills and experience to make anything at all, which is where Machina Labs’ vision comes from, explains CEO Edward Mehr who co-founded Machina Labs after spending time at SpaceX followed by leading the 3D printing team at Relativity Space.

“Craftsmen can pick up different tools and apply them creatively to metal to do all kinds of different things. One day they can pick up a hammer and form a shield out of a sheet of metal,” says Mehr. “Next, they pick up the same hammer, and create a sword out of a metal rod. They’re very flexible.”

The technique that a human metalworker uses to shape metal is called forging, which preserves the grain flow of the metal as it’s worked. Casting, stamping, or milling metal (which are all ways of automating metal part production) are simply not as strong or as durable as parts that are forged, which can be an important differentiator for (say) things that have to go into space. But more on that in a bit.

The problem with human metalworkers is that the throughput is bad—humans are slow, and highly skilled humans in particular don’t scale well. For Mehr and Machina Labs, this is where the robots come in.

“We want to automate and scale using a platform called the ‘robotic craftsman.’ Our core enablers are robots that give us the kinematics of a human craftsman, and artificial intelligence that gives us control over the process,” Mehr says. “The concept is that we can do any process that a human craftsman can do, and actually some that humans can’t do because we can apply more force with better accuracy.”

This flexibility that robot metalworkers offer also enables the crafting of bespoke parts that would be impractical to make in any other way. These include toroidal (donut-shaped) fuel tanks that NASA has had its eye on for the last half century or so.

Machina Labs’ CEO Edward Mehr (on right) stands behind a 15 foot toroidal fuel tank.Machina Labs

“The main challenge of these tanks is that the geometry is complex,” Mehr says. “Sixty years ago, NASA was bump-forming them with very skilled craftspeople, but a lot of them aren’t around anymore.” Mehr explains that the only other way to get that geometry is with dies, but for NASA, getting a die made for a fuel tank that’s necessarily been customized for one single spacecraft would be pretty much impossible to justify. “So one of the main reasons we’re not using toroidal tanks is because it’s just hard to make them.”

Machina Labs is now making toroidal tanks for NASA. For the moment, the robots are just doing the shaping, which is the tough part. Humans then weld the pieces together. But there’s no reason why the robots couldn’t do the entire process end-to-end and even more efficiently. Currently, they’re doing it the “human” way based on existing plans from NASA. “In the future,” Mehr tells us, “we can actually form these tanks in one or two pieces. That’s the next area that we’re exploring with NASA—how can we do things differently now that we don’t need to design around human ergonomics?”

Machina Labs’ ‘robotic craftsmen’ work in pairs to shape sheet metal, with one robot on each side of the sheet. The robots align their tools slightly offset from each other with the metal between them such that as the robots move across the sheet, it bends between the tools. Machina Labs

The video above shows Machina’s robots working on a tank that’s 4.572 m (15 feet) in diameter, likely destined for the Moon. “The main application is for lunar landers,” says Mehr. “The toroidal tanks bring the center of gravity of the vehicle lower than what you would have with spherical or pill-shaped tanks.”

Training these robots to work metal like this is done primarily through physics-based simulations that Machina developed in house (existing software being too slow), followed by human-guided iterations based on the resulting real-world data. The way that metal moves under pressure can be simulated pretty well, and although there’s certainly still a sim-to-real gap (simulating how the robot’s tool adheres to the surface of the material is particularly tricky), the robots are collecting so much empirical data that Machina is making substantial progress towards full autonomy, and even finding ways to improve the process.

An example of the kind of complex metal parts that Machina’s robots are able to make.Machina Labs

Ultimately, Machina wants to use robots to produce all kinds of metal parts. On the commercial side, they’re exploring things like car body panels, offering the option to change how your car looks in geometry rather than just color. The requirement for a couple of beefy robots to make this work means that roboforming is unlikely to become as pervasive as 3D printing, but the broader concept is the same: making physical objects a software problem rather than a hardware problem to enable customization at scale.

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.

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.

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.

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:

The project is designed to leverage the strengths and expertise of each partner equally. The physical capabilities of the new electric Atlas robot, coupled with the ability to programmatically command and teleoperate a broad range of whole-body bimanual manipulation behaviors, will allow research teams to deploy the robot across a range of tasks and collect data on its performance. This data will, in turn, be used to support the training of advanced LBMs, utilizing rigorous hardware and simulation evaluation to demonstrate that large, pre-trained models can enable the rapid acquisition of new robust, dexterous, whole-body skills.

The joint team will also conduct research to answer fundamental training questions for humanoid robots, the ability of research models to leverage whole-body sensing, and understanding human-robot interaction and safety/assurance cases to support these new capabilities.

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.

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