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!

[ DARPA ]

[ LimX Dynamics ]

Thanks, Jinyan!

[ DEEP Robotics ]

The sound in this video is either excellent or terrible, I’m not quite sure which.

[ TU Berlin ]

[ FALCON ]

[ Carnegie Mellon University ]

[ MIT ]

[ Sanctuary AI ]

[ Canvas ] via [ Universal Robots ]

[ Torc ]

This Stanford Robotics Center talk is by Jonathan Hurst at Agility Robotics, on “Humanoid Robots: From the Warehouse to Your House.”

[ Stanford ]

Being long and skinny and wiggly is a strategy that’s been wildly successful for animals, ever since there have been animals, more or less. Roboticists, eternally jealous of biology, have taken notice of this, and have spent decades trying to build robotic versions of snakes, salamanders, worms, and more. There’s been some success, of a sort, although most of the robotic snakes and whatnot that we’ve seen have been for things like disaster relief, which is kind of just what you do when you have a robot with a novel movement strategy but without any other obvious practical application.

Dan Goldman at Georgia Tech has been working on bioinspired robotic locomotion for as long as anyone, and as it turns out, that’s exactly the amount of time that it takes to develop a long and skinny and wiggly robot with a viable commercial use case. Goldman has a new Atlanta-based startup called Ground Control Robotics (GCR) that’s bringing what are essentially giant robotic arthropods to agricultural crop management.

- YouTube

I’m not entirely sure what you’d call this—a robotic giant centipede might be the easiest description to agree on, I guess? But Goldman tells us that he doesn’t consider his robots to be bioinspired as much as they’re “robophysical” models of living systems. “I like the idea of carefully studying the animals,” Goldman says. “We use the models to test biological principles, discover new phenomena with them, and then bring those insights into hardened robots which can go outside of the lab.”

The robot itself is not that complicated, at least on the scale of how complicated robots usually are. It’s made up of a head with some sensors in it plus a handful of identical cable-connected segments, each with a couple of motors for leg actuation. On paper, this works out to be a lot of degrees of freedom, but you can get surprisingly good performance using relatively simple control techniques.

“Centipede robots, like snake robots, are basically swimmers,” Goldman says. The key difference is that adding legs expands the different kinds of environments through which swimming robots can move. The right pattern of lifting and lowering the legs generates a fluidlike thrust force that helps the robot to push off more stuff as it moves to make its motion more consistent and reliable. “We created a new kind of mechanism to take actuation away from the centerline of the robot to the sides, using cables back and forth,” says Goldman. “When you tune things properly, the robot goes from being stiff to unidirectionally compliant. And if you do that, what you find is almost like magic—this thing swims through arbitrarily complex environments with no brain power.”

The complex environments that the robot is designed for are agricultural. Think sensing and weed control in fields, but don’t think about gentle rolling hills lined with neat rows of crops. That kind of farming is very amenable to automation at scale, and there are plenty of robotics companies in that space already. Not all plants grow in well-kept rows on mostly flat ground, however: Perennial crops, where the plant itself sticks around and you harvest stuff off of it every year, can be much more complicated to manage. This is especially true for crops like wine grapes, which can grow on very steep and often rocky slopes. Those kinds of environments are an opportunity for GCR’s robots, offering an initial use case that brings the robot from academic curiosity to something with unique commercial potential.

Wiggly antennae-like structures help the robot to climb over obstacles taller than itself.Ground Control Robotics

“Robotics researchers tend to treat robots as one-off demonstrations of a theory or principle,” Goldman says. “You get the darn thing to work, you submit it to [the International Conference on Robotics and Automation], and then you go onto the next thing. But we’ve had to build in robustness from the get-go, because our robots are experimental physics tools.” Much of the research that Goldman does in his lab is on using these robo-physical models to try to systematically test and (hopefully) understand how animals move the way that they do. “And that’s where we started to see that we could have these robots not just be laboratory toys,” says Goldman, “but that they could become a minimum viable product.”

According to GCR, there is currently no automated solution for weed control around scraggly bushy or vinelike plants (like blueberries or strawberries or grapes), and farmers can spend an enormous amount of money having humans crawl around under the plants to check health and pull weeds. GCR estimates that weed control for blueberries in California can run US $300 per acre or more, and strawberries are even worse, sometimes more than $1,000 per acre. It’s not a fun job, and it’s getting increasingly difficult to find humans willing to do it. For farmers who don’t want to spray pesticides, there aren’t a lot of good options, and GCR thinks that its robotic centipedes could fill that niche.

An obvious question with any novel robotic mobility system is whether you could accomplish basically the same thing with a system that’s much less novel. Like, quadrupeds are getting pretty good these days, why not just use one of them? Or a wheeled robot, for that matter? “We want to send the robot as close to the crops as possible,” says Goldman. “And we don’t want a bigger, clunkier machine to destroy those fields.” This gets back to the clutter problem: A robot large enough to ignore clutter could cause damage, and most robots small enough not to damage clutter become a nightmare of a control problem.

When most of the obstacles that robots encounter are at a comparable scale to themselves, control becomes very difficult. “The terrain reaction forces are almost impossible to predict,” explains Goldman, which means that the robot’s mobility regime gets dominated by environmental noise. One approach would be to try to model all of this noise and the resulting dynamics and implement some kind of control policy, but it turns out that there’s a much simpler strategy: more legs. “It’s possible to generate reliable motion without any sensing at all,” says Goldman, “if we have a lot of legs.”

For this design of robot, adding more legs is easy, which is another advantage of this type of mobility over something like a quadruped. Each of GCR’s robots will cost a lot less than you probably think—likely in the thousand-dollar range, because the leg modules themselves are relatively cheap, and most of the intelligence is mechanical rather than sense-based or compute-based. The concept is that a decentralized swarm of these robots would operate in fields 24/7—just scouting for now, where there’s still a substantial amount of value, and then eventually physically ripping out weeds with some big robotic centipede jaws (or maybe even lasers!) for a lower cost than any other option.

Eventually, these robots will operate autonomously in swarms, and could also be useful for applications like disaster response.Ground Control Robotics

Ground Control Robotics is currently working with a blueberry farmer and a vineyard owner in Georgia on pilot projects to refine the mobility and sensing capabilities of the robots within the next few months. Obviously, there are options to expand into disaster relief (for real) and perhaps even military applications, although Goldman tells us that different environments might require different limb configurations or the ability to tuck the limbs away entirely. I do appreciate that GCR is starting with an application that will likely take a lot more work but also a lot more potential. It’s not often that we get to see such a direct transition between novel robotics research and a commercial product, and while it’s certainly going to be a challenge, I’ve already put my backyard garden on the waiting list.

The main assumption about humanoid robotics that the industry is making right now is that the most realistic near-term pathway to actually making money is in either warehouses or factories. It’s easy to see where this assumption comes from: Repetitive tasks requiring strength or flexibility in well-structured environments is one place where it really seems like robots could thrive, and if you need to make billions of dollars (because somehow that’s how much your company is valued at), it doesn’t appear as though there are a lot of other good options.

Cartwheel Robotics is trying to do something different with humanoids. Cartwheel is more interested in building robots that people can connect with, with the eventual goal of general-purpose home companionship. Founder Scott LaValley describes Cartwheel’s robot as “a small, friendly humanoid robot designed to bring joy, warmth, and a bit of everyday magic into the spaces we live in. It’s expressive, emotionally intelligent, and full of personality—not just a piece of technology but a presence you can feel.”

This rendering shows the design and scale of Cartwheel’s humanoid prototype.Cartwheel

Historically, making a commercially viable social robot is a huge challenge. A little less than a decade ago, a series of social home robots (backed by a substantial amount of investment) tried very, very hard to justify themselves to consumers and did not succeed. Whether the fundamental problems with the concept of social home robots (namely, cost and interactive novelty) have been solved at this point isn’t totally clear, but Cartwheel is making things even more difficult for themselves by going the humanoid route, legs and all. That means dealing with all kinds of problems from motion planning to balancing to safety, all in a way that’s reliable enough for the robot to operate around children.

LaValley is arguably one of the few people who could plausibly make a commercial social humanoid actually happen. His extensive background in humanoid robotics includes nearly a decade at Boston Dynamics working on the Atlas robots, followed by five years at Disney, where he led the team that developed Disney’s Baby Groot robot.

In humanoid robot terms, there’s quite a contrast between the versions of Atlas that LaValley worked on (DRC Atlas in particular) and Baby Groot. They’re obviously designed and built to do very different things, but LaValley says that what really struck him was how his kids reacted when he introduced them to the robots he was working on. “At Boston Dynamics, we were known for terrifying robots,” LaValley remembers. “I was excited to work on the Atlas robots because they were cool technology, but my kids would look at them and go, ‘That’s scary.’ At Disney, I brought my kids in and they would light up with a big smile on their face and ask, ‘Is that really Baby Groot? Can I give it a hug?’ And I thought, this is the type of experience I want to see robots delivering.” While Baby Groot was never a commercial project, for LaValley it marked a pivotal milestone in emotional robotics that shaped his vision for Cartwheel: “Seeing how my kids connected with Baby Groot reframed what robots could and should evoke.”

The current generation of commercial humanoids is pretty much the opposite of what LaValley is looking for. You could argue that this is because they’re designed to do work, rather than be anyone’s friend, but many of the design choices seem to be based on the sort of thing that would be the most eye-catching to the public (and investors) in a rather boringly “futuristic” way. And look, there are plenty of good reasons why you might want to very deliberately design a humanoid with commercial (or at least industrial) aspirations to look or not look a certain way, but for better or worse, nobody is going to like those robots. Respect them? Sure. Think they’re cool? Probably. Want to be friends with them? Not likely. And for Cartwheel, this is the opportunity, LaValley says. “These humanoid robots are built to be tools. They lack personality. They’re soulless. But we’re designing a robot to be a humanoid that humans will want in their day-to-day lives.”

Eventually, Cartwheel’s robots will likely need to be practical (as this rendering suggests) in order to find a place in people’s homes.Cartwheel

Yogi is one of Cartwheel’s prototypes, which LaValley describes as having “toddler proportions,” which are the key to making it appear friendly and approachable. “It has rounded lines, with a big head, and it’s even a little chubby. I don’t see a robot when I see Yogi; I see a character.” A second prototype, called Speedy, is a bit less complicated and is intended to be more of a near-term customizable commercial platform. Think something like Baby Groot, except available as any character you like, and to companies who aren’t Disney. LaValley tells us that a version of Speedy with a special torso designed for a “particular costume” is headed to a customer in the near future.

As the previous generation of social robots learned the hard way, it takes a lot more than good looks for a robot to connect with humans over the long term. Somewhat inevitably, LaValley sees AI as one potential answer to this, since it might offer a way of preserving novelty by keeping interactions fresh. This extends beyond verbal interactions, too, and Cartwheel is experimenting with using AI for whole-body motion generation, where each robot behavior will be unique, even under the same conditions or when given the same inputs.

While Cartwheel is starting with a commercial platform, the end goal is to put these small social humanoids into homes. This means considering safety and affordability in a way that doesn’t really apply to humanoids that are designed to work in warehouses or factories. The small size of Cartwheel’s robots will certainly help with both of those things, but we’re still talking about a robot that’s likely to cost a significant amount—certainly more than a major appliance, although perhaps not as much as a new car, is as much as LaValley was willing to commit to at this point. With that kind of price comes high expectations, and for most people, the only way to justify buying a home humanoid will be if it can somehow be practical as well as lovable.

LaValley is candid about the challenge here: “I don’t have all the answers,” he says. “There’s a lot to figure out.” One approach that’s becoming increasingly common with robots is to go with a service model, where the robot is essentially being rented in the same way that you might pay for the services of a housekeeper or gardener. But again, for that to make sense, Cartwheel’s robots will have to justify themselves financially. “This problem won’t be solved in the next year, or maybe not even in the next five years,” LaValley says. “There are a lot of things we don’t understand—this is going to take a while. We have to work our way to understanding and then addressing the problem set, and our approach is to find development partners and get our robots out into the real world.”

Cartwheel

Cartwheel has been in business for three years now, and got off the ground by providing robotics engineering services to corporate customers. That, along with an initial funding round, allowed LaValley to bootstrap the development of Cartwheel’s own robots, and he expects to deliver a couple dozen variations on Speedy to places like museums and science centers over the next 12 months.

The dream, though, is small home robots that are both companionable and capable, and LaValley is even willing to throw around terms like “general purpose.” “Capability increases over time,” he says, “and maybe our robots will be able to do more than just play with your kids or pick up a few items around the house. I see all robots eventually moving towards general purpose. Our strategy is not to get to general purpose on day one, or even get into the home day one. But we’re working towards that goal. That’s our north star.”

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!

Today I learned that “hippotherapy” is not quite what I wanted it to be.

[ Kuka ]

[ MIT News ]

[ GitHub ] via [ Human Centered Autonomy Lab ]

Thanks, Haonan!

[ EPFL ]

[ Paper ] via [ Singapore University of Technology and Design ]

I like the idea of a humanoid that uses jumping as a primary locomotion mode not because it has to, but because it’s fun.

[ PAL Robotics ]

I had not realized how much nuance there is to digging stuff up with a shovel.

[ Intelligent Motion Laboratory ]

[ Michigan Robotics ]

If you have a humanoid robot and you’re wondering how it should communicate, here’s the answer.

[ Pollen ]

Whose side are you on, Dusty?

Even construction robots should be mindful about siding with the Empire, though there can be consequences!

- YouTube

[ Dusty Robotics ]

This Michigan Robotics Seminar is by Danfei Xu from Georgia Tech, on “Generative Task and Motion Planning.”

[ Michigan Robotics ]

At an event in Dortmund, Germany today, Amazon announced a new robotic system called Vulcan, which the company is calling “its first robotic system with a genuine sense of touch—designed to transform how robots interact with the physical world.” In the short to medium term, the physical world that Amazon is most concerned with is its warehouses, and Vulcan is designed to assist (or take over, depending on your perspective) with stowing and picking items in its mobile robotic inventory system.

In two upcoming papers in IEEE Transactions on Robotics, Amazon researchers describe how both the stowing and picking side of the system operates. We covered stowing in detail a couple of years ago, when we spoke with Aaron Parness, the director of applied science at Amazon Robotics. Parness and his team have made a lot of progress on stowing since then, improving speed and reliability over more than 500,000 stows in operational warehouses to the point where the average stowing robot is now slightly faster than the average stowing human. We spoke with Parness to get an update on stowing, as well as an in-depth look at how Vulcan handles picking, which you can find in this separate article. It’s a much different problem, and well worth a read.

Stowing is the process by which Amazon brings products into its warehouses and adds them to its inventory so that you can order them. Not surprisingly, Amazon has gone to extreme lengths to optimize this process to maximize efficiency in both space and time. Human stowers are presented with a mobile robotic pod full of fabric cubbies (bins) with elastic bands across the front of them to keep stuff from falling out. The human’s job is to find a promising space in a bin, pull the plastic band aside, and stuff the thing into that space. The item’s new home is recorded in Amazon’s system, the pod then drives back into the warehouse, and the next pod comes along, ready for the next item.

Different manipulation tools are used to interact with human-optimized bins.Amazon

The new paper on stowing includes some interesting numbers about Amazon’s inventory-handling process that helps put the scale of the problem in perspective. More than 14 billion items are stowed by hand every year at Amazon warehouses. Amazon is hoping that Vulcan robots will be able to stow 80 percent of these items at a rate of 300 items per hour, while operating 20 hours per day. It’s a very, very high bar.

After a lot of practice, Amazon’s robots are now quite good at the stowing task. Parness tells us that the stow system is operating three times as fast as it was 18 months ago, meaning that it’s actually a little bit faster than an average human. This is exciting, but as Parness explains, expert humans still put the robots to shame. “The fastest humans at this task are like Olympic athletes. They’re far faster than the robots, and they’re able to store items in pods at much higher densities.” High density is important because it means that more stuff can fit into warehouses that are physically closer to more people, which is especially relevant in urban areas where space is at a premium. The best humans can get very creative when it comes to this physical three-dimensional “Tetris-ing,” which the robots are still working on.

Where robots do excel is planning ahead, and this is likely why the average robot stower is now able to outpace the average human stower—Tetris-ing is a mental process, too. In the same way that good Tetris players are thinking about where the next piece is going to go, not just the current piece, robots are able to leverage a lot more information than humans can to optimize what gets stowed where and when, says Parness. “When you’re a person doing this task, you’ve got a buffer of 20 or 30 items, and you’re looking for an opportunity to fit those items into different bins, and having to remember which item might go into which space. But the robot knows all of the properties of all of our items at once, and we can also look at all of the bins at the same time along with the bins in the next couple of pods that are coming up. So we can do this optimization over the whole set of information in 100 milliseconds.”

Essentially, robots are far better at optimization within the planning side of Tetrising, while humans are (still) far better at the manipulation side, but that gap is closing as robots get more experienced at operating in clutter and contact. Amazon has had Vulcan stowing robots operating for over a year in live warehouses in Germany and Washington state to collect training data, and those robots have successfully stowed hundreds of thousands of items.

Stowing is of course only half of what Vulcan is designed to do. Picking offers all kinds of unique challenges too, and you can read our in-depth discussion with Parness on that topic right here.

As far as I can make out, Amazon’s warehouses are highly structured, extremely organized, very tidy, absolute raging messes. Everything in an Amazon warehouse is (usually) exactly where it’s supposed to be, which is typically jammed into some pseudorandom fabric bin the size of a shoebox along with a bunch of other pseudorandom crap. Somehow, this turns out to be the most space- and time-efficient way of doing things, because (as we’ve written about before) you have to consider the process of stowing items away in a warehouse as well as the process of picking them, and that involves some compromises in favor of space and speed.

For humans, this isn’t so much of a problem. When someone orders something on Amazon, a human can root around in those bins, shove some things out of the way, and then pull out the item that they’re looking for. This is exactly the sort of thing that robots tend to be terrible at, because not only is this process slightly different every single time, it’s also very hard to define exactly how humans go about it.

Related: Amazon’s Vulcan Robots Now Stow Items Faster Than Humans

As you might expect, Amazon has been working very very hard on this picking problem. Today at an event in Germany, the company announced Vulcan, a robotic system that can both stow and pick items at human(ish) speeds.

Last time we talked with Aaron Parness, the director of applied science at Amazon Robotics, our conversation was focused on stowing—putting items into bins. As part of today’s announcement, Amazon revealed that its robots are now slightly faster at stowing than the average human is. But in the stow context, there’s a limited amount that a robot really has to understand about what’s actually happening in the bin. Fundamentally, the stowing robot’s job is to squoosh whatever is currently in a bin as far to one side as possible in order to make enough room to cram a new item in. As long as the robot is at least somewhat careful not to crushify anything, it’s a relatively straightforward task, at least compared to picking.

The choices made when an item is stowed into a bin will affect how hard it is to get that item out of that bin later on—this is called “bin etiquette.” Amazon is trying to learn bin etiquette with AI to make picking more efficient.Amazon

The defining problem of picking, as far as robots are concerned, is sensing and manipulation in clutter. “It’s a naturally contact-rich task, and we have to plan on that contact and react to it,” Parness says. And it’s not enough to solve these problems slowly and carefully, because Amazon Robotics is trying to put robots in production, which means that its systems are being directly compared to a not-so-small army of humans who are doing this exact same job very efficiently.

“There’s a new science challenge here, which is to identify the right item,” explains Parness. The thing to understand about identifying items in an Amazon warehouse is that there are a lot of them: something like 400 million unique items. One single floor of an Amazon warehouse can easily contain 15,000 pods, which is over a million bins, and Amazon has several hundred warehouses. This is a lot of stuff.

In theory, Amazon knows exactly which items are in every single bin. Amazon also knows (again, in theory), the weight and dimensions of each of those items, and probably has some pictures of each item from previous times that the item has been stowed or picked. This is a great starting point for item identification, but as Parness points out, “We have lots of items that aren’t feature rich—imagine all of the different things you might get in a brown cardboard box.”

As challenging as it is to correctly identify an item in a bin that may be stuffed to the brim with nearly identical items, an even bigger challenge is actually getting that item that you just identified out of the bin. The hardware and software that humans have for doing this task is unmatched by any robot, which is always a problem, but the real complicating factor is dealing with items that are all jumbled together in a small fabric bin. And the picking process itself involves more than just extraction—once the item is out of the bin, you then have to get it to the next order-fulfillment step, which means dropping it into another bin or putting it on a conveyor or something.

“When we were originally starting out, we assumed we’d have to carry the item over some distance after we pulled it out of the bin,” explains Parness. “So we were thinking we needed pinch grasping.” A pinch grasp is when you grab something between a finger (or fingers) and your thumb, and at least for humans, it’s a versatile and reliable way of grabbing a wide variety of stuff. But as Parness notes, for robots in this context, it’s more complicated: “Even pinch grasping is not ideal because if you pinch the edge of a book, or the end of a plastic bag with something inside it, you don’t have pose control of the item and it may flop around unpredictably.”

At some point, Parness and his team realized that while an item did have to move farther than just out of the bin, it didn’t actually have to get moved by the picking robot itself. Instead, they came up with a lifting conveyor that positions itself directly outside of the bin being picked from, so that all the robot has to do is get the item out of the bin and onto the conveyor. “It doesn’t look that graceful right now,” admits Parness, but it’s a clever use of hardware to substantially simplify the manipulation problem, and has the side benefit of allowing the robot to work more efficiently, since the conveyor can move the item along while the arm starts working on the next pick.

Amazon’s robots have different techniques for extracting items from bins, using different gripping hardware depending on what needs to be picked. The type of end effector that the system chooses and the grasping approach depend on what the item is, where it is in the bin, and also what it’s next to. It’s a complicated planning problem that Amazon is tackling with AI, as Parness explains. “We’re starting to build foundation models of items, including properties like how squishy they are, how fragile they are, and whether they tend to get stuck on other items or no. So we’re trying to learn those things, and it’s early stage for us, but we think reasoning about item properties is going to be important to get to that level of reliability that we need.”

Reliability has to be superhigh for Amazon (and with many other commercial robotic deployments) simply because small errors multiplied over huge deployments result in an unacceptable amount of screwing up. There’s a very, very long tail of unusual things that Amazon’s robots might encounter when trying to extract an item from a bin. Even if there’s some particularly weird bin situation that might only show up once in a million picks, that still ends up happening many times per day on the scale at which Amazon operates. Fortunately for Amazon, they’ve got humans around, and part of the reason that this robotic system can be effective in production at all is that if the robot gets stuck, or even just sees a bin that it knows is likely to cause problems, it can just give up, route that particular item to a human picker, and move on to the next one.

The other new technique that Amazon is implementing is a sort of modern approach to “visual servoing,” where the robot watches itself move and then adjusts its movement based on what it sees. As Parness explains: “It’s an important capability because it allows us to catch problems before they happen. I think that’s probably our biggest innovation, and it spans not just our problem, but problems across robotics.”

Parness was very clear that (for better or worse) Amazon isn’t thinking about its stowing and picking robots in terms of replacing humans completely. There’s that long tail of items that need a human touch, and it’s frankly hard to imagine any robotic-manipulation system capable enough to make at least occasional human help unnecessary in an environment like an Amazon warehouse, which somehow manages to maximize organization and chaos at the same time.

These stowing and picking robots have been undergoing live testing in an Amazon warehouse in Germany for the past year, where they’re already demonstrating ways in which human workers could directly benefit from their presence. For example, Amazon pods can be up to 2.5 meters tall, meaning that human workers need to use a stepladder to reach the highest bins and bend down to reach the lowest ones. If the robots were primarily tasked with interacting with these bins, it would help humans work faster while putting less stress on their bodies.

With the robots so far managing to keep up with human workers, Parness tells us that the emphasis going forward will be primarily on getting better at not screwing up: “I think our speed is in a really good spot. The thing we’re focused on now is getting that last bit of reliability, and that will be our next year of work.” While it may seem like Amazon is optimizing for its own very specific use cases, Parness reiterates that the bigger picture here is using every last one of those 400 million items jumbled into bins as a unique opportunity to do fundamental research on fast, reliable manipulation in complex environments.

“If you can build the science to handle high contact and high clutter, we’re going to use it everywhere,” says Parness. “It’s going to be useful for everything, from warehouses to your own home. What we’re working on now are just the first problems that are forcing us to develop these capabilities, but I think it’s the future of robotic manipulation.”

As far as I can make out, Amazon’s warehouses are highly structured, extremely organized, very tidy, absolute raging messes. Everything in an Amazon warehouse is (usually) exactly where it’s supposed to be, which is typically jammed into some pseudorandom fabric bin the size of a shoebox along with a bunch of other pseudorandom crap. Somehow, this turns out to be the most space- and time-efficient way of doing things, because (as we’ve written about before) you have to consider the process of stowing items away in a warehouse as well as the process of picking them, and that involves some compromises in favor of space and speed.

For humans, this isn’t so much of a problem. When someone orders something on Amazon, a human can root around in those bins, shove some things out of the way, and then pull out the item that they’re looking for. This is exactly the sort of thing that robots tend to be terrible at, because not only is this process slightly different every single time, it’s also very hard to define exactly how humans go about it.

As you might expect, Amazon has been working very very hard on this picking problem. Today at an event in Germany, the company announced Vulcan, a robotic system that can both stow and pick items at human(ish) speeds.

Last time we talked with Aaron Parness, the director of applied science at Amazon Robotics, our conversation was focused on stowing—putting items into bins. As part of today’s announcement, Amazon revealed that its robots are now slightly faster at stowing than the average human is. But in the stow context, there’s a limited amount that a robot really has to understand about what’s actually happening in the bin. Fundamentally, the stowing robot’s job is to squoosh whatever is currently in a bin as far to one side as possible in order to make enough room to cram a new item in. As long as the robot is at least somewhat careful not to crushify anything, it’s a relatively straightforward task, at least compared to picking.

The choices made when an item is stowed into a bin will affect how hard it is to get that item out of that bin later on—this is called “bin etiquette.” Amazon is trying to learn bin etiquette with AI to make picking more efficient.Amazon

The defining problem of picking, as far as robots are concerned, is sensing and manipulation in clutter. “It’s a naturally contact-rich task, and we have to plan on that contact and react to it,” Parness says. And it’s not enough to solve these problems slowly and carefully, because Amazon Robotics is trying to put robots in production, which means that its systems are being directly compared to a not-so-small army of humans who are doing this exact same job very efficiently.

“There’s a new science challenge here, which is to identify the right item,” explains Parness. The thing to understand about identifying items in an Amazon warehouse is that there are a lot of them: something like 400 million unique items. One single floor of an Amazon warehouse can easily contain 15,000 pods, which is over a million bins, and Amazon has several hundred warehouses. This is a lot of stuff.

In theory, Amazon knows exactly which items are in every single bin. Amazon also knows (again, in theory), the weight and dimensions of each of those items, and probably has some pictures of each item from previous times that the item has been stowed or picked. This is a great starting point for item identification, but as Parness points out, “We have lots of items that aren’t feature rich—imagine all of the different things you might get in a brown cardboard box.”

As challenging as it is to correctly identify an item in a bin that may be stuffed to the brim with nearly identical items, an even bigger challenge is actually getting that item that you just identified out of the bin. The hardware and software that humans have for doing this task is unmatched by any robot, which is always a problem, but the real complicating factor is dealing with items that are all jumbled together in a small fabric bin. And the picking process itself involves more than just extraction—once the item is out of the bin, you then have to get it to the next order-fulfillment step, which means dropping it into another bin or putting it on a conveyor or something.

“When we were originally starting out, we assumed we’d have to carry the item over some distance after we pulled it out of the bin,” explains Parness. “So we were thinking we needed pinch grasping.” A pinch grasp is when you grab something between a finger (or fingers) and your thumb, and at least for humans, it’s a versatile and reliable way of grabbing a wide variety of stuff. But as Parness notes, for robots in this context, it’s more complicated: “Even pinch grasping is not ideal because if you pinch the edge of a book, or the end of a plastic bag with something inside it, you don’t have pose control of the item and it may flop around unpredictably.”

At some point, Parness and his team realized that while an item did have to move farther than just out of the bin, it didn’t actually have to get moved by the picking robot itself. Instead, they came up with a lifting conveyor that positions itself directly outside of the bin being picked from, so that all the robot has to do is get the item out of the bin and onto the conveyor. “It doesn’t look that graceful right now,” admits Parness, but it’s a clever use of hardware to substantially simplify the manipulation problem, and has the side benefit of allowing the robot to work more efficiently, since the conveyor can move the item along while the arm starts working on the next pick.

Amazon’s robots have different techniques for extracting items from bins, using different gripping hardware depending on what needs to be picked. The type of end effector that the system chooses and the grasping approach depend on what the item is, where it is in the bin, and also what it’s next to. It’s a complicated planning problem that Amazon is tackling with AI, as Parness explains. “We’re starting to build foundation models of items, including properties like how squishy they are, how fragile they are, and whether they tend to get stuck on other items or no. So we’re trying to learn those things, and it’s early stage for us, but we think reasoning about item properties is going to be important to get to that level of reliability that we need.”

Reliability has to be superhigh for Amazon (and with many other commercial robotic deployments) simply because small errors multiplied over huge deployments result in an unacceptable amount of screwing up. There’s a very, very long tail of unusual things that Amazon’s robots might encounter when trying to extract an item from a bin. Even if there’s some particularly weird bin situation that might only show up once in a million picks, that still ends up happening many times per day on the scale at which Amazon operates. Fortunately for Amazon, they’ve got humans around, and part of the reason that this robotic system can be effective in production at all is that if the robot gets stuck, or even just sees a bin that it knows is likely to cause problems, it can just give up, route that particular item to a human picker, and move on to the next one.

The other new technique that Amazon is implementing is a sort of modern approach to “visual servoing,” where the robot watches itself move and then adjusts its movement based on what it sees. As Parness explains: “It’s an important capability because it allows us to catch problems before they happen. I think that’s probably our biggest innovation, and it spans not just our problem, but problems across robotics.”

Parness was very clear that (for better or worse) Amazon isn’t thinking about its stowing and picking robots in terms of replacing humans completely. There’s that long tail of items that need a human touch, and it’s frankly hard to imagine any robotic-manipulation system capable enough to make at least occasional human help unnecessary in an environment like an Amazon warehouse, which somehow manages to maximize organization and chaos at the same time.

These stowing and picking robots have been undergoing live testing in an Amazon warehouse in Germany for the past year, where they’re already demonstrating ways in which human workers could directly benefit from their presence. For example, Amazon pods can be up to 2.5 meters tall, meaning that human workers need to use a stepladder to reach the highest bins and bend down to reach the lowest ones. If the robots were primarily tasked with interacting with these bins, it would help humans work faster while putting less stress on their bodies.

With the robots so far managing to keep up with human workers, Parness tells us that the emphasis going forward will be primarily on getting better at not screwing up: “I think our speed is in a really good spot. The thing we’re focused on now is getting that last bit of reliability, and that will be our next year of work.” While it may seem like Amazon is optimizing for its own very specific use cases, Parness reiterates that the bigger picture here is using every last one of those 400 million items jumbled into bins as a unique opportunity to do fundamental research on fast, reliable manipulation in complex environments.

“If you can build the science to handle high contact and high clutter, we’re going to use it everywhere,” says Parness. “It’s going to be useful for everything, from warehouses to your own home. What we’re working on now are just the first problems that are forcing us to develop these capabilities, but I think it’s the future of robotic manipulation.”

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!

[ DEEP Robotics ]

The latest OK Go music video includes lots of robots.

And here’s a bit more on how it was done, mostly with arms from Universal Robots.

[ OK Go ]

[ Berkeley Humanoid Lite ]

I think this may be the first time I’ve ever seen a pedestal-mounted Atlas from Boston Dynamics.

[ NVIDIA ]

[ University of Bath ]

Whenever I see a soft robot, I have to ask, “Okay, but how soft is it really?” And usually, there’s a pump or something hidden away off-camera somewhere. So it’s always cool to see actually soft robotics actuators, like these, which are based on phase-changing water.

[ Nature Communications ] via [ Collaborative Robotics Laboratory, University of Coimbra ]

Thanks, Pedro!

[ Paper ] via [ IEEE Robotics and Automation Magazine ]

Thanks, Bram!

[ Waymo ]

[ Robotics & Mechanisms Laboratory ]

[ PNDbotics ]

[ Burger Bots ]

Look at this little guy, such a jaunty walk!

[ Science Advances ]

[ Hybrid Robotics ]

It’s debatable whether this is technically a robot, but sure, let’s go with it, because it’s pretty neat—a cable car of sorts consisting of a soft twisted ring that’s powered by infrared light.

[ North Carolina State University ]

[ Bloomberg ]

[ University of Pennsylvania ]

I come from dairy-farming stock. My grandfather, the original Harry Goldstein, owned a herd of dairy cows and a creamery in Louisville, Ky., that bore the family name. One fateful day in early April 1944, Harry was milking his cows when a heavy metallic part of his homemade milking contraption—likely some version of the then-popular Surge Bucket Milker—struck him in the abdomen, causing a blood clot that ultimately led to cardiac arrest and his subsequent demise a few days later, at the age of 48.

Fast forward 80 years and dairy farming is still a dangerous occupation. According to an analysis of U.S. Bureau of Labor Statistics data done by the advocacy group Farmworker Justice, the U.S. dairy industry recorded 223 injuries per 10,000 full-time workers in 2020, almost double the rate for all of private industry combined. Contact with animals tops the list of occupational hazards for dairy workers, followed by slips, trips, and falls. Other significant risks include contact with objects or equipment, overexertion, and exposure to toxic substances. Every year, a few dozen dairy workers in the United States meet a fate similar to my grandfather’s, with 31 reported deadly accidents on dairy farms in 2021.

As Senior Editor Evan Ackerman notes in “Robots for Cows (and Their Humans)”, traditional dairy farming is very labor-intensive. Cows need to be milked at least twice per day to prevent discomfort. Conventional milking facilities are engineered for human efficiency, with systems like rotating carousels that bring the cows to the dairy workers.

The robotic systems that Netherlands-based Lely has been developing since the early 1990s are much more about doing things the bovine way. That includes letting the cows choose when to visit the milking robot, resulting in a happier herd and up to 10 percent more milk production.

Turns out that what’s good for the cows might be good for the humans, too. Another Lely bot deals with feeding, while yet another mops up the manure, the proximate cause of much of the slipping and sliding that can result in injuries. The robots tend to reset the cow–human relationship—it becomes less adversarial because the humans aren’t always there bossing the cows around.

Farmer well-being is also enhanced because the humans don’t have to be around to tempt fate, and they can spend time doing other things, freed up by the robot laborers. In fact, when Ackerman visited Lely’s demonstration farm in Schipluiden, Netherlands, to see the Lely robots in action, he says, “The original plan was for me to interview the farmer, and he was just not there at all for the entire visit while the cows were getting milked by the robots. In retrospect, that might have been the most effective way he could communicate how these robots are changing work for dairy farmers.”

The farmer’s absence also speaks volumes about how far dairy technology has evolved since my grandfather’s day. Harry Goldstein’s life was cut short by the very equipment he hacked to make his own work easier. Today’s dairy-farming innovations aren’t just improving efficiency—they’re keeping humans out of harm’s way entirely. In the dairy farms of the future, the most valuable safety features might simply be a barn resounding with the whirring of robots and moos of contentment.

Meet FREDERICK Mark 2, the Friendly Robot for Education, Discussion and Entertainment, the Retrieval of Information, and the Collation of Knowledge, better known as Freddy II. This remarkable robot could put together a simple model car from an assortment of parts dumped in its workspace. Its video-camera eyes and pincer hand identified and sorted the individual pieces before assembling the desired end product. But onlookers had to be patient. Assembly took about 16 hours, and that was after a day or two of “learning” and programming.

Freddy II was completed in 1973 as one of a series of research robots developed by Donald Michie and his team at the University of Edinburgh during the 1960s and ’70s. The robots became the focus of an intense debate over the future of AI in the United Kingdom. Michie eventually lost, his funding was gutted, and the ensuing AI winter set back U.K. research in the field for a decade.

In 1967, Donald Michie, along with Richard Gregory and Hugh Christopher Longuet-Higgins, founded the Department of Machine Intelligence and Perception at the University of Edinburgh with the near-term goal of developing a semiautomated robot and then longer-term vision of programming “integrated cognitive systems,” or what other people might call intelligent robots. At the time, the U.S. Defense Advanced Research Projects Agency and Japan’s Computer Usage Development Institute were both considering plans to create fully automated factories within a decade. The team at Edinburgh thought they should get in on the action too.

Two years later, Stephen Salter and Harry G. Barrow joined Michie and got to work on Freddy I. Salter devised the hardware while Barrow designed and wrote the software and computer interfacing. The resulting simple robot worked, but it was crude. The AI researcher Jean Hayes (who would marry Michie in 1971) referred to this iteration of Freddy as an “arthritic Lady of Shalott.”

Freddy I consisted of a robotic arm, a camera, a set of wheels, and some bumpers to detect obstacles. Instead of roaming freely, it remained stationary while a small platform moved beneath it. Barrow developed an adaptable program that enabled Freddy I to recognize irregular objects. In 1969, Salter and Barrow published in Machine Intelligence their results, “Design of Low-Cost Equipment for Cognitive Robot Research,” which included suggestions for the next iteration of the robot.

Freddy I, completed in 1969, could recognize objects placed in front of it—in this case, a teacup.University of Edinburgh

More people joined the team to build Freddy Mark 1.5, which they finished in May 1971. Freddy 1.5 was a true robotic hand-eye system. The hand consisted of two vertical, parallel plates that could grip an object and lift it off the platform. The eyes were two cameras: one looking directly down on the platform, and the other mounted obliquely on the truss that suspended the hand over the platform. Freddy 1.5’s world was a 2-meter by 2-meter square platform that moved in an x-y plane.

Freddy 1.5 quickly morphed into Freddy II as the team continued to grow. Improvements included force transducers added to the “wrist” that could deduce the strength of the grip, the weight of the object held, and whether it had collided with an object. But what really set Freddy II apart was its versatile assembly program: The robot could be taught to recognize the shapes of various parts, and then after a day or two of programming, it could assemble simple models. The various steps can be seen in this extended video, narrated by Barrow:

And then what happened? So much. But before I get into all that, let me just say that rarely do I, as a historian, have the luxury of having my subjects clearly articulate the aims of their projects, imagine the future, and then, years later, reflect on their experiences. As a cherry on top of this historian’s delight, the topic at hand—artificial intelligence—also happens to be of current interest to pretty much everyone.

As with many fascinating histories of technology, events turn on a healthy dose of professional bickering. In this case, the disputants were Michie and the applied mathematician James Lighthill, who had drastically different ideas about the direction of robotics research. Lighthill favored applied research, while Michie was more interested in the theoretical and experimental possibilities. Their fight escalated quickly, became public with a televised debate on the BBC, and concluded with the demise of an entire research field in Britain.

A damning report in 1973 by applied mathematician James Lighthill [left] resulted in funding being pulled from the AI and robotics program led by Donald Michie [right]. Left: Chronicle/Alamy; Right: University of Edinburgh

It all started in September 1971, when the British Science Research Council, which distributed public funds for scientific research, commissioned Lighthill to survey the state of academic research in artificial intelligence. The SRC was finding it difficult to make informed funding decisions in AI, given the field’s complexity. It suspected that some AI researchers’ interests were too narrowly focused, while others might be outright charlatans. Lighthill was called in to give the SRC a road map.

No intellectual slouch, Lighthill was the Lucasian Professor of Mathematics at the University of Cambridge, a position also held by Isaac Newton, Charles Babbage, and Stephen Hawking. Lighthill solicited input from scholars in the field and completed his report in March 1972. Officially titled “ Artificial Intelligence: A General Survey,” but informally called the Lighthill Report, it divided AI into three broad categories: A, for advanced automation; B, for building robots, but also bridge activities between categories A and C; and C, for computer-based central nervous system research. Lighthill acknowledged some progress in categories A and C, as well as a few disappointments.

Lighthill viewed Category B, though, as a complete failure. “Progress in category B has been even slower and more discouraging,” he wrote, “tending to sap confidence in whether the field of research called AI has any true coherence.” For good measure, he added, “AI not only fails to take the first fence but ignores the rest of the steeplechase altogether.” So very British.

Lighthill concluded his report with his view of the next 25 years in AI. He predicted a “fission of the field of AI research,” with some tempered optimism for achievement in categories A and C but a valley of continued failures in category B. Success would come in fields with clear applications, he argued, but basic research was a lost cause.

The Science Research Council published Lighthill’s report the following year, with responses from N. Stuart Sutherland of the University of Sussex and Roger M. Needham of the University of Cambridge, as well as Michie and his colleague Longuet-Higgins.

Sutherland sought to relabel category B as “basic research in AI” and to have the SRC increase funding for it. Needham mostly supported Lighthill’s conclusions and called for the elimination of the term AI—“a rather pernicious label to attach to a very mixed bunch of activities, and one could argue that the sooner we forget it the better.”

Longuet-Higgins focused on his own area of interest, cognitive science, and ended with an ominous warning that any spin-off of advanced automation would be “more likely to inflict multiple injuries on human society,” but he didn’t explain what those might be.

Michie, as the United Kingdom’s academic leader in robots and machine intelligence, understandably saw the Lighthill Report as a direct attack on his research agenda. With his funding at stake, he provided the most critical response, questioning the very foundation of the survey: Did Lighthill talk with any international experts? How did he overcome his own biases? Did he have any sources and references that others could check? He ended with a request for more funding—specifically the purchase of a DEC System 10 (also known as the PDP-10) mainframe computer. According to Michie, if his plan were followed, Britain would be internationally competitive in AI by the end of the decade.

After Michie’s funding was cut, the many researchers affiliated with his bustling lab lost their jobs.University of Edinburgh

This whole affair might have remained an academic dispute, but then the BBC decided to include a debate between Lighthill and a panel of experts as part of its “Controversy” TV series. “Controversy” was an experiment to engage the public in science. On 9 May 1973, an interested but nonspecialist audience filled the auditorium at the Royal Institution in London to hear the debate.

Lighthill started with a review of his report, explaining the differences he saw between automation and what he called “the mirage” of general-purpose robots. Michie responded with a short film of Freddy II assembling a model, explaining how the robot processes information. Michie argued that AI is a subject with its own purposes, its own criteria, and its own professional standards.

After a brief back and forth between Lighthill and Michie, the show’s host turned to the other panelists: John McCarthy, a professor of computer science at Stanford University, and Richard Gregory, a professor in the department of anatomy at the University of Bristol who had been Michie’s colleague at Edinburgh. McCarthy, who coined the term artificial intelligence in 1955, supported Michie’s position that AI should be its own area of research, not simply a bridge between automation and a robot that mimics a human brain. Gregory described how the work of Michie and McCarthy had influenced the field of psychology.

You can watch the debate or read a transcript.

Despite international support from the AI community, though, the SRC sided with Lighthill and gutted funding for AI and robotics; Michie had lost. Michie’s bustling lab went from being an international center of research to just Michie, a technician, and an administrative assistant. The loss ushered in the first British AI winter, with the United Kingdom making little progress in the field for a decade.

For his part, Michie pivoted and recovered. He decommissioned Freddy II in 1980, at which point it moved to the Royal Museum of Scotland (now the National Museum of Scotland), and he replaced it with a Unimation PUMA robot.

In 1983, Michie founded the Turing Institute in Glasgow, an AI lab that worked with industry on both basic and applied research. The year before, he had written Machine Intelligence and Related Topics: An Information Scientist’s Weekend Book (Gordon and Breach). Michie intended it as intellectual musings that he hoped scientists would read, perhaps on the weekend, to help them get beyond the pursuits of the workweek. The book is wide-ranging, covering his three decades of work.

In the introduction to the chapters covering Freddy and the aftermath of the Lighthill report, Michie wrote, perhaps with an eye toward history:

History has indeed rendered judgment on the debate and the Lighthill Report. In 2019, for example, computer scientist Maarten van Emden, a colleague of Michie’s, reflected on the demise of the Freddy project with these choice words for Lighthill: “a pompous idiot who lent himself to produce a flaky report to serve as a blatantly inadequate cover for a hatchet job.”

And in a March 2024 post on GitHub, the blockchain entrepreneur Jeffrey Emanuel thoughtfully dissected Lighthill’s comments and the debate itself. Of Lighthill, he wrote, “I think we can all learn a very valuable lesson from this episode about the dangers of overconfidence and the importance of keeping an open mind. The fact that such a brilliant and learned person could be so confidently wrong about something so important should give us pause.”

Arguably, both Lighthill and Michie correctly predicted certain aspects of the AI future while failing to anticipate others. On the surface, the report and the debate could be described as simply about funding. But it was also more fundamentally about the role of academic research in shaping science and engineering and, by extension, society. Ideally, universities can support both applied research and more theoretical work. When funds are limited, though, choices are made. Lighthill chose applied automation as the future, leaving research in AI and machine intelligence in the cold.

It helps to take the long view. Over the decades, AI research has cycled through several periods of spring and winter, boom and bust. We’re currently in another AI boom. Is this time different? No one can be certain what lies just over the horizon, of course. That very uncertainty is, I think, the best argument for supporting people to experiment and conduct research into fundamental questions, so that they may help all of us to dream up the next big thing.

Part of a continuing series looking at historical artifacts that embrace the boundless potential of technology.

An abridged version of this article appears in the May 2025 print issue as “This Robot Was the Fall Guy for British AI.”

Donald Michie’s lab regularly published articles on the group’s progress, especially in Machine Intelligence, a journal founded by Michie.

The Lighthill Report and recordings of the debate are both available in their entirety online—primary sources that capture the intensity of the moment.

In 2009, a group of alumni from Michie’s Edinburgh lab, including Harry Barrow and Pat Fothergill (formerly Ambler), created a website to share their memories of working on Freddy. The site offers great firsthand accounts of the development of the robot. Unfortunately for the historian, they didn’t explore the lasting effects of the experience. A decade later, though, Maarten van Emden did, in his 2019 article “Reflecting Back on the Lighthill Affair,” in the IEEE Annals of the History of Computing.

Beyond his academic articles, Michie was a prolific author. Two collections of essays I found particularly useful are On Machine Intelligence (John Wiley & Sons, 1974) and Machine Intelligence and Related Topics: An Information Scientist’s Weekend Book (Gordon and Breach, 1982).

Jon Agar’s 2020 article “What Is Science for? The Lighthill Report on Artificial Intelligence Reinterpreted” and Jeffrey Emanuel’s GitHub post offer historical interpretations on this mostly forgotten blip in the history of robotics and artificial intelligence.

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!

[ LEVA ]

You’ve probably heard about this humanoid robot half-marathon in China, because it got a lot of media attention, which I presume was the goal. And for those of us who remember when Asimo running was a big deal, marathon running is still impressive in some sense. It’s just hard to connect that to these robots doing anything practical, you know?

[ NBC ]

[ LRN ]

On Kickstarter now, for an absurdly cheap US $80.

[ Kickstarter ]

Thanks, Rich!

HEBI robots demonstrated inchworm mobility during the Innovation Faire of the FIRST Robotics World Championships in Houston.

[ HEBI ]

Thanks, Andrew!

Happy Easter from Flexiv!

[ Flexiv ]

[ PNDbotics ]

[ DAIR Lab ]

I will never understand why robotics companies feel the need to add the sounds of sick actuators when their robots move.

[ Kepler ]

[ Trossen ]

This week’s Carnegie Mellon University Robotics Institute (CMU RI) seminar is a retro edition (2008!) from Charlie Kemp, previously of the Healthcare Robotics Lab at Georgia Tech and now at Hello Robot.

[ CMU RI ]

This week’s actual CMU RI seminar is from a much more modern version of Charlie Kemp.

[ CMU RI ]

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!

[ Unitree ]

[ TU Delft ]

RAI’s Ultra Mobile Vehicle (UMV) is learning some new tricks!

[ RAI Institute ]

[ RoMeLa ]

We wrote about Parallel Systems back in January of 2022, and it’s good to see that their creative take on autonomous rail is still moving along.

[ Parallel Systems ]

[ EPFL ]

[ ROBOTERA ]

[ Extend Robotics ]

I kind of like the idea of having a mobile robot that lives in my garage and manages the charging and cleaning of my car.

[ Flexiv ]

[ DARPA ]

[ 1X ] via [ TED ]

Rodney Brooks gave a keynote at the Stanford HAI spring conference on Robotics in a Human-Centered World.

There are a bunch of excellent talks from this conference on YouTube at the link below, but I think this panel is especially good, as a discussion of going from from research to real-world impact.

[ YouTube ] via [ Stanford HAI ]

Wing CEO Adam Woodworth discusses consumer drone delivery with Peter Diamandis at Abundance 360.

[ Wing ]

This CMU RI Seminar is from Sangbae Kim, who was until very recently at MIT but is now the Robotics Architect at Meta’s Robotics Studio.

[ CMU RI ]

This is a sponsored article brought to you by Amazon.

The cutting edge of robotics and artificial intelligence (AI) doesn’t occur just at NASA, or one of the top university labs, but instead is increasingly being developed in the warehouses of the e-commerce company Amazon. As online shopping continues to grow, companies like Amazon are pushing the boundaries of these technologies to meet consumer expectations.

Warehouses, the backbone of the global supply chain, are undergoing a transformation driven by technological innovation. Amazon, at the forefront of this revolution, is leveraging robotics and AI to shape the warehouses of the future. Far from being just a logistics organization, Amazon is positioning itself as a leader in technological innovation, making it a prime destination for engineers and scientists seeking to shape the future of automation.

Amazon’s success in e-commerce is built on a foundation of continuous technological innovation. Its fulfillment centers are increasingly becoming hubs of cutting-edge technology where robotics and AI play a pivotal role. Heath Ruder, Director of Product Management at Amazon, explains how Amazon’s approach to integrating robotics with advanced material handling equipment is shaping the future of its warehouses.

“We’re integrating several large-scale products into our next-generation fulfillment center in Shreveport, Louisiana,” says Ruder. “It’s our first opportunity to get our robotics systems combined under one roof and understand the end-to-end mechanics of how a building can run with incorporated autonomation.” Ruder is referring to the facility’s deployment of its Automated Storage and Retrieval Systems (ASRS), called Sequoia, as well as robotic arms like “Robin” and “Cardinal” and Amazon’s proprietary autonomous mobile robot, “Proteus”.

Amazon has already deployed “Robin”, a robotic arm that sorts packages for outbound shipping by transferring packages from conveyors to mobile robots. This system is already in use across various Amazon fulfillment centers and has completed over three billion successful package moves. “Cardinal” is another robotic arm system that efficiently packs packages into carts before the carts are loaded onto delivery trucks.

“Proteus” is Amazon’s autonomous mobile robot designed to work around people. Unlike traditional robots confined to a restricted area, Proteus is fully autonomous and navigates through fulfillment centers using sensors and a mix of AI-based and ML systems. It works with human workers and other robots to transport carts full of packages more efficiently.

The integration of these technologies is estimated to increase operational efficiency by 25 percent. “Our goal is to improve speed, quality, and cost. The efficiency gains we’re seeing from these systems are substantial,” says Ruder. However, the real challenge is scaling this technology across Amazon’s global network of fulfillment centers. “Shreveport was our testing ground and we are excited about what we have learned and will apply at our next building launching in 2025.”

Amazon’s investment in cutting-edge robotics and AI systems is not just about operational efficiency. It underscores the company’s commitment to being a leader in technological innovation and workplace safety, making it a top destination for engineers and scientists looking to solve complex, real-world problems.

One of the most complex challenges Amazon’s robotics team faces is how to make robots capable of handling a wide variety of tasks that require discernment. Mike Wolf, a principal scientist at Amazon Robotics, plays a key role in developing AI models that enable robots to better manipulate objects, across a nearly infinite variety of scenarios.

“The complexity of Amazon’s product catalog—hundreds of millions of unique items—demands advanced AI systems that can make real-time decisions about object handling,” explains Wolf. But how do these AI systems learn to handle such an immense variety of objects? Wolf’s team is developing machine learning algorithms that enable robots to learn from experience.

“We’re developing the next generation of AI and robotics. For anyone interested in this field, Amazon is the place where you can make a difference on a global scale.” —Mike Wolf, Amazon Robotics

In fact, robots at Amazon continuously gather data from their interactions with objects, refining their ability to predict how items will be affected when manipulated. Every interaction a robot has—whether it’s picking up a package or placing it into a container—feeds back into the system, refining the AI model and helping the robot to improve. “AI is continually learning from failure cases,” says Wolf. “Every time a robot fails to complete a task successfully, that’s actually an opportunity for the system to learn and improve.” This data-centric approach supports the development of state-of-the-art AI systems that can perform increasingly complex tasks, such as predicting how objects are affected when manipulated. This predictive ability will help robots determine the best way to pack irregularly shaped objects into containers or handle fragile items without damaging them.

“We want AI that understands the physics of the environment, not just basic object recognition. The goal is to predict how objects will move and interact with one another in real time,” Wolf says.

Valerie Samzun, Senior Technical Product Manager at Amazon, leads a cutting-edge robotics program that aims to enhance workplace safety and make jobs more rewarding, fulfilling, and intellectually stimulating by allowing robots to handle repetitive tasks.

“The goal is to reduce certain repetitive and physically demanding tasks from associates,” explains Samzun. “This allows them to focus on higher-value tasks in skilled roles.” This shift not only makes warehouse operations more efficient but also opens up new opportunities for workers to advance their careers by developing new technical skills.

“Our research combines several cutting-edge technologies,” Samzun shared. “The project uses robotic arms equipped with compliant manipulation tools to detect the amount of force needed to move items without damaging them or other items.” This is an advancement that incorporates learnings from previous Amazon robotics projects. “This approach allows our robots to understand how to interact with different objects in a way that’s safe and efficient,” says Samzun. In addition to robotic manipulation, the project relies heavily on AI-driven algorithms that determine the best way to handle items and utilize space.

Samzun believes the technology will eventually expand to other parts of Amazon’s operations, finding multiple applications across its vast network. “The potential applications for compliant manipulation are huge,” she says.

As Amazon continues to push the boundaries of what’s possible with robotics and AI, it’s also becoming a highly attractive destination for engineers, scientists, and technical professionals. Both Wolf and Samzun emphasize the unique opportunities Amazon offers to those interested in solving real-world problems at scale.

For Wolf, who transitioned to Amazon from NASA’s Jet Propulsion Laboratory, the appeal lies in the sheer impact of the work. “The draw of Amazon is the ability to see your work deployed at scale. There’s no other place in the world where you can see your robotics work making a direct impact on millions of people’s lives every day,” he says. Wolf also highlights the collaborative nature of Amazon’s technical teams. Whether working on AI algorithms or robotic hardware, scientists and engineers at Amazon are constantly collaborating to solve new challenges.

Amazon’s culture of innovation extends beyond just technology. It’s also about empowering people. Samzun, who comes from a non-engineering background, points out that Amazon is a place where anyone with the right mindset can thrive, regardless of their academic background. “I came from a business management background and found myself leading a robotics project,” she says. “Amazon provides the platform for you to grow, learn new skills, and work on some of the most exciting projects in the world.”

For young engineers and scientists, Amazon offers a unique opportunity to work on state-of-the-art technology that has real-world impact. “We’re developing the next generation of AI and robotics,” says Wolf. “For anyone interested in this field, Amazon is the place where you can make a difference on a global scale.”

From Amazon’s leadership, it’s clear that the future of warehousing is about more than just automation. It’s about harnessing the power of robotics and AI to create smarter, more efficient, and safer working environments. But at its core it remains centered on people in its operations and those who make this technology possible—engineers, scientists, and technical professionals who are driven to solve some of the world’s most complex problems.

Amazon’s commitment to innovation, combined with its vast operational scale, makes it a leader in warehouse automation. The company’s focus on integrating robotics, AI, and human collaboration is transforming how goods are processed, stored, and delivered. And with so many innovative projects underway, the future of Amazon’s warehouses is one where technology and human ingenuity work hand in hand.

“We’re building systems that push the limits of robotics and AI,” says Wolf. “If you want to work on the cutting edge, this is the place to be.”

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!

[ MIT ]

[ JSK Lab ]

Thanks, Shintaro!

[ University of California San Diego ]

I enjoyed the Murderbot books, and the trailer for the TV show actually looks not terrible.

[ Murderbot ]

For service robots, being able to operate an unmodified elevator is much more difficult (and much more important) than you might think.

[ Pudu Robotics ]

[ Dexterity Robotics ]

I am utterly charmed by the design of this little robot.

[ RoMeLa ]

[ Atonaton ]

[ Clone Robotics ]

There are a lot of heavily produced humanoid robot videos from the companies selling them, but now that these platforms are entering the research space, we should start getting a more realistic sense of their capabilities.

[ University College London ]

Here’s a bit more footage from RIVR on their home delivery robot.

[ RIVR ]

And now, this.

[ EngineAI ]

[ Moonshot Podcast ]

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!

I love the platform and I love the use case, but this particular delivery method is... Odd?

[ RIVR ]

[ Robust AI ]

[ Disney Research Studios ]

[ Westwood ]

[ Figure ]

[ University of Zurich (UZH) ]

[ Unitree ]

You can say “real world manipulation” all you want, but until it’s actually in the real world, I’m not buying it.

[ 1X ]

[ Pudu Robotics ]

If you ever wanted to manipulate a trilby with 25 robots, a solution now exists.

[ Paper ] via [ EPFL Reconfigurable Robotics Lab ] published by [ IEEE Robotics and Automation Letters ]

We’ve been sharing videos from the Suzumori Endo Robotics Lab at the Institute of Science Tokyo for many years, and Professor Suzumori is now retiring.

Best wishes to Professor Suzumori!

[ Suzumori Endo Lab ]

[ DARPA ]

[ NASA ]

“Mooooo.”

This dairy barn is full of cows, as you might expect. Cows are being milked, cows are being fed, cows are being cleaned up after, and a few very happy cows are even getting vigorously scratched behind the ears. “I wonder where the farmer is,” remarks my guide, Jan Jacobs. Jacobs doesn’t seem especially worried, though—the several hundred cows in this barn are being well cared for by a small fleet of fully autonomous robots, and the farmer might not be back for hours. The robots will let him know if anything goes wrong.

At one of the milking robots, several cows are lined up, nose to tail, politely waiting their turn. The cows can get milked by robot whenever they like, which typically means more frequently than the twice a day at a traditional dairy farm. Not only is getting milked more often more comfortable for the cows, cows also produce about 10 percent more milk when the milking schedule is completely up to them.

“There’s a direct correlation between stress and milk production,” Jacobs says. “Which is nice, because robots make cows happier and therefore, they give more milk, which helps us sell more robots.”

Jan Jacobs is the human-robot interaction design lead for Lely, a maker of agricultural machinery. Founded in 1948 in Maassluis, Netherlands, Lely deployed its first Astronaut milking robot in the early 1990s. The company has since developed other robotic systems that assist with cleaning, feeding, and cow comfort, and the Astronaut milking robot is on its fifth generation. Lely is now focused entirely on robots for dairy farms, with around 135,000 of them deployed around the world.

The weather outside the barn is miserable. It’s late fall in the Netherlands, and a cold rain is gusting in from the sea, which is probably why the cows have quite sensibly decided to stay indoors and why the farmer is still nowhere to be found. Lely requires that dairy farmers who adopt its robots commit to letting their cows move freely between milking, feeding, and resting, as well as inside and outside the barn, at their own pace. “We believe that free cow traffic is a core part of the future of farming,” Jacobs says as we watch one cow stroll away from the milking robot while another takes its place. This is possible only when the farm operates on the cows’ schedule rather than a human’s.

A conventional dairy farm relies heavily on human labor. Lely estimates that repetitive daily tasks represent about a third of the average workday of a dairy farmer. In the morning, the cows are milked for the first time. Most dairy cows must be milked at least twice a day or they’ll become uncomfortable, and so the herd will line up on their own. Traditional milking parlors are designed to maximize human milking efficiency. A milking carousel, for instance, slowly rotates cows as they’re milked so that the dairy worker doesn’t have to move between stalls.

“We were spending 6 hours a day milking,” explains dairy farmer Josie Rozum, whose 120-cow herd at Takes Dairy Farm uses a pair of Astronaut A5 milking robots. “Now that the robots are handling all of that, we can focus more on animal care and comfort.”Lely

An experienced human using well-optimized equipment can attach a milking machine to a cow in just 20 to 30 seconds. The actual milking takes only a few minutes, but with the average small dairy farm in North America providing a home for several hundred cows, milking typically represents a time commitment of 4 to 6 hours per day.

There are other jobs that must be done every day at a dairy. Cows are happier with continuous access to food, which means feeding them several times a day. The feed is a mix of roughage (hay), silage (grass), and grain. The cows will eat all of this, but they prefer the grain, and so it’s common to see cows sorting their food by grabbing a mouthful and throwing it up into the air. The lighter roughage and silage flies farther than the grain does, leaving the cow with a pile of the tastier stuff as the rest gets tossed out of reach. This makes “feed pushing” necessary to shove the rest of the feed back within reach of the cow.

And of course there’s manure. A dairy cow produces an average of 68 kilograms of manure a day. All that manure has to be collected and the barn floors regularly cleaned.

The amount of labor needed to operate a dairy meant that until the early 1900s, most family farms could support only about eight cows. The introduction of the first milking machines, called bucket milkers, helped farmers milk 10 cows per hour instead of 4 by the mid-1920s. Rural electrification furthered dairy automation starting in the 1950s, and since then, both farm size and milk production have increased steadily. In the 1930s, a good dairy cow produced 3,600 kilograms of milk per year. Today, it’s almost 11,000 kilograms, and Lely believes that robots are what will enable small dairy farms to continue to scale sustainably.

Lely

But dairy robots are expensive. A milking robot can cost several hundred thousand dollars, plus an additional US $5,000 to $10,000 per year in operating costs. The Astronaut A5, Lely’s latest milking robot, uses a laser-guided robot arm to clean the cow’s udder before attaching teat cups one at a time. While the cow munches on treats, the Astronaut monitors her milk output, collecting data on 32 parameters, including indicators of the quality of the milk and the health of the cow. When milking is complete, the robot cleans the udder again, and the cow is free to leave as the robot steam cleans itself in preparation for the next cow.

Lely argues that although the initial cost is higher than that of a traditional milking parlor, the robots pay for themselves over time through higher milk production (due primarily to increased milking frequency) and lower labor costs. Lely’s other robots can also save on labor. The Vector mobile robot handles continuous feeding and feed pushing, and the Discovery Collector is a robotic manure vacuum that keeps the floors clean.

At Takes Dairy Farm, Rozum and her family used to spend several hours per day managing food for the cows. “The feeding robot is another amazing piece of the puzzle for our farm that allows us to focus on other things.”Takes Family Farm

For most dairy farmers, though, making more money is not the main reason to get a robot, explains Marcia Endres, a professor in the department of animal science at the University of Minnesota. Endres specializes in dairy-cattle management, behavior, and welfare, and studies dairy robot adoption. “When we first started doing research on this about 12 years ago, most of the farms that were installing robots were smaller farms that did not want to hire employees,” Endres says. “They wanted to do the work just with family labor, but they also wanted to have more flexibility with their time. They wanted a better lifestyle.”

Flexibility was key for the Takes family, who added Lely robots to their dairy farm in Ely, Iowa, four years ago. “When we had our old milking parlor, everything that we did as a family was always scheduled around milking,” says Josie Rozum, who manages the farm and a creamery along with her parents—Dan and Debbie Takes—and three brothers. “With the robots, we can prioritize our personal life a little bit more—we can spend time together on Christmas morning and know that the cows are still getting milked.”

Takes Family Dairy Farm’s 120-cow herd is milked by a pair of Astronaut A5 robots, with a Vector and three Discovery Collectors for feeding and cleaning. “They’ve become a crucial part of the team,” explains Rozum. “It would be challenging for us to find outside help, and the robots keep things running smoothly.” The robots also add sustainability to small dairy farms, and not just in the short term. “Growing up on the farm, we experienced the hard work, and we saw what that commitment did to our parents,” Rozum explains. “It’s a very tough lifestyle. Having the robots take over a little bit of that has made dairy farming more appealing to our generation.”

Takes Dairy Farm

Of the 25,000 dairy farms in the United States, Endres estimates about 10 percent have robots. This is about a third of the adoption rate in Europe, where farms tend to be smaller, so the cost of implementing the robots is lower. Endres says that over the last five years, she’s seen a shift toward robot adoption at larger farms with over 500 cows, due primarily to labor shortages. “These larger dairies are having difficulty finding employees who want to milk cows—it’s a very tedious job. And the robot is always consistent. The farmers tell me, ‘My robot never calls in sick, and never shows up drunk.’ ”

Endres is skeptical of Lely’s claim that its robots are responsible for increased milk production. “There is no research that proves that cows will be more productive just because of robots,” she says. It may be true that farms that add robots do see increased milk production, she adds, but it’s difficult to measure the direct effect that the robots have. “I have many dairies that I work with where they have both a robotic milking system and a conventional milking system, and if they are managing their cows well, there isn’t a lot of difference in milk production.”

The Lely Luna cow brush helps to keep cows’ skin healthy. It’s also relaxing and enjoyable, so cows will brush themselves several times a day.Lely

The robots do seem to improve the cows’ lives, however. “Welfare is not just productivity and health—it’s also the affective state, the ability to have a more natural life,” Endres says. “Again, it’s hard to measure, but I think that on most of these robot farms, their affective state is improved.” The cows’ relationship with humans changes too, comments Endres. When the cows no longer associate humans with being told where to go and what to do all the time, they’re much more relaxed and friendly toward people they meet. Rozum agrees. “We’ve noticed a tremendous change in our cows’ demeanor. They’re more calm and relaxed, just doing their thing in the barn. They’re much more comfortable when they can choose what to do.”

Cows are curious and clever animals, and have the same instinct that humans have when confronted with a new robot: They want to play with it. Because of this, Lely has had to cow-proof its robots, modifying their design and programming so that the machines can function autonomously around cows. Like many mobile robots, Lely’s dairy robots include contact-sensing bumpers that will pause the robot’s motion if it runs into something. On the Vector feeding robot, Lely product engineer René Beltman tells me, they had to add a software option to disable the bumper. “The cows learned that, ‘oh, if I just push the bumper, then the robot will stop and put down more feed in my area for me to eat.’ It was a free buffet. So you don’t want the cows to end up controlling the robot.” Emergency stop buttons had to be relocated so that they couldn’t be pressed by questing cow tongues.

There’s also a social component to cow-robot interaction. Within their herd, cows have a well-established hierarchy, and the robots need to work within this hierarchy to do their jobs. For example, a cow won’t move out of the way if it thinks that another cow is lower in the hierarchy than it is, and it will treat a robot the same way. The engineers had to figure out how the Discovery Collector could drive back and forth to vacuum up manure without getting blocked by cows. “In our early tests, we’d use sensors to have the robot stop to avoid running into any of the cows,” explains Jacobs. “But that meant that the robot became the weakest one in the hierarchy, and it would just end up crying in the corner because the cows wouldn’t move for it. So now, it doesn’t stop.”

One of the dirtiest jobs on a dairy farm is handled by the Discovery Collector, an autonomous manure vacuum. The robot relies on wheel odometry and ultrasonic sensors for navigation because it’s usually covered in manure.Evan Ackerman

“We make the robot drive slower for the first week, when it’s being introduced to a new herd,” adds Beltman. “That gives the cows time to figure out that the robot is at the top of the hierarchy.”

Besides maintaining their dominance at the top of the herd, the current generation of Lely robots doesn’t interact much with the cows, but that’s changing, Jacobs tells me. Right now, when a robot is driving through the barn, it makes a beeping sound to let the cows know it’s coming. Lely is looking into how to make these sounds more enjoyable for the cows. “This was a recent revelation for me,” Jacobs says. ”We’re not just designing interactions for humans. The cows are our users, too.”

Last year, Jacobs and researchers from Delft University of Technology, in the Netherlands, presented a paper at the IEEE Human-Robot Interaction (HRI) Conference exploring this concept of robot behavior development on working dairy farms. The researchers visited robotic dairies, interviewed dairy farmers, and held workshops within Lely to establish a robot code of conduct—a guide that Lely’s designers and engineers use when considering how their robots should look, sound, and act, for the benefit of both humans and cows. On the engineering side, this includes practical things like colors and patterns for lights and different types of sounds so that information is communicated consistently across platforms.

But there’s much more nuance to making a robot seem “reliable” or “friendly” to the end user, since such things are not only difficult to define but also difficult to implement in a way that’s appropriate for dairy farmers, who prioritize functionality.

Jacobs doesn’t want his robots to try to be anyone’s friend—not the cow’s, and not the farmer’s. “The robot is an employee, and it should have a professional relationship,” he says. “So the robot might say ‘Hi,’ but it wouldn’t say, ‘How are you feeling today?’ ” What’s more important is that the robots are trustworthy. For Jacobs, instilling trust is simple: “You cannot gain trust by doing tricks. If your robot is reliable and predictable, people will trust it.”

The electrically driven, pneumatically balanced robotic arm that the Lely Astronaut uses to milk cows is designed to withstand accidental (or intentional) kicks.Lely

The real challenge, Jacobs explains, is that Lely is largely on its own when it comes to finding the best way of integrating its robots into the daily lives of people who may have never thought they’d have robot employees. “There’s not that much knowledge in the robot world about how to approach these problems,” Jacobs says. “We’re working with almost 20,000 farmers who have a bigger robot workforce than a human workforce. They’re robot managers. And I don’t know that there necessarily are other companies that have a customer base of normal people who have strategic dependence on robots for their livelihood. That is where we are now.”

With the additional time and flexibility that the robots enable, some dairy farmers have been able to diversify. On our way back to Lely’s headquarters, we stop at Farm Het Lansingerland, owned by a Lely customer who has added a small restaurant and farm shop to his dairy. Large windows look into the barn so that restaurant patrons can watch the robots at work, caring for the cows that produce the cheese that’s on the menu. A self-guided tour takes you right up next to an Astronaut A5 milking robot, while signs on the floor warn of Vector feeding robots on the move. “This farmer couldn’t expand—this was as many cows as he’s allowed to have here,” Jacobs explains to me over cheese sandwiches. “So, he needs to have additional income streams. That’s why he started these other things. And the robots were essential for that.”

The farmer is an early adopter—someone who’s excited about the technology and actively interested in the robots themselves. But most of Lely’s tens of thousands of customers just want a reliable robotic employee, not a science project. “We help the farmer to prepare not just the environment for the robots, but also the mind,” explains Jacobs. “It’s a complete shift in their way of working.”

Besides managing the robots, the farmer must also learn to manage the massive amount of data that the robots generate about the cows. “The amount of data we get from the robots is a game changer,” says Rozum. “We can track milk production, health, and cow habits in real time. But it’s overwhelming. You could spend all day just sitting at the computer, looking at data and not get anything else done. It took us probably a year to really learn how to use it.”

The most significant advantages to farmers come from using the data for long-term optimization, says the University of Minnesota’s Endres. “In a conventional barn, the cows are treated as a group,” she says. “But the robots are collecting data about individual animals, which lets us manage them as individuals.” By combining data from a milking robot and a feeding robot, for example, farmers can close the loop, correlating when and how the cows are fed with their milk production. Lely is doing its best to simplify this type of decision making, says Jacobs. “You need to understand what the data means, and then you need to present it to the farmer in an actionable way.”

A: One Astronaut A5 robot can milk up to 60 cows. After the Astronaut cleans the teats, a laser sensor guides a robotic arm to attach the teat cups. Milking takes just a few minutes.

B: In the feed kitchen, the Vector robot recharges itself while different ingredients are loaded into its hopper and mixed together. Mixtures can be customized for different groups of cows.

C: The Vector robot dispenses freshly mixed food in small batches throughout the day. A laser measures the height of leftover food to make sure that the cows are getting the right amounts.

D: The Discovery Collector is a mop and vacuum for cow manure. It navigates the barn autonomously and returns to its docking station to remove waste, refill water, and wirelessly recharge.

E: As it milks, the Astronaut is collecting a huge amount of data—32 different parameters per teat. If it detects an issue, the farmer is notified, helping to catch health problems early.

F: Automated gates control meadow access and will keep a cow inside if she’s due to be milked soon. Cows are identified using RFID collars, which also track their behavior and health.

After lunch, we stop by Lely headquarters, where bright red life-size cow statues guard the entrance and all of the conference rooms are dairy themed. We get comfortable in Butter, and I ask Jacobs and Beltman what the future holds for their dairy robots.

In the near term, Lely is focused on making its existing robots more capable. Its latest feed-pushing robot is equipped with lidar and stereo cameras, which allow it to autonomously navigate around large farms without needing to follow a metal strip bolted to the ground. A new overhead camera system will leverage AI to recognize individual cows and track their behavior, while also providing farmers with an enormous new dataset that could allow Lely’s systems to help farmers make more nuanced decisions about cow welfare. The potential of AI is what Jacobs seems most excited about, although he’s cautious as well. “With AI, we’re suddenly going to take away an entirely different level of work. So, we’re thinking about doing research into the meaningfulness of work, to make sure that the things that we do with AI are the things that farmers want us to do with AI.”

“The idea of AI is very intriguing,” comments Rozum. “I think AI could help to simplify things for farmers. It would be a tool, a resource. But we know our cows best, and a farmer’s judgment has to be there too. There’s just some component of dairy farming that you cannot take the human out of. Robots are not going to be successful on a farm unless you have good farmers.”

Lely is aware of this and knows that its robots have to find the right balance between being helpful, and taking over. “We want to make sure not to take away the kinds of interactions that give dairy farmers joy in their work,” says Beltman. “Like feeding calves—every farmer likes to feed the calves.” Lely does sell an automated calf feeder that many dairy farmers buy, which illustrates the point: What’s the best way of designing robots to give humans the flexibility to do the work that they enjoy?

“This is where robotics is going,” Jacobs tells me as he gives me a lift to the train station. “As a human, you could have two other humans and six robots, and that’s your company.” Many industries, he says, look to robots with the objective of minimizing human involvement as much as possible so that the robots can generate the maximum amount of value for whoever happens to be in charge.

Dairy farms are different. Perhaps that’s because the person buying the robot is the person who most directly benefits from it. But I wonder if the concern over automation of jobs would be mitigated if more companies chose to emphasize the sustainability and joy of work equally with profit. Automation doesn’t have to be zero-sum—if implemented thoughtfully, perhaps robots can make work easier, more efficient, and more fun, too.

Jacobs certainly thinks so. “That’s my utopia,” he says. “And we’re working in the right direction.”

This is a sponsored article brought to you by Freudenberg Sealing Technologies.

The increasing deployment of collaborative robots (cobots) in outdoor environments presents significant engineering challenges, requiring highly advanced sealing solutions to ensure reliability and durability. Unlike industrial robots that operate in controlled indoor environments, outdoor cobots are exposed to extreme weather conditions that can compromise their mechanical integrity. Maintenance robots used in servicing wind turbines, for example, must endure intense temperature fluctuations, high humidity, prolonged UV radiation exposure, and powerful wind loads. Similarly, agricultural robots operate in harsh conditions where they are continuously exposed to abrasive dust, chemically aggressive fertilizers and pesticides, and mechanical stresses from rough terrains.

To ensure these robotic systems maintain long-term functionality, sealing solutions must offer effective protection against environmental ingress, mechanical wear, corrosion, and chemical degradation. Outdoor robots must perform flawlessly in temperature ranges spanning from scorching heat to freezing cold while withstanding constant exposure to moisture, lubricants, solvents, and other contaminants. In addition, sealing systems must be resilient to continuous vibrations and mechanical shocks, which are inherent to robotic motion and can accelerate material fatigue over time.

The development of sealing solutions for outdoor robotics demands an intricate balance of durability, flexibility, and resistance to wear. Robotic joints, particularly those in high-mobility systems, experience multidirectional movements within confined installation spaces, making the selection of appropriate sealing materials and geometries crucial. Traditional elastomeric O-rings, widely used in industrial applications, often fail under such extreme conditions. Exposure to high temperatures can cause thermal degradation, while continuous mechanical stress accelerates fatigue, leading to early seal failure. Chemical incompatibility with lubricants, fuels, and cleaning agents further contributes to material degradation, shortening operational lifespans.

Friction-related wear is another critical concern, especially in robotic joints that operate at high speeds. Excessive friction not only generates heat but can also affect movement precision. In collaborative robotics, where robots work alongside humans, such inefficiencies pose safety risks by delaying response times and reducing motion accuracy. Additionally, prolonged exposure to UV radiation can cause conventional sealing materials to become brittle and crack, further compromising their performance.

To address these demanding conditions, Freudenberg Sealing Technologies has developed a specialized sealing solution: Ingress Protection Seals for Robots (IPSR). Unlike conventional seals that rely on metallic springs for mechanical support, the IPSR design features an innovative Z-shaped geometry that dynamically adapts to the axial and radial movements typical in robotic joints.

Numerous seals are required in cobots and these are exposed to high speeds and forces.Freudenberg Sealing Technologies

This unique structural design distributes mechanical loads more efficiently, significantly reducing friction and wear over time. While traditional spring-supported seals tend to degrade due to mechanical fatigue, the IPSR configuration eliminates this limitation, ensuring long-lasting performance. Additionally, the optimized contact pressure reduces frictional forces in robotic joints, thereby minimizing heat generation and extending component lifespans. This results in lower maintenance requirements, a crucial factor in applications where downtime can lead to significant operational disruptions.

The development of IPSR technology relied extensively on Finite Element Analysis (FEA) simulations to optimize seal geometries, material selection, and surface textures before physical prototyping. These advanced computational techniques allowed engineers to predict and enhance seal behavior under real-world operational conditions.

FEA simulations focused on key performance factors such as frictional forces, contact pressure distribution, deformation under load, and long-term fatigue resistance. By iteratively refining the design based on simulation data, Freudenberg engineers were able to develop a sealing solution that balances minimal friction with maximum durability.

Furthermore, these simulations provided insights into how IPSR seals would perform under extreme conditions, including exposure to humidity, rapid temperature changes, and prolonged mechanical stress. This predictive approach enabled early detection of potential failure points, allowing for targeted improvements before mass production. By reducing the need for extensive physical testing, Freudenberg was able to accelerate the development cycle while ensuring high-performance reliability.

The effectiveness of a sealing solution is largely determined by its material composition. Freudenberg utilizes advanced elastomeric compounds, including Fluoroprene XP and EPDM, both selected for their exceptional chemical resistance, mechanical strength, and thermal stability.

Fluoroprene XP, in particular, offers superior resistance to aggressive chemicals, including solvents, lubricants, fuels, and industrial cleaning agents. Additionally, its resilience against ozone and UV radiation makes it an ideal choice for outdoor applications where continuous exposure to sunlight could otherwise lead to material degradation. EPDM, on the other hand, provides outstanding flexibility at low temperatures and excellent aging resistance, making it suitable for applications that require long-term durability under fluctuating environmental conditions.

To further enhance performance, Freudenberg applies specialized solid-film lubricant coatings to IPSR seals. These coatings significantly reduce friction and eliminate stick-slip effects, ensuring smooth robotic motion and precise movement control. This friction management not only improves energy efficiency but also enhances the overall responsiveness of robotic systems, an essential factor in high-precision automation.

While advanced simulations provide critical insights into seal behavior, empirical testing remains essential for validating real-world performance. Freudenberg subjected IPSR seals to rigorous durability tests, including prolonged exposure to moisture, dust, temperature cycling, chemical immersion, and mechanical vibration.

Throughout these tests, IPSR seals consistently achieved IP65 certification, demonstrating their ability to effectively prevent environmental contaminants from compromising robotic components. Real-world deployment in maintenance robotics for wind turbines and agricultural automation further confirmed their reliability, with extensive wear analysis showing significantly extended operational lifetimes compared to traditional sealing technologies.

In collaborative robotics, sealing performance plays a direct role in operational safety. Excessive friction in robotic joints can delay emergency-stop responses and reduce motion precision, posing potential hazards in human-robot interaction. By incorporating low-friction coatings and optimized sealing geometries, Freudenberg ensures that robotic systems respond rapidly and accurately, enhancing workplace safety and efficiency.

Freudenberg Sealing Technologies provides customized sealing solutions across a wide range of robotic applications, ensuring optimal performance in diverse environments.

Automated Guided Vehicles (AGVs) operate in industrial settings where they are exposed to abrasive contaminants, mechanical vibrations, and chemical exposure. Freudenberg employs reinforced PTFE composites to enhance durability and protect internal components.

Delta robots can perform complex movements at high speed. This requires seals that meet the high dynamic and acceleration requirements.Freudenberg Sealing Technologies

Delta robots, commonly used in food processing, pharmaceuticals, and precision electronics, require FDA-compliant materials that withstand rigorous cleaning procedures such as Cleaning-In-Place (CIP) and Sterilization-In-Place (SIP). Freudenberg utilizes advanced fluoropolymers that maintain structural integrity under aggressive sanitation processes.

Seals for Scara robots must have high chemical resistance, compressive strength and thermal resistance to function reliably in a variety of industrial environments.Freudenberg Sealing Technologies

SCARA robots benefit from Freudenberg’s Modular Plastic Sealing Concept (MPSC), which integrates sealing, bearing support, and vibration damping within a compact, lightweight design. This innovation optimizes robot weight distribution and extends component service life.

Six-axis robots used in automotive, aerospace, and electronics manufacturing require sealing solutions capable of withstanding high-speed operations, mechanical stress, and chemical exposure. Freudenberg’s Premium Sine Seal (PSS), featuring reinforced PTFE liners and specialized elastomer compounds, ensures maximum durability and minimal friction losses.

Freudenberg Sealing Technologies remains at the forefront of innovation, continuously developing new materials, sealing designs, and validation methods to address evolving challenges in robotics. Through strategic customer collaborations, cutting-edge material science, and state-of-the-art simulation technologies, Freudenberg ensures that its sealing solutions provide unparalleled reliability, efficiency, and safety across all robotic platforms.

A new prototype is laying claim to the title of smallest, lightest untethered flying robot.

At less than a centimeter in wingspan, the wirelessly powered robot is currently very limited in how far it can travel away from the magnetic fields that drive its flight. However, the scientists who developed it suggest there are ways to boost its range, which could lead to potential applications such as search and rescue operations, inspecting damaged machinery in industrial settings, and even plant pollination.

One strategy to shrink flying robots involves removing their batteries and supplying them electricity using tethers. However, tethered flying robots face problems operating freely in complex environments. This has led some researchers to explore wireless methods of powering robot flight.

“The dream was to make flying robots to fly anywhere and anytime without using an electrical wire for the power source,” says Liwei Lin, a professor of mechanical engineering at University of California at Berkeley. Lin and his fellow researchers detailed their findings in Science Advances.

Each flying robot has a 3D-printed body that consists of a propeller with four blades. This rotor is encircled by a ring that helps the robot stay balanced during flight. On top of each body are two tiny permanent magnets.

All in all, the insect-scale prototypes have wingspans as small as 9.4 millimeters and weigh as little as 21 milligrams. Previously, the smallest reported flying robot, either tethered or untethered, was 28 millimeters wide.

When exposed to an external alternating magnetic field, the robots spin and fly without tethers. The lowest magnetic field strength needed to maintain flight is 3.1 millitesla. (In comparison, a refrigerator magnet has a strength of about 10 mT.)

When the applied magnetic field alternates with a frequency of 310 hertz, the robots can hover. At 340 Hz, they accelerate upward. The researchers could steer the robots laterally by adjusting the applied magnetic fields. The robots could also right themselves after collisions to stay airborne without complex sensing or controlling electronics, as long as the impacts were not too large.

Experiments show the lift force the robots generate can exceed their weight by 14 percent, to help them carry payloads. For instance, a prototype that’s 20.5 millimeters wide and weighing 162.4 milligrams could carry an infrared sensor weighing 110 mg to scan its environment. The robots proved efficient at converting the energy given them into lift force—better than nearly all other reported flying robots, tethered or untethered, and also better than fruit flies and hummingbirds.

Currently the maximum operating range of these prototypes is about 10 centimeters away from the magnetic coils. One way to extend the operating range of these robots is to increase the magnetic field strength they experience tenfold by adding more coils, optimizing the configuration of these coils, and using beamforming coils, Lin notes. Such developments could allow the robots to fly up to a meter away from the magnetic coils.

The scientists could also miniaturize the robots even further. This would make them lighter, and so reduce the magnetic field strength they need for propulsion. “It could be possible to drive micro flying robots using electromagnetic waves such as those in radio or cell phone transmission signals,” Lin says. Future research could also place devices that can convert magnetic energy to electricity onboard the robots to power electronic components, the researchers add.

Your weekly selection of awesome robot videos

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!

And details!

[ Paper ] via [ University of California San Diego ]

Why do you want a humanoid robot to walk like a human? So that it doesn’t look weird, I guess, but it’s hard to imagine that a system that doesn’t have the same arrangement of joints and muscles that we do will move optimally by just trying to mimic us.

[ Figure ]

I don’t know how it manages it, but this little soft robotic worm somehow moves with an incredible amount of personality.

[ Paper ] via [ Georgia Tech ]

Thanks, Ellen!

A 180-degree pelvis rotation would put the “break” in “breakdancing” if this were a human doing it.

[ Boston Dynamics ]

My colleagues were impressed by this cooking robot, but that may be because journalists are always impressed by free food.

[ Posha ]

[ Paper ] via [ Dragon Lab ]

Thanks, Moju!

[ Unitree ]

There should be more robots that you can ride around on.

[ AgileX Robotics ]

There should be more robots that wear hats at work.

[ Ugo ]

iRobot, who pioneered giant docks for robot vacuums, is now moving away from giant docks for robot vacuums.

[ iRobot ]

There’s a famous experiment where if you put a dead fish in current, it starts swimming, just because of its biomechanical design. Somehow, you can do the same thing with an unactuated quadruped robot on a treadmill.

[ Delft University of Technology ]

Mush! Narrowly!

[ Hybrid Robotics ]

It’s freaking me out a little bit that this couple is apparently wandering around a huge mall that is populated only by robots and zero other humans.

[ MagicLab ]

I’m trying, I really am, but the yellow is just not working for me.

[ Kepler ]

[ Boston Dynamics ]

[ NASA ]

This University of Toronto Robotics Institute Seminar is from Sergey Levine at UC Berkeley, on Robotics Foundation Models.

[ University of Toronto ]

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!

Every time you see a humanoid demo in a warehouse or factory, ask yourself: Would a “superhumanoid” like this actually be a better answer?

[ Dexterity ]

The only reason that this is the second video in Video Friday this week, and not the first, is because you’ve almost certainly already seen it.

This is a collaboration between the Robotics and AI Institute and Boston Dynamics, and RAI has its own video, which is slightly different:

- YouTube

[ Boston Dynamics ] via [ RAI ]

Well this just looks a little bit like magic.

[ University of Pennsylvania Sung Robotics Lab ]

[ EngineAI ]

[ Sanctuary AI ]

This video is “introducing BotQ, Figure’s new high-volume manufacturing facility for humanoid robots,” but I just see some injection molding and finishing of a few plastic parts.

[ Figure ]

What’s in the bags, though?

[ Deep Robotics ]

This hurts my knees to watch, in a few different ways.

[ Unitree ]

Why the recent obsession with two legs when instead robots could have six? So much cuter!

[ Jizai ] via [ RobotStart ]

The world must know: who killed Mini-Duck?

[ Pollen ]

Seven hours of Digit robots at work at ProMat.

And there are two more days of these livestreams if you need more!

[ Agility ]