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. One Controller for Many Drones and Robots “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. Tests on Ukraine Battlefields to Begin Soon “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. What are the advances of Gemini Robotics? 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 the 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. What Is Embodied Reasoning? 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. DeepMind’s Approach to Robotic Safety 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. DeepMind’s Robotic Partnerships 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. Why Is Undergrounding So Expensive? 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.” Earthworm-Inspired Robotics for Power Lines 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. Advancements in Burrowing Precision “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. RoboCup German Open: 12–16 March 2025, NUREMBERG, GERMANY German Robotics Conference: 13–15 March 2025, NUREMBERG, GERMANY European Robotics Forum: 25–27 March 2025, STUTTGART, GERMANY RoboSoft 2025: 23–26 April 2025, LAUSANNE, SWITZERLAND ICUAS 2025: 14–17 May 2025, CHARLOTTE, NC ICRA 2025: 19–23 May 2025, ATLANTA, GA London Humanoids Summit: 29–30 May 2025, LONDON IEEE RCAR 2025: 1–6 June 2025, TOYAMA, JAPAN 2025 Energy Drone & Robotics Summit: 16–18 June 2025, HOUSTON, TX RSS 2025: 21–25 June 2025, LOS ANGELES ETH Robotics Summer School: 21–27 June 2025, GENEVA IAS 2025: 30 June–4 July 2025, GENOA, ITALY ICRES 2025: 3–4 July 2025, PORTO, PORTUGAL IEEE World Haptics: 8–11 July 2025, SUWON, KOREA IFAC Symposium on Robotics: 15–18 July 2025, PARIS RoboCup 2025: 15–21 July 2025, BAHIA, BRAZIL Enjoy today’s videos! Last year, we unveiled the new Atlas—faster, stronger, more compact, and less messy. We’re designing the world’s most dynamic humanoid robot to do anything and everything, but we get there one step at a time. Our first task is part sequencing, a common logistics task in automotive manufacturing. Discover why we started with sequencing, how we are solving hard problems, and how we’re delivering a humanoid robot with real value. 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 ] Professor Patrícia Alves-Oliveira is studying authenticity of artworks co-created by an artist and a robot. Her research lab, Robot Studio, is developing methods to authenticate artwork by analyzing their entire creative process. This is accomplished by using the artist’s biometrics as well as the process of artwork creation, from the first brushstroke to the final painting. This work aims to bring ownership back to artists in the age of generative AI. [ 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 ] In this demo, Reachy 2 autonomously sorts healthy and unhealthy foods. No machine learning, no pre-trained AI—just real-time object detection! [ Pollen ] Biological snakes achieve high mobility with numerous joints, inspiring snake-like robots for rescue and inspection. However, conventional designs feature a limited number of joints. This paper presents an underactuated snake robot consisting of many passive links that can dynamically change its joint coupling configuration by repositioning motor-driven joint units along internal rack gears. Furthermore, a soft robot skin wirelessly powers the units, eliminating wire tangling and disconnection risks. [ Paper ] Thanks, Ayato! Tech United Eindhoven is working on quadrupedal soccer robots, which should be fun. [ Tech United ] Autonomous manipulation in everyday tasks requires flexible action generation to handle complex, diverse real-world environments, such as objects with varying hardness and softness. Imitation Learning (IL) enables robots to learn complex tasks from expert demonstrations. However, a lot of existing methods rely on position/unilateral control, leaving challenges in tasks that require force information/control, like carefully grasping fragile or varying-hardness objects. To address these challenges, we introduce Bilateral Control-Based Imitation Learning via Action Chunking with Transformers(Bi-ACT) and”A” “L”ow-cost “P”hysical “Ha”rdware Considering Diverse Motor Control Modes for Research in Everyday Bimanual Robotic Manipulation (ALPHA-α). [ Alpha-Biact ] Thanks, Masato! Powered by UBTECH’s revolutionary framework “BrainNet”, a team of Walker S1 humanoid robots work together to master complex tasks at Zeekr’s Smart Factory! Teamwork makes the dream of robots work. [ UBTECH ] Personal mobile robotic assistants are expected to find wide applications in industry and healthcare. However, manually steering a robot while in motion requires significant concentration from the operator, especially in tight or crowded spaces. This work presents a virtual leash with which a robot can naturally follow an operator. We successfully validate on the ANYmal platform the robustness and performance of our entire pipeline in real-world experiments. [ 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 ] Researchers at Penn Engineering have discovered that certain features of AI-governed robots carry security vulnerabilities and weaknesses that were previously unidentified and unknown. Funded by the National Science Foundation and the Army Research Laboratory, the research aims to address the emerging vulnerability for ensuring the safe deployment of large language models (LLMs) in robotics. [ RoboPAIR ] ReachBot is a joint project between Stanford and NASA to explore a new approach to mobility in challenging environments such as martian caves. It consists of a compact robot body with very long extending arms, based on booms used for extendable antennas. The booms unroll from a coil and can extend many meters in low gravity. In this talk I will introduce the ReachBot design and motion planning considerations, report on a field test with a single ReachBot arm in a lava tube in the Mojave Desert, and discuss future plans, which include the possibility of mounting one or more ReachBot arms equipped with wrists and grippers on a mobile platform – such as ANYMal. [ 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 University of California San Diego. Mercier’s student Zixiao Lin described the new circuit last month at 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 A-h/L, but are about 1000 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. Stacking Batteries on the Fly The solid-state battery’s ability to be diced up into tiny cells suggested that researchers 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-mm2 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 Adiabatic Charging 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-volt 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 volts, 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.”