When you see a squirrel jump to a branch, you might think (and I myself thought, up until just now) that they’re doing what birds and primates would do to stick the landing: just grabbing the branch and hanging on. But it turns out that squirrels, being squirrels, don’t actually have prehensile hands or feet, meaning that they can’t grasp things with any significant amount of strength. Instead, they manage to land on branches using a “palmar” grasp, which isn’t really a grasp at all, in the sense that there’s not much grabbing going on. It’s more accurate to say that the squirrel is mostly landing on its palms and then balancing, which is very impressive.

This kind of dynamic stability is a trait that squirrels share with one of our favorite robots: Salto. Salto is a jumper too, and it’s about as non-prehensile as it’s possible to get, having just one limb with basically no grip strength at all. The robot is great at bouncing around on the ground, but if it could move vertically, that’s an entire new mobility dimension that could lead to some potentially interesting applications, including environmental scouting, search and rescue, and disaster relief.

In a paper published today in Science Robotics, roboticists have now taught Salto to leap from one branch to another like squirrels do, using a low torque gripper and relying on its balancing skills instead.

While we’re going to be mostly talking about robots here (because that’s what we do), there’s an entire paper by many of the same robotics researchers that was published in late February in the Journal of Experimental Biology about how squirrels land on branches this way. While you’d think that the researchers might have found some domesticated squirrels for this, they actually spent about a month bribing wild squirrels on the UC Berkeley campus to bounce around some instrumented perches while high speed cameras were rolling.

Squirrels aim for perfectly balanced landings, which allow them to immediately jump again. They don’t always get it quite right, of course, and they’re excellent at recovering from branch landings where they go a little bit over or under where they want to be. The research showed how squirrels use their musculoskeletal system to adjust their body position, dynamically absorbing the impact of landing with their forelimbs and altering their mass distribution to turn near misses into successful perches.

It’s these kinds of skills that Salto really needs to be able to usefully make jumps in the real world. When everything goes exactly the way it’s supposed to, jumping and perching is easy, but that almost never happens and the squirrel research shows how important it is to be able to adapt when things go wonky. It’s not like the little robot has a lot of degrees of freedom to work with—it’s got just one leg, just one foot, a couple of thrusters, and that spinning component which, believe it or not, functions as a tail. And yet, Salto manages to (sometimes!) make it work.

Those balanced upright landings are super impressive, although we should mention that Salto only achieved that level of success with two out of 30 trials. It only actually fell off the perch five times, and the rest of the time, it did manage a landing but then didn’t quite balance and either overshot or undershot the branch. There are some mechanical reasons why this is particularly difficult for Salto—for example, having just one leg to use for both jumping and landing means that the robot’s leg has to be rotated mid-jump. This takes time, and causes Salto to jump more vertically than squirrels do, since squirrels jump with their back legs and land with their front legs.

Based on these tests, the researchers identified four key features for balanced landings that apply to robots (and squirrels):

1. Power and accuracy are important!
2. It’s easier to land a shallower jump with a more horizontal trajectory.
3. Being able to squish down close to the branch helps with balancing.
4. Responsive actuation is also important!

Of these, Salto is great at the first one, very much not great at the second one, and also not great at the third and fourth ones. So in some sense, it’s amazing that the roboticists have been able to get it to do this branch-to-branch jumping as well as they have. There’s plenty more to do, though. Squirrels aren’t the only arboreal jumpers out there, and there’s likely more to learn from other animals—Salto was originally inspired by the galago (also known as bush babies), although those are more difficult to find on the UC Berkeley campus. And while the researchers don’t mention it, the obvious extension to this work is to chain together multiple jumps, and eventually to combine branch jumping with the ground jumping and wall jumping that Salto can do already to really give those squirrels a jump for their nuts.

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!

In 2026, a JAXA spacecraft is heading to the Martian moon Phobos to chuck a little rover at it.

[ DLR ]

[ UBTECH ]

[ LimX Dynamics ]

This is indeed a very fluid running gait, and the flip is also impressive, but I’m wondering what sort of actual value these skills add, you know? Or even what kind of potential value they’re leading up to.

[ EngineAI ]

[ Paper ] via [ Mitsubishi Electric Research Laboratories ]

Thanks, Yuki!

Running robot, you say? I’m thinking it might actually be a power walking robot.

[ MagicLab ]

Wake up, Reachy!

[ Pollen ]

Robot vacuum docks have gotten large enough that we’re now all supposed to pretend that we’re happy they’ve become pieces of furniture.

[ Roborock ]

[ MIT Sea Grant ]

I was at this RoboGames match! In 2010! And now I feel old!

[ Hardcore Robotics ]

Daniel Simu with a detailed breakdown of his circus acrobat partner robot. If you don’t want to watch the whole thing, make sure and check out 3:30.

[ Daniel Simu ]

Ukraine’s young tech entrepreneurs think that a combination of robots and lessons from war-gaming could turn the tide in the war against Russia. They are developing an intelligent operating system to enable a single controller to remotely operate swarms of interconnected drones and cannon-equipped land robots. The tech, they say, could help Ukraine cope with Russia’s numerical advantage.

Kyiv-based start-up Ark Robotics is conducting trials on an embryo of such a system in cooperation with one of the brigades of Ukraine’s ground forces. The company emerged about a year ago, when a group of young roboticists heard a speech by one of the Ukrainian commanders detailing challenges on the frontline.

“At that time, we were building unmanned ground vehicles [UGVs],” Andryi Udovychenko, Ark Robotics’s operations lead, told IEEE Spectrum on the sidelines of the Brave 1 Defense Tech Innovations Forum held in Kyiv last month. “But we heard that what we had [to offer] wasn’t enough. They said they needed something more.”

Since the war began, a vibrant defense tech innovation ecosystem has emerged in Ukraine, having started from modest beginnings of modifying China-made DJI MAVIC drones to make up for the lack of artillery. Today, Ukraine is a drone-making powerhouse. Dozens of startup companies are churning out newer and better tech and rapidly refining it to improve the effectiveness of the beleaguered nation’s troops. First-person-view drones have become a symbol of this war, but since last year they have begun to be complemented by UGVs, which help on the ground with logistics, evacuation of the wounded and also act as a new means of attack.

The new approach allows the Ukrainians to keep their soldiers away from the battle ground for longer periods but doesn’t erase the fact that Ukraine has far fewer soldiers than Russia does.

“Every single drone needs one operator, complicated drones need two or three operators, and we don’t have that many people,” Serhii Kupriienko, the CEO and founder of Swarmer, said during a panel at the Kyiv event. Swarmer is a Kyiv-based start-up developing technologies to allow groups of drones to operate as one self-coordinated swarm.

Ark Robotics are trying to take that idea yet another step. The company’s Frontier OS aspires to become a unifying interface that would allow drones and UGVs made by various makers to work together under the control of operators seated in control rooms miles away from the action.

“We have many types of drones that are using different controls, different interfaces and it’s really hard to build cohesion,” Udovychenko says. “To move forward, we need a system where we can control multiple different types of vehicles in a cohesive manner in complex operations.”

Udovychenko, a gaming enthusiast, is excited about the progress Ark Robotics has made. It could be a game-changer, he says, a new foundational technology for defense. It would make Ukraine “like Protoss,” the fictional technologically advanced nation in the military science fiction strategy game StarCraft.

But what powers him is much more than youthful geekiness. Building up Ukraine’s technological dominance is a mission fueled by grief and outrage.

“I don’t want to lose any more friends,” he remarks at one point, becoming visibly emotional. “We don’t want to be dying in the trenches, but we need to be able to defend our country and given that the societal math doesn’t favor us, we need to make our own math to win.”

Soldiers at an undisclosed location used laptops to test software from Ark Robotics.Ark Robotics

The scope of the challenge isn’t lost on him. The company has so far built a vehicle computing unit that serves as a central hub and control board for various unmanned vehicles including flying drones, UGVs and even marine vehicles.

“We are building this as a solution that enables the integration of various team developers and software, allowing us to extract the best components and rapidly scale them,” Udovychenko says. “This system pairs a high-performance computing module with an interface board that provides multiple connections for vehicle systems.

The platform allows a single operator to remotely guide a flock of robots but will in the future also incorporate autonomous navigation and task execution, according to Udovychenko. So far, the team has tested the technology in simple logistics exercises. For the grand vision to work, though, the biggest challenge will be maintaining reliable communication links between the controller and the robotic fleet, but also between the robots and drones.

“We’re not talking about communications in a relatively safe environment when you have an LTE network that has enough bandwidth to accommodate thousands of phones,” Udovychenko notes. “At the frontline, everything is affected by electronic warfare, so you need to be able to switch between different solutions including satellite, digital radio and radio mesh so that even if you lose connection to the server, you still have connection between the drones and robots so that they can move together and maintain some level of control between them.”

Udovychenko expects Ark Robotics’s partner brigade in the Ukraine armed forces to test the early version of the tech in a real-life situation within the next couple of months. His young drone operator friends are excited, he says. And how could they not be? The technology promises to turn warfighting into a kind of real-life video game. The new class of multi-drone operators will likely be recruited from the ranks of gaming aficionados.

“If we can take the best pilots and give them tools to combine the operations, we might see a tremendous advantage,” Udovychenko says. “It’s like in StarCraft. Some people are simply able to play the game right and obliterate their opponents within minutes even if they’re starting from the same basic conditions.”

Speaking at the Brave 1 Defense Tech Innovations Forum, Colonel Andrii Lebedenko, Deputy Commander-in-Chief of the Armed Forces of Ukraine, acknowledged that land battles have so far been Ukraine’s weakest area. He said that replacing “humans with robots as much as possible” is Ukraine’s near-term goal and he expressed confidence that upcoming technologies will give greater autonomy to the robot swarms.

Some roboticists, however, are more skeptical that swarms of autonomous robots will crawl en-masse across the battlefields of Eastern Ukraine any time soon. “Swarming is certainly a goal we should reach but it’s much easier with FPV drones than with ground-based robots,” Ivan Movchan, CEO of the Ukrainian Scale Company, a Kharkiv-based robot maker, told Spectrum.

“Navigation on the ground is more challenging simply because of the obstacles,” he adds. “But I do expect UGVs to become very common in Ukraine over the next year.”

Generative AI models are getting closer to taking action in the real world. Already, the big AI companies are introducing AI agents that can take care of web-based busywork for you, ordering your groceries or making your dinner reservation. Today, Google DeepMind announced two generative AI models designed to power tomorrow’s robots.

The models are both built on Google Gemini, a multimodal foundation model that can process text, voice, and image data to answer questions, give advice, and generally help out. DeepMind calls the first of the new models, Gemini Robotics, an “advanced vision-language-action model,” meaning that it can take all those same inputs and then output instructions for a robot’s physical actions. The models are designed to work with any hardware system, but were mostly tested on the two-armed Aloha 2 system that DeepMind introduced last year.

In a demonstration video, a voice says: “Pick up the basketball and slam dunk it” (at 2:27 in the video below). Then a robot arm carefully picks up a miniature basketball and drops it into a miniature net—and while it wasn’t a NBA-level dunk, it was enough to get the DeepMind researchers excited.

Google DeepMind released this demo video showing off the capabilities of its Gemini Robotics foundation model to control robots. Gemini Robotics

“This basketball example is one of my favorites,” said Kanishka Rao, the principal software engineer for the project, in a press briefing. He explains that the robot had “never, ever seen anything related to basketball,” but that its underlying foundation model had a general understanding of the game, knew what a basketball net looks like, and understood what the term “slam dunk” meant. The robot was therefore “able to connect those [concepts] to actually accomplish the task in the physical world,” says Rao.

Carolina Parada, head of robotics at Google DeepMind, said in the briefing that the new models improve over the company’s prior robots in three dimensions: generalization, adaptability, and dexterity. All of these advances are necessary, she said, to create “a new generation of helpful robots.”

Generalization means that a robot can apply a concept that it has learned in one context to another situation, and the researchers looked at visual generalization (for example, does it get confused if the color of an object or background changed), instruction generalization (can it interpret commands that are worded in different ways), and action generalization (can it perform an action it had never done before).

Parada also says that robots powered by Gemini can better adapt to changing instructions and circumstances. To demonstrate that point in a video, a researcher told a robot arm to put a bunch of plastic grapes into a clear Tupperware container, then proceeded to shift three containers around on the table in an approximation of a shyster’s shell game. The robot arm dutifully followed the clear container around until it could fulfill its directive.

Google DeepMind says Gemini Robotics is better than previous models at adapting to changing instructions and circumstances. Google DeepMind

As for dexterity, demo videos showed the robotic arms folding a piece of paper into an origami fox and performing other delicate tasks. However, it’s important to note that the impressive performance here is in the context of a narrow set of high-quality data that the robot was trained on for these specific tasks, so the level of dexterity that these tasks represent is not being generalized.

The second model introduced today is Gemini Robotics-ER, with the ER standing for “embodied reasoning,” which is the sort of intuitive physical world understanding that humans develop with experience over time. We’re able to do clever things like look at an object we’ve never seen before and make an educated guess about the best way to interact with it, and this is what DeepMind seeks to emulate with Gemini Robotics-ER.

Parada gave an example of Gemini Robotics-ER’s ability to identify an appropriate grasping point for picking up a coffee cup. The model correctly identifies the handle, because that’s where humans tend to grasp coffee mugs. However, this illustrates a potential weakness of relying on human-centric training data: for a robot, especially a robot that might be able to comfortably handle a mug of hot coffee, a thin handle might be a much less reliable grasping point than a more enveloping grasp of the mug itself.

Vikas Sindhwani, DeepMind’s head of robotic safety for the project, says the team took a layered approach to safety. It starts with classic physical safety controls that manage things like collision avoidance and stability, but also includes “semantic safety” systems that evaluate both its instructions and the consequences of following them. These systems are most sophisticated in the Gemini Robotics-ER model, says Sindhwani, which is “trained to evaluate whether or not a potential action is safe to perform in a given scenario.”

And because “safety is not a competitive endeavor,” Sindhwani says, DeepMind is releasing a new data set and what it calls the Asimov benchmark, which is intended to measure a model’s ability to understand common-sense rules of life. The benchmark contains both questions about visual scenes and text scenarios, asking models’ opinions on things like the desirability of mixing bleach and vinegar (a combination that make chlorine gas) and putting a soft toy on a hot stove. In the press briefing, Sindhwani said that the Gemini models had “strong performance” on that benchmark, and the technical report showed that the models got more than 80 percent of questions correct.

Back in December, DeepMind and the humanoid robotics company Apptronik announced a partnership, and Parada says that the two companies are working together “to build the next generation of humanoid robots with Gemini at its core.” DeepMind is also making its models available to an elite group of “trusted testers”: Agile Robots, Agility Robotics, Boston Dynamics, and Enchanted Tools.

After January’s Southern California wildfires, the question of burying energy infrastructure to prevent future fires has gained renewed urgency in the state. While the exact cause of the fires remains under investigation, California utilities have spent years undergrounding power lines to mitigate fire risks. Pacific Gas & Electric, which has installed over 1,287 kilometers of underground power lines since 2021, estimates the method is 98 percent effective in reducing ignition threats. Southern California Edison has buried over 40 percent of its high-risk distribution lines, and 63 percent of San Diego Gas & Electric’s regional distribution system is now underground.

Still, the exorbitant cost of underground construction leaves much of the U.S. power grid’s 8.8 million kilometers of distribution lines and 180 million utility poles exposed to tree strikes, flying debris, and other opportunities for sparks to cascade into a multi-acre blaze. Recognizing the need for cost-effective undergrounding solutions, the U.S. Department of Energy launched GOPHURRS in January 2024. The three-year program pours $34 million into 12 projects to develop more efficient undergrounding technologies that minimize surface disruptions while supporting medium-voltage power lines.

One recipient, Case Western Reserve University in Cleveland, Ohio, is building a self-propelled robotic sleeve that mimics earthworms’ characteristic peristaltic movement to advance through soil. Awarded $2 million, Case’s “peristaltic conduit” concept hopes to more precisely navigate underground and reduce the risk of unintended damage, such as breaking an existing pipe.

Despite its benefits, undergrounding remains cost-prohibitive at US $1.1 to $3.7 million per kilometer ($1.8 to $6 million per mile) for distribution lines and $3.7 to $62 million per kilometer for transmission lines, according to estimates from California’s three largest utilities. That’s significantly more than overhead infrastructure, which costs $394,000 to $472,000 per kilometer for distribution lines and $621,000 to $6.83 million per kilometer for transmission lines.

The most popular method of undergrounding power lines, called open trenching, requires extensive excavation, conduit installation, and backfilling, making it expensive and logistically complicated. And it’s often impractical in dense urban areas where underground infrastructure is already congested with plumbing, fiber optics, and other utilities.

Trenchless methods like horizontal directional drilling (HDD) provide a less invasive way to get power lines under roads and railways by creating a controlled, curved bore path that starts at a shallow entry angle, deepens to pass obstacles, and resurfaces at a precise exit point. But HDD is even more expensive than open trenching due to specialized equipment, complex workflows, and the risk of damaging existing infrastructure.

Given the steep costs, utilities often prioritize cheaper fire mitigation strategies like trimming back nearby trees and other plants, using insulated conductors, and stepping up routine inspections and repairs. While not as effective as undergrounding, these measures have been the go-to option, largely because faster, cheaper underground construction methods don’t yet exist.

Ted Kury, director of energy studies at the University of Florida’s Public Utility Research Center, who has extensively studied the costs and benefits of undergrounding, says technologies implementing directional drilling improvements “could make undergrounding more practical in urban or densely populated areas where open trenching, and its attendant disruptions to the surrounding infrastructure, could result in untenable costs.”

In Case’s worm-inspired robot, alternating sections are designed to expand and retract to anchor and advance the machine. This flexible force increases precision and reduces the risk of impacting and breaking pipes. Conventional methods require large turning radii exceeding 300 meters, but Case’s 1.5-meter turning radius will enable the device to flexibly maneuver around existing infrastructure.

“We use actuators to change the length and diameter of each segment,” says Kathryn Daltorio, an associate engineering professor and co-director of Case’s Biologically-Inspired Robotics Lab. “The short and fat segments press against the walls of the burrow, then they anchor so the thin segments can advance forward. If two segments aren’t touching the ground but they’re changing length at the same time, your anchors don’t slip and you advance forward.”

Daltorio and her colleagues have studied earthworm-inspired robotics for over a decade, originally envisioning the technology for surgical and confined-space applications before recognizing its potential for undergrounding power lines.

Case Western Reserve University’s worm-like digging robot can turn faster than other drilling techniques to avoid obstacles.Kathryn Daltorio/Case School of Engineering

Traditional HDD relies on pushing a drill head through soil, requiring more force as the bore length grows. Case’s drilling concept generates the force needed for the tip from the peristaltic segments within the borehole. As the path gets longer, only the front segments dig deeper. “If the robot hits something, operators can pull back and change directions, burrowing along the way to complete the circuit by changing the depth,” Daltorio says.

Another key difference from HDD is integrated conduit installation. In HDD, the drill goes through the entire length first, and then the power conduit is pulled through. Case’s peristaltic robot lays the conduit while traveling, reducing the overall installation time.

“The peristaltic conduit approach is fascinating [and] certainly seems to be addressing concerns regarding the sheer variety of underground obstacles,” says the University of Florida’s Kury. However, he highlights a larger concern with undergrounding innovations—not just Case’s—in meeting a constantly evolving environment. Today’s underground will look very different in 10 years, as soil profiles shift, trees grow, animals tunnel, and people dig and build. “Underground cables will live for decades, and the sustainability of these technologies depends on how they adapt to this changing structure,” Kury added.

Daltorio notes that current undergrounding practices involve pouring concrete around the lines before backfilling to protect them from future excavation, a challenge for existing trenchless methods. But Case’s project brings two major benefits. First, by better understanding borehole design, engineers have more flexibility in choosing conduit materials to match the standards for particular environments. Also, advancements in burrowing precision could minimize the likelihood of future disruptions from human activities.

The research team is exploring different ways to reinforce the digging robot’s exterior while it’s underground.Olivia Gatchall

Daltorio’s team is collaborating with several partners, with Auburn University in Alabama contributing geotechnical expertise, Stony Brook University in New York running the modeling, and the University of Texas at Austin studying sediment interactions.

The project aims to halve undergrounding costs, though Daltorio cautions that it’s too early to commit to a specific cost model. Still, the time-saving potential appears promising. “With conventional approaches, planning, permitting and scheduling can take months,” Daltorio says. “By simplifying the process, it might be a few inspections at the endpoints, a few days of autonomous burrowing with minimal disruption to traffic above, followed by a few days of cleaning, splicing, and inspection.”

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!

My favorite part is 1:40, where Atlas squats down to pick a part up off the ground.

[ Boston Dynamics ]

I’m mostly impressed that making contact with that stick doesn’t cause the robot to fall over.

[ Unitree ]

[ Robot Studio ] at [ University of Michigan ]

Hard to believe that RoMeLa has been developing humanoid robots for 20 (!) years. Here’s to 20 more!

[ RoMeLa ] at [ University of California Los Angeles ]

[ Pollen ]

[ Paper ]

Thanks, Ayato!

Tech United Eindhoven is working on quadrupedal soccer robots, which should be fun.

[ Tech United ]

[ Alpha-Biact ]

Thanks, Masato!

[ UBTECH ]

[ ETH Zurich Robotic Systems Lab ]

I do not ever want to inspect a wind turbine blade from the inside.

[ Flyability ]

Sometimes you can learn more about a robot from an instructional unboxing video than from a fancy demo.

[ DEEP Robotics ]

[ RoboPAIR ]

[ ReachBot ]

Although they’re a staple of sci-fi movies and conspiracy theories, in real life, tiny flying microbots—weighed down by batteries and electronics—have struggled to get very far. But a new combination of circuits and lightweight solid-state batteries called a “flying batteries” topology could let these bots really take off, potentially powering microbots for hours from a system that weighs milligrams.

Microbots could be an important technology to find people buried in rubble or scout ahead in other dangerous situations. But they’re a difficult engineering challenge, says Patrick Mercier, an electrical and computer engineering professor at the University of California, San Diego. Mercier’s student Zixiao Lin described the new circuit last month at the IEEE International Solid State Circuits Conference (ISSCC). “You have these really tiny robots, and you want them to last as long as possible in the field,” Mercier says. “The best way to do that is to use lithium-ion batteries, because they have the best energy density. But there’s this fundamental problem, where the actuators need much higher voltage than what the battery is capable of providing.”

A lithium cell can provide about 4 volts, but piezoelectric actuators for microbots need tens to hundreds of volts, explains Mercier. Researchers, including Mercier’s own group, have developed circuits such as boost converters to pump up the voltage. But because they need relatively large inductors or a bunch of capacitors, these add too much mass and volume, typically taking up about as much room as the battery itself.

A new kind of solid-state battery, developed at the French national electronics laboratory CEA-Leti, offered a potential solution. The batteries are a thin-film stack of material, including lithium cobalt oxide and lithium phosphorus oxynitride, made using semiconductor processing technology, and they can be diced up into tiny cells. A 0.33-cubic-millimeter, 0.8-milligram cell can store 20 microampere-hours of charge, or about 60 ampere-hours per liter. (Lithium-ion earbud batteries provide more than 100 Ah/L, but are about 1,000 times as large.) A CEA-Leti spinoff based on the technology, Inject Power, in Grenoble, France, is gearing up to begin volume manufacturing in late 2026.

Because a solid-state battery can be diced up into tiny cells, researchers thought that they could achieve high voltages using a circuit that needs no capacitors or inductors. Instead, the circuit actively rearranges the connections among many tiny batteries moving them from parallel to serial and back again.

Imagine a microdrone that moves by flapping wings attached to a piezoelectric actuator. On its circuit board are a dozen or so of the solid-state microbatteries. Each battery is part of a circuit consisting of four transistors. These act as switches that can dynamically change the connection to that battery’s neighbor so that it is either parallel, so they share the same voltage, or serial, so their voltages are added.

At the start, all the batteries are in parallel, delivering a voltage that is nowhere near enough to trigger the actuator. The 2-square-millimeter IC the UCSD team built then begins opening and closing the transistor switches. This rearranges the connections between the cells so that first two cells are connected serially, then three, then four, and so on. In a few hundredths of a second, the batteries are all connected in series, and the voltage has piled so much charge onto the actuator that it snaps the microbot’s wings down. The IC then unwinds the process, making the batteries parallel again, one at a time.

The integrated circuit in the “flying battery” has a total area of 2 square millimeters.Patrick Mercier

Why not just connect every battery in series at once instead of going through this ramping up and down scheme? In a word, efficiency.

As long as the battery serialization and parallelization is done at a low-enough frequency, the system is charging adiabatically. That is, its power losses are minimized.

But it’s what happens after the actuator triggers “where the real magic comes in,” says Mercier. The piezoelectric actuator in the circuit acts like a capacitor, storing energy. “Just like you have regenerative breaking in a car, we can recover some of the energy that we stored in this actuator.” As each battery is unstacked, the remaining energy storage system has a lower voltage than the actuator, so some charge flows back into the batteries.

The UCSD team actually tested two varieties of solid-state microbatteries—1.5-volt ceramic version from Tokyo-based TDK (CeraCharge 1704-SSB) and a 4-V custom design from CEA-Leti. With 1.6 grams of TDK cells, the circuit reached 56.1 volts and delivered a power density of 79 milliwatts per gram, but with 0.014 grams of the custom storage, it maxed out at 68 V, and demonstrated a power density of 4,500 mW/g.

Mercier plans to test the system with robotics partners while his team and CEA-Leti work to improved the flying batteries system’s packaging, miniaturization, and other properties. One important characteristic that needs work is the internal resistance of the microbatteries. “The challenge there is that the more you stack, the higher the series resistance is, and therefore the lower the frequency we can operate the system,” he says.

Nevertheless, Mercier seems bullish on flying batteries’ chances of keeping microbots aloft. “Adiabatic charging with charge recovery and no passives: Those are two wins that help increase flight time.”

Salto has been one of our favorite robots since we were first introduced to it in 2016 as a project out of Ron Fearing’s lab at UC Berkeley. The palm-sized spring-loaded jumping robot has gone from barely being able to chain together a few open-loop jumps to mastering landings, bouncing around outside, powering through obstacle courses, and occasionally exploding.

What’s quite unusual about Salto is that it’s still an active research project—nine years is an astonishingly long life time for any robot, especially one without any immediately obvious practical applications. But one of Salto’s original creators, Justin Yim (who is now a professor at the University of Illinois), has found a niche where Salto might be able to do what no other robot can: mid-air sampling of the water geysering out of the frigid surface of Enceladus, a moon of Saturn.

What makes Enceladus so interesting is that it’s completely covered in a 40 kilometer thick sheet of ice, and underneath that ice is a 10 km-deep global ocean. And within that ocean can be found—we know not what. Diving in that buried ocean is a problem that robots may be able to solve at some point, but in the near(er) term, Enceladus’ south pole is home to over a hundred cryovolcanoes that spew plumes of water vapor and all kinds of other stuff right out into space, offering a sampling opportunity to any robot that can get close enough for a sip.

“We can cover large distances, we can get over obstacles, we don’t require an atmosphere, and we don’t pollute anything.” —Justin Yim, University of Illinois

Yim, along with another Salto veteran Ethan Schaler (now at JPL), have been awarded funding through NASA’s Innovative Advanced Concepts (NIAC) program to turn Salto into a robot that can perform “Legged Exploration Across the Plume,” or in an only moderately strained backronym, LEAP. LEAP would be a space-ified version of Salto with a couple of major modifications allowing it to operate in a freezing, airless, low-gravity environment.

As best as we can make out from images taken during Cassini flybys, the surface of Enceladus is unfriendly to traditional rovers, covered in ridges and fissures, although we don’t have very much information on the exact properties of the terrain. There’s also essentially no atmosphere, meaning that you can’t fly using aerodynamics, and if you use rockets to fly instead, you run the risk of your exhaust contaminating any samples that you take.

“This doesn’t leave us with a whole lot of options for getting around, but one that seems like it might be particularly suitable is jumping,” Yim tells us. “We can cover large distances, we can get over obstacles, we don’t require an atmosphere, and we don’t pollute anything.” And with Enceladus’ gravity being just 1/80th that of Earth, Salto’s meter-high jump on Earth would enable it to travel a hundred meters or so on Enceladus, taking samples as it soars through cryovolcano plumes.

The current version of Salto does require an atmosphere, because it uses a pair of propellers as tiny thrusters to control yaw and roll. On LEAP, those thrusters would be replaced with an angled pair of reaction wheels instead. To deal with the terrain, the robot will also likely need a foot that can handle jumping from (and landing on) surfaces composed of granular ice particles.

LEAP is designed to jump through Enceladus’ many plumes to collect samples, and use the moon’s terrain to direct subsequent jumps.NASA/Justin Yim

While the vision is for LEAP to jump continuously, bouncing over the surface and through plumes in a controlled series of hops, sooner or later it’s going to have a bad landing, and the robot has to be prepared for that. “I think one of the biggest new technological developments is going to be multimodal locomotion,” explains Yim. “Specifically, we’d like to have a robust ability to handle falls.” The reaction wheels can help with this in two ways: they offer some protection by acting like a shell around the robot, and they can also operate as a regular pair of wheels, allowing the robot to roll around on the ground a little bit. “With some maneuvers that we’re experimenting with now, the reaction wheels might also be able to help the robot to pop itself back upright so that it can start jumping again after it falls over,” Yim says.

A NIAC project like this is about as early-stage as it gets for something like LEAP, and an Enceladus mission is very far away as measured by almost every metric—space, time, funding, policy, you name it. Long term, the idea with LEAP is that it could be an add-on to a mission concept called the Enceladus Orbilander. This US $2.5 billion spacecraft would launch sometime in the 2030s, and spend about a dozen years getting to Saturn and entering orbit around Enceladus. After 1.5 years in orbit, the spacecraft would land on the surface, and spend a further 2 years looking for biosignatures. The Orbilander itself would be stationary, Yim explains, “so having this robotic mobility solution would be a great way to do expanded exploration of Enceladus, getting really long distance coverage to collect water samples from plumes on different areas of the surface.”

LEAP has been funded through a nine-month Phase 1 study that begins this April. While the JPL team investigates ice-foot interactions and tries to figure out how to keep the robot from freezing to death, at the University of Illinois Yim will be upgrading Salto with self-righting capability. Honestly, it’s exciting to think that after so many years, Salto may have finally found an application where it offers the actual best solution for solving this particular problem of low-gravity mobility for science.

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!

[ Science Robotics ] via [ EPFL ]

A robot CAN get up this way, but SHOULD a robot get up this way?

[ University of Illinois Urbana-Champaign ]

I’m impressed with the capabilities here, but not the use case. There are already automated systems that do this much faster, much more reliably, and almost certainly much more cheaply. So, probably best to think of this as more of a technology demo than anything with commercial potential.

[ Figure ]

You all know by now not to take this video too seriously, but I will say that an advantage of building a robot like this for the home is that realistically it can spend most of its time sitting down and (presumably) charging.

[ 1X Technologies ]

This video compilation showcases novel aerial and underwater drone platforms and an ultra-quiet electric vertical takeoff and landing (eVTOL) propeller. These technologies were developed by the Advanced Vertical Flight Laboratory (AVFL) at Texas A&M University and Harmony Aeronautics, an AVFL spin-off company.

[ AVFL ]

Yes! More research like this please; legged robots (of all sizes) are TOO STOMPY.

[ ETH Zurich ]

Robosquirrel!

[ BBC ] via [ Laughing Squid ]

[ Columbia University, School of Engineering and Applied Science ]

If I was asking my robot to do a front flip for the first(ish) time, my face would probably look like the poor guy at 0:25. But it worked!

[ EngineAI ]

A hazardous manner? Like teaching it martial arts...?

[ Unitree ]

[ SLAMSpoof ]

Thanks, Kentaro!

[ Sanctuary AI ]

I don’t know whether it’s the shape or the noise or what, but this robot pleases me.

[ University of Pennsylvania, Sung Robotics Lab ]

[ Clearpath Robotics ]

[ European Space Agency ]

[ IBM Research ]

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!

This is moderately impressive; my favorite part is probably the handoffs and that extra little bit of HRI with what we’d call eye contact if these robots had faces. But keep in mind that you’re looking at close to best case for robotic manipulation, and that if the robots had been given the bag instead of well-spaced objects on a single color background, or if the fridge had a normal human amount of stuff in it, they might be having a much different time of it. Also, is it just me, or is the sound on this video very weird? Like, some things make noise, some things don’t, and the robots themselves occasionally sound more like someone just added in some “soft actuator sound” or something. Also also, I’m of a suspicious nature, and when there is an abrupt cut between “robot grasps door” and “robot opens door,” I assume the worst.

[ Figure ]

[ Paper ] via [ Science Robotics ]

I don’t know about you, but I always dance better when getting beaten with a stick.

[ Unitree Robotics ]

This is big news, people: Sweet Bite Ham Ham, one of the greatest and most useless robots of all time, has a new treat.

All yours for about US $100, overseas shipping included.

[ Ham Ham ] via [ Robotstart ]

[ MagicLab ]

Thanks, Ni Tao!

No, I’m not creeped out at all, why?

[ Clone Robotics ]

Happy 40th Birthday to the MIT Media Lab!

[ MIT Media Lab ]

[ Paper ] via [ BruBotics ]

Thanks, Bram!

[ Paper ] via [ IEEE Transactions on Robots ]

[ Robotics and Intelligent Systems Laboratory ]

I’m not sure I’ve ever seen this trick performed by a robot with soft fingers before.

[ Paper ]

There are a lot of robots involved in car manufacturing. Like, a lot.

[ Kawasaki Robotics ]

[ Carnegie Mellon University Robotics Institute ]

Somebody’s got to test all those luxury handbags and purses. And by somebody, I mean somerobot.

[ Qb Robotics ]

Do not trust people named Evan.

[ Tufts University Human-Robot Interaction Lab ]

[ MIT ]

About a year ago, Boston Dynamics released a research version of its Spot quadruped robot, which comes with a low-level application programming interface (API) that allows direct control of Spot’s joints. Even back then, the rumor was that this API unlocked some significant performance improvements on Spot, including a much faster running speed. That rumor came from the Robotics and AI (RAI) Institute, formerly The AI Institute, formerly the Boston Dynamics AI Institute, and if you were at Marc Raibert’s talk at the ICRA@40 conference in Rotterdam last fall, you already know that it turned out not to be a rumor at all.

Today, we’re able to share some of the work that the RAI Institute has been doing to apply reality-grounded reinforcement learning techniques to enable much higher performance from Spot. The same techniques can also help highly dynamic robots operate robustly, and there’s a brand new hardware platform that shows this off: an autonomous bicycle that can jump.

This video is showing Spot running at a sustained speed of 5.2 meters per second (11.6 miles per hour). Out of the box, Spot’s top speed is 1.6 m/s, meaning that RAI’s spot has more than tripled (!) the quadruped’s factory speed.

If Spot running this quickly looks a little strange, that’s probably because it is strange, in the sense that the way this robot dog’s legs and body move as it runs is not very much like how a real dog runs at all. “The gait is not biological, but the robot isn’t biological,” explains Farbod Farshidian, roboticist at the RAI Institute. “Spot’s actuators are different from muscles, and its kinematics are different, so a gait that’s suitable for a dog to run fast isn’t necessarily best for this robot.”

The best Farshidian can categorize how Spot is moving is that it’s somewhat similar to a trotting gait, except with an added flight phase (with all four feet off the ground at once) that technically turns it into a run. This flight phase is necessary, Farshidian says, because the robot needs that time to successively pull its feet forward fast enough to maintain its speed. This is a “discovered behavior,” in that the robot was not explicitly programmed to “run,” but rather was just required to find the best way of moving as fast as possible.

The Spot controller that ships with the robot when you buy it from Boston Dynamics is based on model predictive control (MPC), which involves creating a software model that approximates the dynamics of the robot as best you can, and then solving an optimization problem for the tasks that you want the robot to do in real time. It’s a very predictable and reliable method for controlling a robot, but it’s also somewhat rigid, because that original software model won’t be close enough to reality to let you really push the limits of the robot. And if you try to say, “Okay, I’m just going to make a superdetailed software model of my robot and push the limits that way,” you get stuck because the optimization problem has to be solved for whatever you want the robot to do, in real time, and the more complex the model is, the harder it is to do that quickly enough to be useful. Reinforcement learning (RL), on the other hand, learns offline. You can use as complex of a model as you want, and then take all the time you need in simulation to train a control policy that can then be run very efficiently on the robot.

Your browser does not support the video tag. In simulation, a couple of Spots (or hundreds of Spots) can be trained in parallel for robust real-world performance.Robotics and AI Institute

In the example of Spot’s top speed, it’s simply not possible to model every last detail for all of the robot’s actuators within a model-based control system that would run in real time on the robot. So instead, simplified (and typically very conservative) assumptions are made about what the actuators are actually doing so that you can expect safe and reliable performance.

Farshidian explains that these assumptions make it difficult to develop a useful understanding of what performance limitations actually are. “Many people in robotics know that one of the limitations of running fast is that you’re going to hit the torque and velocity maximum of your actuation system. So, people try to model that using the data sheets of the actuators. For us, the question that we wanted to answer was whether there might exist some other phenomena that was actually limiting performance.”

Searching for these other phenomena involved bringing new data into the reinforcement learning pipeline, like detailed actuator models learned from the real-world performance of the robot. In Spot’s case, that provided the answer to high-speed running. It turned out that what was limiting Spot’s speed was not the actuators themselves, nor any of the robot’s kinematics: It was simply the batteries not being able to supply enough power. “This was a surprise for me,” Farshidian says, “because I thought we were going to hit the actuator limits first.”

Spot’s power system is complex enough that there’s likely some additional wiggle room, and Farshidian says the only thing that prevented them from pushing Spot’s top speed past 5.2 m/s is that they didn’t have access to the battery voltages so they weren’t able to incorporate that real-world data into their RL model. “If we had beefier batteries on there, we could have run faster. And if you model that phenomena as well in our simulator, I’m sure that we can push this farther.”

Farshidian emphasizes that RAI’s technique is about much more than just getting Spot to run fast—it could also be applied to making Spot move more efficiently to maximize battery life, or more quietly to work better in an office or home environment. Essentially, this is a generalizable tool that can find new ways of expanding the capabilities of any robotic system. And when real-world data is used to make a simulated robot better, you can ask the simulation to do more, with confidence that those simulated skills will successfully transfer back onto the real robot.

Reinforcement learning isn’t just good for maximizing the performance of a robot—it can also make that performance more reliable. The RAI Institute has been experimenting with a completely new kind of robot that it invented in-house: a little jumping bicycle called the Ultra Mobility Vehicle, or UMV, which was trained to do parkour using essentially the same RL pipeline for balancing and driving as was used for Spot’s high-speed running.

There’s no independent physical stabilization system (like a gyroscope) keeping the UMV from falling over; it’s just a normal bike that can move forward and backward and turn its front wheel. As much mass as possible is then packed into the top bit, which actuators can rapidly accelerate up and down. “We’re demonstrating two things in this video,” says Marco Hutter, director of the RAI Institute’s Zurich office. “One is how reinforcement learning helps make the UMV very robust in its driving capabilities in diverse situations. And second, how understanding the robots’ dynamic capabilities allows us to do new things, like jumping on a table which is higher than the robot itself.”

“The key of RL in all of this is to discover new behavior and make this robust and reliable under conditions that are very hard to model. That’s where RL really, really shines.” —Marco Hutter, The RAI Institute

As impressive as the jumping is, for Hutter, it’s just as difficult (if not more difficult) to do maneuvers that may seem fairly simple, like riding backwards. “Going backwards is highly unstable,” Hutter explains. “At least for us, it was not really possible to do that with a classical [MPC] controller, particularly over rough terrain or with disturbances.”

Getting this robot out of the lab and onto terrain to do proper bike parkour is a work in progress that the RAI Institute says it will be able to demonstrate in the near future, but it’s really not about what this particular hardware platform can do—it’s about what any robot can do through RL and other learning-based methods, says Hutter. “The bigger picture here is that the hardware of such robotic systems can in theory do a lot more than we were able to achieve with our classic control algorithms. Understanding these hidden limits in hardware systems lets us improve performance and keep pushing the boundaries on control.”

Your browser does not support the video tag. Teaching the UMV to drive itself down stairs in sim results in a real robot that can handle stairs at any angle.Robotics and AI Institute

Just a few weeks ago, the RAI Institute announced a new partnership with Boston Dynamics “to advance humanoid robots through reinforcement learning.” Humanoids are just another kind of robotic platform, albeit a significantly more complicated one with many more degrees of freedom and things to model and simulate. But when considering the limitations of model predictive control for this level of complexity, a reinforcement learning approach seems almost inevitable, especially when such an approach is already streamlined due to its ability to generalize.

“One of the ambitions that we have as an institute is to have solutions which span across all kinds of different platforms,” says Hutter. “It’s about building tools, about building infrastructure, building the basis for this to be done in a broader context. So not only humanoids, but driving vehicles, quadrupeds, you name it. But doing RL research and showcasing some nice first proof of concept is one thing—pushing it to work in the real world under all conditions, while pushing the boundaries in performance, is something else.”

Transferring skills into the real world has always been a challenge for robots trained in simulation, precisely because simulation is so friendly to robots. “If you spend enough time,” Farshidian explains, “you can come up with a reward function where eventually the robot will do what you want. What often fails is when you want to transfer that sim behavior to the hardware, because reinforcement learning is very good at finding glitches in your simulator and leveraging them to do the task.”

Simulation has been getting much, much better, with new tools, more accurate dynamics, and lots of computing power to throw at the problem. “It’s a hugely powerful ability that we can simulate so many things, and generate so much data almost for free,” Hutter says. But the usefulness of that data is in its connection to reality, making sure that what you’re simulating is accurate enough that a reinforcement learning approach will in fact solve for reality. Bringing physical data collected on real hardware back into the simulation, Hutter believes, is a very promising approach, whether it’s applied to running quadrupeds or jumping bicycles or humanoids. “The combination of the two—of simulation and reality—that’s what I would hypothesize is the right direction.”

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!

[ Meta PARTNR ]

[ Humanoid ]

[ Faboratory Research ] at [ Yale University ]

[ NASA ]

[ Mentee Robotics ]

[ Cybathlon ]

[ Paper ] via [ Mitsubishi Electric Research Laboratories ]

Thanks, Yuki!

[ Waymo ]

One of a humanoid robot’s unique advantages lies in its bipedal mobility, allowing it to navigate diverse terrains with efficiency and agility. This capability enables Moby to move freely through various environments and assist with high-risk tasks in critical industries like construction, mining, and energy.

[ UCR ]

[ Pollen Robotics ]

[ OTTO ] by [ Rockwell Automation ]

This Carnegie Mellon University Robotics Institute Seminar is from CMU’s Aaron Johnson, on “Uncertainty and Contact with the World.”

[ CMU RI ]

In theory, one of the main applications for robots should be operating in environments that (for whatever reason) are too dangerous for humans. I say “in theory” because in practice it’s difficult to get robots to do useful stuff in semi-structured or unstructured environments without direct human supervision. This is why there’s been some emphasis recently on teleoperation: Human software teaming up with robot hardware can be a very effective combination.

For this combination to work, you need two things. First, an intuitive control system that lets the user embody themselves in the robot to pilot it effectively. And second, a robot that can deliver on the kind of embodiment that the human pilot needs. The second bit is the more challenging, because humans have very high standards for mobility, strength, and dexterity. But researchers at the Italian Institute of Technology (IIT) have a system that manages to check both boxes, thanks to its enormously powerful quadruped, which now sports a pair of massive arms on its head.

“The primary goal of this project, conducted in collaboration with INAIL, is to extend human capabilities to the robot, allowing operators to perform complex tasks remotely in hazardous and unstructured environments to mitigate risks to their safety by exploiting the robot’s capabilities,” explains Claudio Semini, who leads the Robot Teleoperativo project at IIT. The project is based around the HyQReal hydraulic quadruped, the most recent addition to IIT’s quadruped family.

Hydraulics have been very visibly falling out of favor in robotics, because they’re complicated and messy, and in general robots don’t need the absurd power density that comes with hydraulics. But there are still a few robots in active development that use hydraulics specifically because of all that power. If your robot needs to be highly dynamic or lift really heavy things, hydraulics are, at least for now, where it’s at.

IIT’s HyQReal quadruped is one of those robots. If you need something that can carry a big payload, like a pair of massive arms, this is your robot. Back in 2019, we saw HyQReal pulling a three-tonne airplane. HyQReal itself weighs 140 kilograms, and its knee joints can output up to 300 newton-meters of torque. The hydraulic system is powered by onboard batteries and can provide up to 4 kilowatts of power. It also uses some of Moog’s lovely integrated smart actuators, which sadly don’t seem to be in development anymore. Beyond just lifting heavy things, HyQReal’s mass and power make it a very stable platform, and its aluminum roll cage and Kevlar skin ensure robustness.

The HyQReal hydraulic quadruped is tethered for power during experiments at IIT, but it can also run on battery power.IIT

The arms that HyQReal is carrying are IIT-INAIL arms, which weigh 10 kg each and have a payload of 5 kg per arm. To put that in perspective, the maximum payload of a Boston Dynamics Spot robot is only 14 kg. The head-mounted configuration of the arms means they can reach the ground, and they also have an overlapping workspace to enable bimanual manipulation, which is enhanced by HyQReal’s ability to move its body to assist the arms with their reach. “The development of core actuation technologies with high power, low weight, and advanced control has been a key enabler in our efforts,” says Nikos Tsagarakis, head of the HHCM Lab at IIT. “These technologies have allowed us to realize a low-weight bimanual manipulation system with high payload capacity and large workspace, suitable for integration with HyQReal.”

Maximizing reachable space is important, because the robot will be under the remote control of a human, who probably has no particular interest in or care for mechanical or power constraints—they just want to get the job done.

To get the job done, IIT has developed a teleoperation system, which is weird-looking because it features a very large workspace that allows the user to leverage more of the robot’s full range of motion. Having tried a bunch of different robotic telepresence systems, I can vouch for how important this is: It’s super annoying to be doing some task through telepresence, and then hit a joint limit on the robot and have to pause to reset your arm position. “That is why it is important to study operators’ quality of experience. It allows us to design the haptic and teleoperation systems appropriately because it provides insights into the levels of delight or frustration associated with immersive visualization, haptic feedback, robot control, and task performance,” confirms Ioannis Sarakoglou, who is responsible for the development of the haptic teleoperation technologies in the HHCM Lab. The whole thing takes place in a fully immersive VR environment, of course, with some clever bandwidth optimization inspired by the way humans see that transmits higher-resolution images only where the user is looking.

HyQReal’s telepresence control system offers haptic feedback and a large workspace.IIT

The system is designed to be used in hazardous environments where you wouldn’t want to send a human. That’s why IIT’s partner on this project is INAIL, Italy’s National Institute for Insurance Against Accidents at Work, which is understandably quite interested in finding ways in which robots can be used to keep humans out of harm’s way.

In tests with Italian firefighters in 2022 (using an earlier version of the robot with a single arm), operators were able to use the system to extinguish a simulated tunnel fire. It’s a good first step, but Semini has plans to push the system to handle “more complex, heavy, and demanding tasks, which will better show its potential for real-world applications.”

As always with robots and real-world applications, there’s still a lot of work to be done, Semini says. “The reliability and durability of the systems in extreme environments have to be improved,” he says. “For instance, the robot must endure intense heat and prolonged flame exposure in firefighting without compromising its operational performance or structural integrity.” There’s also managing the robot’s energy consumption (which is not small) to give it a useful operating time, and managing the amount of information presented to the operator to boost situational awareness while still keeping things streamlined and efficient. “Overloading operators with too much information increases cognitive burden, while too little can lead to errors and reduce situational awareness,” says Yonas Tefera, who lead the development of the immersive interface. “Advances in immersive mixed-reality interfaces and multimodal controls, such as voice commands and eye tracking, are expected to improve efficiency and reduce fatigue in the future.”

There’s a lot of crossover here with the goals of the DARPA Robotics Challenge for humanoid robots, except IIT’s system is arguably much more realistically deployable than any of those humanoids are, at least in the near term. While we’re just starting to see the potential of humanoids in carefully controlled environment, quadrupeds are already out there in the world, proving how reliable their four-legged mobility is. Manipulation is the obvious next step, but it has to be more than just opening doors. We need it to use tools, lift debris, and all that other DARPA Robotics Challenge stuff that will keep humans safe. That’s what Robot Teleoperativo is trying to make real.

You can find more detail about the Robot Teleoperativo project in this paper, presented in November at the 2024 IEEE Conference on Telepresence, in Pasadena, Calif.

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!

[ ASAP ] from [ Carnegie Mellon University ] and [ Nvidia ]

[ RIVR ]

[ UCR ]

[ Autonomous Robots Lab ]

Thanks, Kostas!

[ Giraffe ] via [ TechCrunch ]

IROS 2025, coming this fall in Hangzhou, China.

[ IROS 2025 ]

This cute lil guy is from a “Weak Robots Exhibition” in Japan.

[ RobotStart ]

I see no problem with cheating via infrastructure to make autonomous vehicles more reliable.

[ Oak Ridge National Laboratory ]

I am not okay with how this coffee cup is handled. Neither is my editor.

[ Qb Robotics ]

[ Paper ] from [ ETH Zurich and Instituto Italiano de Technologia ]

[ Verity ]

For some reason, KUKA is reaffirming its commitment to environmental responsibility and diversity.

[ KUKA ]

Here’s a panel from the recent Humanoids Summit on generative AI for robotics, which includes panelists from OpenAI and Agility Robotics. Just don’t mind the moderator, he’s a bit of a dork.

[ Humanoids Summit ]

The 2004 DARPA Grand Challenge was a spectacular failure. The Defense Advanced Research Projects Agency had offered a US $1 million prize for the team that could design an autonomous ground vehicle capable of completing an off-road course through sometimes flat, sometimes winding and mountainous desert terrain. As IEEE Spectrum reported at the time, it was “the motleyest assortment of vehicles assembled in one place since the filming of Mad Max 2: The Road Warrior.” Not a single entrant made it across the finish line. Some didn’t make it out of the parking lot.

Videos of the attempts are comical, although any laughter comes at the expense of the many engineers who spent countless hours and millions of dollars to get to that point.

So it’s all the more remarkable that in the second DARPA Grand Challenge, just a year and a half later, five vehicles crossed the finish line. Stanley, developed by the Stanford Racing Team, eked out a first-place win to claim the $2 million purse. This modified Volkswagen Touareg [shown at top] completed the 212-kilometer course in 6 hours, 54 minutes. Carnegie Mellon’s Sandstorm and H1ghlander took second and third place, respectively, with times of 7:05 and 7:14.

Kat-5, sponsored by the Gray Insurance Co. of Metairie, La., came in fourth with a respectable 7:30. The vehicle was named after Hurricane Katrina, which had just pummeled the Gulf Coast a month and a half earlier. Oshkosh Truck’s TerraMax also finished the circuit, although its time of 12:51 exceeded the 10-hour time limit set by DARPA.

So how did the Grand Challenge go from a total bust to having five robust finishers in such a short period of time? It’s definitely a testament to what can be accomplished when engineers rise to a challenge. But the outcome of this one race was preceded by a much longer path of research, and that plus a little bit of luck are what ultimately led to victory.

Let’s back up to 1998, when computer scientist Sebastian Thrun was working at Carnegie Mellon and experimenting with a very different robot: a museum tour guide. For two weeks in the summer, Minerva, which looked a bit like a Dalek from “Doctor Who,” navigated an exhibit at the Smithsonian National Museum of American History. Its main task was to roll around and dispense nuggets of information about the displays.

Minerva was a museum tour-guide robot developed by Sebastian Thrun.

In an interview at the time, Thrun acknowledged that Minerva was there to entertain. But Minerva wasn’t just a people pleaser ; it was also a machine learning experiment. It had to learn where it could safely maneuver without taking out a visitor or a priceless artifact. Visitor, nonvisitor; display case, not-display case; open floor, not-open floor. It had to react to humans crossing in front of it in unpredictable ways. It had to learn to “see.”

Fast-forward five years: Thrun transferred to Stanford in July 2003. Inspired by the first Grand Challenge, he organized the Stanford Racing Team with the aim of fielding a robotic car in the second competition.

In a vast oversimplification of Stanley’s main task, the autonomous robot had to differentiate between road and not-road in order to navigate the route successfully. The Stanford team decided to focus its efforts on developing software and used as much off-the-shelf hardware as they could, including a laser to scan the immediate terrain and a simple video camera to scan the horizon. Software overlapped the two inputs, adapted to the changing road conditions on the fly, and determined a safe driving speed. (For more technical details on Stanley, check out the team’s paper.) A remote-control kill switch, which DARPA required on all vehicles, would deactivate the car before it could become a danger. About 100,000 lines of code did that and much more.

The Stanford team hadn’t entered the 2004 Grand Challenge and wasn’t expected to win the 2005 race. Carnegie Mellon, meanwhile, had two entries—a modified 1986 Humvee and a modified 1999 Hummer—and was the clear favorite. In the 2004 race, CMU’s Sandstorm had gone furthest, completing 12 km. For the second race, CMU brought an improved Sandstorm as well as a new vehicle, H1ghlander.

Many of the other 2004 competitors regrouped to try again, and new ones entered the fray. In all, 195 teams applied to compete in the 2005 event. Teams included students, academics, industry experts, and hobbyists.

After site visits in the spring, 43 teams made it to the qualifying event, held 27 September through 5 October at the California Speedway, in Fontana. Each vehicle took four runs through the course, navigating through checkpoints and avoiding obstacles. A total of 23 teams were selected to attempt the main course across the Mojave Desert. Competing was a costly endeavor—CMU’s Red Team spent more than $3 million in its first year—and the names of sponsors were splashed across the vehicles like the logos on race cars.

In the early hours of 8 October, the finalists gathered for the big race. Each team had a staggered start time to help avoid congestion along the route. About two hours before a team’s start, DARPA gave them a CD containing approximately 3,000 GPS coordinates representing the course. Once the team hit go, it was hands off: The car had to drive itself without any human intervention. PBS’s NOVA produced an excellent episode on the 2004 and 2005 Grand Challenges that I highly recommend if you want to get a feel for the excitement, anticipation, disappointment, and triumph.

In the 2005 Grand Challenge, Carnegie Mellon University’s H1ghlander was one of five autonomous cars to finish the race.Damian Dovarganes/AP

H1ghlander held the pole position, having placed first in the qualifying rounds, followed by Stanley and Sandstorm. H1ghlander pulled ahead early and soon had a substantial lead. That’s where luck, or rather the lack of it, came in.

About two hours into the race, H1ghlander slowed down and started rolling backward down a hill. Although it eventually resumed moving forward, it never regained its top speed, even on long, straight, level sections of the course. The slower but steadier Stanley caught up to H1ghlander at the 163-km (101.5-mile) marker, passed it, and never let go of the lead.

What went wrong with H1ghlander remained a mystery, even after extensive postrace analysis. It wasn’t until 12 years after the race—and once again with a bit of luck—that CMU discovered the problem: Pressing on a small electronic filter between the engine control module and the fuel injector caused the engine to lose power and even turn off. Team members speculated that an accident a few weeks before the competition had damaged the filter. (To learn more about how CMU finally figured this out, see Spectrum Senior Editor Evan Ackerman’s 2017 story.)

Regardless of who won the Grand Challenge, many success stories came out of the contest. A year and a half after the race, Thrun had already made great progress on adaptive cruise control and lane-keeping assistance, which is now readily available on many commercial vehicles. He then worked on Google’s Street View and its initial self-driving cars. CMU’s Red Team worked with NASA to develop rovers for potentially exploring the moon or distant planets. Closer to home, they helped develop self-propelled harvesters for the agricultural sector.

Stanford team leader Sebastian Thrun holds a $2 million check, the prize for winning the 2005 Grand Challenge.Damian Dovarganes/AP

Of course, there was also a lot of hype, which tended to overshadow the race’s militaristic origins—remember, the “D” in DARPA stands for “defense.” Back in 2000, a defense authorization bill had stipulated that one-third of the U.S. ground combat vehicles be “unmanned” by 2015, and DARPA conceived of the Grand Challenge to spur development of these autonomous vehicles. The U.S. military was still fighting in the Middle East, and DARPA promoters believed self-driving vehicles would help minimize casualties, particularly those caused by improvised explosive devices.

DARPA sponsored more contests, such as the 2007 Urban Challenge, in which vehicles navigated a simulated city and suburban environment; the 2012 Robotics Challenge for disaster-response robots; and the 2022 Subterranean Challenge for—you guessed it—robots that could get around underground. Despite the competitions, continued military conflicts, and hefty government contracts, actual advances in autonomous military vehicles and robots did not take off to the extent desired. As of 2023, robotic ground vehicles made up only 3 percent of the global armored-vehicle market.

Today, there are very few fully autonomous ground vehicles in the U.S. military; instead, the services have forged ahead with semiautonomous, operator-assisted systems, such as remote-controlled drones and ship autopilots. The one Grand Challenge finisher that continued to work for the U.S. military was Oshkosh Truck, the Wisconsin-based sponsor of the TerraMax. The company demonstrated a palletized loading system to transport cargo in unmanned vehicles for the U.S. Army.

Much of the contemporary reporting on the Grand Challenge predicted that self-driving cars would take us closer to a “Jetsons” future, with a self-driving vehicle to ferry you around. But two decades after Stanley, the rollout of civilian autonomous cars has been confined to specific applications, such as Waymo robotaxis transporting people around San Francisco or the GrubHub Starships struggling to deliver food across my campus at the University of South Carolina.

I’ll be watching to see how the technology evolves outside of big cities. Self-driving vehicles would be great for long distances on empty country roads, but parts of rural America still struggle to get adequate cellphone coverage. Will small towns and the spaces that surround them have the bandwidth to accommodate autonomous vehicles? As much as I’d like to think self-driving autos are nearly here, I don’t expect to find one under my carport anytime soon.

Not long after the 2005 race, Stanley was ready to retire. Recalling his experience testing Minerva at the National Museum of American History, Thrun thought the museum would make a nice home. He loaned it to the museum in 2006, and since 2008 it has resided permanently in the museum’s collections, alongside other remarkable specimens in robotics and automobiles. In fact, it isn’t even the first Stanley in the collection.

Stanley now resides in the collections of the Smithsonian Institution’s National Museum of American History, which also houses another Stanley—this 1910 Stanley Runabout. Behring Center/National Museum of American History/Smithsonian Institution

That distinction belongs to a 1910 Stanley Runabout, an early steam-powered car introduced at a time when it wasn’t yet clear that the internal-combustion engine was the way to go. Despite clear drawbacks—steam engines had a nasty tendency to explode—“Stanley steamers” were known for their fine craftsmanship. Fred Marriott set the land speed record while driving a Stanley in 1906. It clocked in at 205.5 kilometers per hour, which was significantly faster than the 21st-century Stanley’s average speed of 30.7 km/hr. To be fair, Marriott’s Stanley was racing over a flat, straight course rather than the off-road terrain navigated by Thrun’s Stanley.

Across the century that separates the two Stanleys, it’s easy to trace a narrative of progress. Both are clearly recognizable as four-wheeled land vehicles, but I suspect the science-fiction dreamers of the early 20th century would have been hard-pressed to imagine the suite of technologies that would propel a 21st-century self-driving car. What will the vehicles of the early 22nd century be like? Will they even have four tires, or will they run on something entirely new?

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 February 2025 print issue as “Slow and Steady Wins the Race.”

Sebastian Thrun and his colleagues at the Stanford Artificial Intelligence Laboratory, along with members of the other groups that sponsored Stanley, published “Stanley: The Robot That Won the DARPA Grand Challenge.” This paper, from the Journal of Field Robotics, explains the vehicle’s development.

The NOVA PBS episode “The Great Robot Race” provides interviews and video footage from both the failed first Grand Challenge and the successful second one. I personally liked the side story of GhostRider, an autonomous motorcycle that competed in both competitions but didn’t quite cut it. (GhostRider also now resides in the Smithsonian’s collection.)

Smithsonian curator Carlene Stephens kindly talked with me about how she collected Stanley for the National Museum of American History and where she sees artifacts like this fitting into the stream of history.

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!

This video about ‘foster’ Aibos helping kids at a children’s hospital is well worth turning on auto-translated subtitles for.

[ Aibo Foster Program ]

[ Unitree ]

Happy Chinese New Year from PNDbotics!

[ PNDbotics ]

[ LimX Dynamics ]

Mr. Bucket is my favorite.

[ Mitsubishi Electric Research Laboratories ]

Thanks, Yuki!

[ Zoox ]

The Humanoids Summit at the Computer History Museum in December was successful enough (either because of or in spite of my active participation) that it’s not only happening again in 2025: There’s also going to be a spring version of the conference in London in May!

[ Humanoids Summit ]

I’m not sure it’ll ever be practical at scale, but I do really like JSK’s musculoskeletal humanoid work.

[ Paper ]

[ Canadian Space Agency ]

[ Corvus Robotics ]

Thanks, Joan!

I would have said that this controller was too small to be manipulated with a pinch grasp. I would be wrong.

[ Pollen ]

[ NASA ]

[ Team BlackSheep ]

[ Zoox ]

Most people know that robots no longer sound like tinny trash cans. They sound like Siri, Alexa, and Gemini. They sound like the voices in labyrinthine customer support phone trees. And even those robot voices are being made obsolete by new AI-generated voices that can mimic every vocal nuance and tic of human speech, down to specific regional accents. And with just a few seconds of audio, AI can now clone someone’s specific voice.

This technology will replace humans in many areas. Automated customer support will save money by cutting staffing at call centers. AI agents will make calls on our behalf, conversing with others in natural language. All of that is happening, and will be commonplace soon.

But there is something fundamentally different about talking with a bot as opposed to a person. A person can be a friend. An AI cannot be a friend, despite how people might treat it or react to it. AI is at best a tool, and at worst a means of manipulation. Humans need to know whether we’re talking with a living, breathing person or a robot with an agenda set by the person who controls it. That’s why robots should sound like robots.

You can’t just label AI-generated speech. It will come in many different forms. So we need a way to recognize AI that works no matter the modality. It needs to work for long or short snippets of audio, even just a second long. It needs to work for any language, and in any cultural context. At the same time, we shouldn’t constrain the underlying system’s sophistication or language complexity.

We have a simple proposal: all talking AIs and robots should use a ring modulator. In the mid-twentieth century, before it was easy to create actual robotic-sounding speech synthetically, ring modulators were used to make actors’ voices sound robotic. Over the last few decades, we have become accustomed to robotic voices, simply because text-to-speech systems were good enough to produce intelligible speech that was not human-like in its sound. Now we can use that same technology to make robotic speech that is indistinguishable from human sound robotic again.

A ring modulator has several advantages: It is computationally simple, can be applied in real-time, does not affect the intelligibility of the voice, and--most importantly--is universally “robotic sounding” because of its historical usage for depicting robots.

Responsible AI companies that provide voice synthesis or AI voice assistants in any form should add a ring modulator of some standard frequency (say, between 30-80 Hz) and of a minimum amplitude (say, 20 percent). That’s it. People will catch on quickly.

Here are a couple of examples you can listen to for examples of what we’re suggesting. The first clip is an AI-generated “podcast” of this article made by Google’s NotebookLM featuring two AI “hosts.” Google’s NotebookLM created the podcast script and audio given only the text of this article. The next two clips feature that same podcast with the AIs’ voices modulated more and less subtly by a ring modulator:

Raw audio sample generated by Google’s NotebookLM Your browser does not support the audio element.

Audio sample with added ring modulator (30 Hz-25%) Your browser does not support the audio element.

Audio sample with added ring modulator (30 Hz-40%) Your browser does not support the audio element.

We were able to generate the audio effect with a 50-line Python script generated by Anthropic’s Claude. One of the most well-known robot voices were those of the Daleks from Doctor Who in the 1960s. Back then robot voices were difficult to synthesize, so the audio was actually an actor’s voice run through a ring modulator. It was set to around 30 Hz, as we did in our example, with different modulation depth (amplitude) depending on how strong the robotic effect is meant to be. Our expectation is that the AI industry will test and converge on a good balance of such parameters and settings, and will use better tools than a 50-line Python script, but this highlights how simple it is to achieve.

Of course there will also be nefarious uses of AI voices. Scams that use voice cloning have been getting easier every year, but they’ve been possible for many years with the right know-how. Just like we’re learning that we can no longer trust images and videos we see because they could easily have been AI-generated, we will all soon learn that someone who sounds like a family member urgently requesting money may just be a scammer using a voice-cloning tool.

We don’t expect scammers to follow our proposal: They’ll find a way no matter what. But that’s always true of security standards, and a rising tide lifts all boats. We think the bulk of the uses will be with popular voice APIs from major companies--and everyone should know that they’re talking with a robot.

This article is part of our exclusive IEEE Journal Watch series in partnership with IEEE Xplore.

Swarms of autonomous robots are increasingly being tested and deployed in complex missions, yet a certain level of human oversight during these missions is still required. Which means a major question remains: How many robots—and how complex a mission—can a single human manage before becoming overwhelmed?

In a study funded by the U.S. Defense Advanced Research Projects Agency (DARPA), experts show that humans can single-handedly and effectively manage a heterogenous swarm of more than 100 autonomous ground and aerial vehicles, while feeling overwhelmed only for brief periods of time during an overall small portion of the mission. For instance, in a particularly challenging, multi-day experiment in an urban setting, human controllers were overloaded with stress and workload only three percent of the time. The results were published 19 November in IEEE Transactions on Field Robotics.

Julie A. Adams, the associate director of research at Oregon State University’s Collaborative Robotics and Intelligent Systems Institute, has been studying human interactions with robots and other complex systems, such as aircraft cockpits and nuclear power plant control rooms, for 35 years. She notes that robot swarms can be used to support missions where work may be particularly dangerous and hazardous for humans, such as monitoring wildfires.

“Swarms can be used to provide persistent coverage of an area, such as monitoring for new fires or looters in the recently burned areas of Los Angeles,” Adams says. “The information can be used to direct limited assets, such as firefighting units or water tankers to new fires and hotspots, or to locations at which fires were thought to have been extinguished.”

These kinds of missions can involve a mix of many different kinds of unmanned ground vehicles (such as the Aion Robotics R1 wheeled robot) and aerial autonomous vehicles (like the Modal AI VOXL M500 quadcopter), and a human controller may need to reassign individual robots to different tasks as the mission unfolds. Notably, some theories over the past few decades—and even Adams’ early thesis work—suggest that a single human has limited capacity to deploy very large numbers of robots.

“These historical theories and the associated empirical results showed that as the number of ground robots increased, so did the human’s workload, which often resulted in reduced overall performance,” says Adams, noting that, although earlier research focused on unmanned ground vehicles (UGVs), which must deal with curbs and other physical barriers, unmanned aerial vehicles (UAVs) often encounter fewer physical barriers.

Human controllers managed their swarms of autonomous vehicles with a virtual display. The fuschia ring represents the area the person could see within their head-mounted display.DARPA

As part of DARPA’s OFFensive Swarm-Enabled Tactics (OFFSET) program, Adams and her colleagues sought to explore whether these theories applied to very complex missions involving a mix of unmanned ground and air vehicles. In November 2021, at Fort Campbell in Kentucky, two human controllers took turns engaging in a series of missions over the course of three weeks with the objective of neutralizing an adversarial target. Both human controllers had significant experience controlling swarms, and participated in alternating shifts that ranged from 1.5 to 3 hours per day.

During the tests, the human controllers were positioned in a designated area on the edge of the testing site, and used a virtual reconstruction of the environment to keep tabs on where vehicles were and what tasks they were assigned to.

The largest mission shift involved 110 drones, 30 ground vehicles, and up to 50 virtual vehicles representing additional real-world vehicles. The robots had to navigate through the physical urban environment, as well as a series of virtual hazards represented using AprilTags—simplified QR codes that could represent imaginary hazards—that were scattered throughout the mission site.

DARPA made the final field exercise exceptionally challenging by providing thousands of hazards and pieces of information to inform the search. “The complexity of the hazards was significant,” Adams says, noting that some hazards required multiple robots to interact with them simultaneously, and some hazards moved around the environment.

Throughout each mission shift, the human controller’s physiological responses to the tasks at hand were monitored. For example, sensors collected data on their heart-rate variability, posture, and even their speech rate. The data were input into an established algorithm that estimates workload levels and was used to determine when the controller was reaching a workload level that exceeded a normal range, called an “overload state.”

Adams notes that, despite the complexity and large volume of robots to manage in this field exercise, the number and duration of overload state instances were relatively short—a handful of minutes during a mission shift. “The total percentage of estimated overload states was 3 percent of all workload estimates across all shifts for which we collected data,” she says.

www.youtube.com

The most common reason for a human commander to reach an overload state is when they had to generate multiple new tactics or inspect which vehicles in the launch zone were available for deployment.

Adams notes that these finding suggest that—counter to past theories—the number of robots may be less influential on human swarm control performance than previously thought. Her team is exploring the other factors that may impact swarm control missions, such as other human limitations, system designs and UAS designs, the results of which will potentially inform US Federal Aviation Administration drone regulations, she says.