The RoboBee is only slightly larger than a penny. Credit: Harvard Microrobotics Laboratory
The first step was to perform experiments to determine the effects of oscillation on the newly designed robotic legs and leg joints. This involved manually disturbing the leg and then releasing it, capturing the resulting oscillations on high-speed video. This showed that the leg and joint essentially acted as an “underdamped spring-mass-damper model,” with a bit of “viscoelastic creep” for good measure. Next, the team performed a series of free-fall experiments with small fiberglass crash-test dummy vehicles with mass and inertia similar to RoboBee’s, capturing each free fall on high-speed video. This was followed by tests of different takeoff and landing approaches.
The final step was running experiments on consecutive takeoff and landing sequences using RoboBee, with the little robot taking off from one leaf, hovering, then moving laterally before hovering briefly and landing on another leaf nearby. The basic setup was the same as prior experiments, with the exception of placing a plant branch in the motion-capture arena. RoboBee was able to safely land on the second leaf (or similar uneven surfaces) over repeated trials with varying parameters.
Going forward, Wood’s team will seek to further improve the mechanical damping upon landing, drawing lessons from stingless bees and mosquitoes, as well as scaling up to larger vehicles. This would require an investigation into more complex leg geometries, per the authors. And RoboBee still needs to be tethered to off-board control systems. The team hopes one day to incorporate onboard electronics with built-in sensors.
“The longer-term goal is full autonomy, but in the interim we have been working through challenges for electrical and mechanical components using tethered devices,” said Wood. “The safety tethers were, unsurprisingly, getting in the way of our experiments, and so safe landing is one critical step to remove those tethers.” This would make RoboBee more viable for a range of practical applications, including environmental monitoring, disaster surveillance, or swarms of RoboBees engaged in artificial pollination.
It’s well understood that spiders have poor eyesight and thus sense the vibrations in their webs whenever prey (like a fly) gets caught; the web serves as an extension of their sensory system. But spiders also exhibit less-understood behaviors to locate struggling prey. Most notably, they take on a crouching position, sometimes moving up and down to shake the web or plucking at the web by pulling in with one leg. The crouching seems to be triggered when prey is stationary and stops when the prey starts moving.
But it can be difficult to study the underlying mechanisms of this behavior because there are so many variables at play when observing live spiders. To simplify matters, researchers at Johns Hopkins University’s Terradynamics Laboratory are building crouching spider robots and testing them on synthetic webs. The results provide evidence for the hypothesis that spiders crouch to sense differences in web frequencies to locate prey that isn’t moving—something analogous to echolocation. The researchers presented their initial findings today at the American Physical Society’s Global Physics Summit in Anaheim, California.
“Our lab investigates biological problems using robot physical models,” team member Eugene Lin told Ars. “Animal experiments are really hard to reproduce because it’s hard to get the animal to do what you want to do.” Experiments with robot physical models, by contrast, “are completely repeatable. And while you’re building them, you get a better idea of the actual [biological] system and how certain behaviors happen.” The lab has also built robots inspired by cockroaches and fish.
The research was done in collaboration with two other labs at JHU. Andrew Gordus’ lab studies spider behavior, particularly how they make their webs, and provided biological expertise as well as videos of the particular spider species (U. diversus) of interest. Jochen Mueller’s lab provided expertise in silicone molding, allowing the team to use their lab to 3D-print their spider robot’s flexible joints.
Crouching spider, good vibrations
A spider exhibiting crouching behavior. Credit: YouTube/Terradynamics Lab/JHU
The first spider robot model didn’t really move or change its posture; it was designed to sense vibrations in the synthetic web. But Lin et al. later modified it with actuators so it could move up and down. Also, there were only four legs, with two joints in each and two accelerometers on each leg; real spiders have eight legs and many more joints. But the model was sufficient for experimental proof of principle. There was also a stationary prey robot.
Biohybrid robots work by combining biological components like muscles, plant material, and even fungi with non-biological materials. While we are pretty good at making the non-biological parts work, we’ve always had a problem with keeping the organic components alive and well. This is why machines driven by biological muscles have always been rather small and simple—up to a couple centimeters long and typically with only a single actuating joint.
“Scaling up biohybrid robots has been difficult due to the weak contractile force of lab-grown muscles, the risk of necrosis in thick muscle tissues, and the challenge of integrating biological actuators with artificial structures,” says Shoji Takeuchi, a professor at the Tokyo University, Japan. Takeuchi led a research team that built a full-size, 18 centimeter-long biohybrid human-like hand with all five fingers driven by lab-grown human muscles.
Keeping the muscles alive
Out of all the roadblocks that keep us from building large-scale biohybrid robots, necrosis has probably been the most difficult to overcome. Growing muscles in a lab usually means a liquid medium to supply nutrients and oxygen to muscle cells seeded on petri dishes or applied to gel scaffoldings. Since these cultured muscles are small and ideally flat, nutrients and oxygen from the medium can easily reach every cell in the growing culture.
When we try to make the muscles thicker and therefore more powerful, cells buried deeper in those thicker structures are cut off from nutrients and oxygen, so they die, undergoing necrosis. In living organisms, this problem is solved by the vascular network. But building artificial vascular networks in lab-grown muscles is still something we can’t do very well. So, Takeuchi and his team had to find their way around the necrosis problem. Their solution was sushi rolling.
The team started by growing thin, flat muscle fibers arranged side by side on a petri dish. This gave all the cells access to nutrients and oxygen, so the muscles turned out robust and healthy. Once all the fibers were grown, Takeuchi and his colleagues rolled them into tubes called MuMuTAs (multiple muscle tissue actuators) like they were preparing sushi rolls. “MuMuTAs were created by culturing thin muscle sheets and rolling them into cylindrical bundles to optimize contractility while maintaining oxygen diffusion,” Takeuchi explains.
On Wednesday, Google DeepMind announced two new AI models designed to control robots: Gemini Robotics and Gemini Robotics-ER. The company claims these models will help robots of many shapes and sizes understand and interact with the physical world more effectively and delicately than previous systems, paving the way for applications such as humanoid robot assistants.
It’s worth noting that even though hardware for robot platforms appears to be advancing at a steady pace (well, maybe not always), creating a capable AI model that can pilot these robots autonomously through novel scenarios with safety and precision has proven elusive. What the industry calls “embodied AI” is a moonshot goal of Nvidia, for example, and it remains a holy grail that could potentially turn robotics into general-use laborers in the physical world.
Along those lines, Google’s new models build upon its Gemini 2.0 large language model foundation, adding capabilities specifically for robotic applications. Gemini Robotics includes what Google calls “vision-language-action” (VLA) abilities, allowing it to process visual information, understand language commands, and generate physical movements. By contrast, Gemini Robotics-ER focuses on “embodied reasoning” with enhanced spatial understanding, letting roboticists connect it to their existing robot control systems.
For example, with Gemini Robotics, you can ask a robot to “pick up the banana and put it in the basket,” and it will use a camera view of the scene to recognize the banana, guiding a robotic arm to perform the action successfully. Or you might say, “fold an origami fox,” and it will use its knowledge of origami and how to fold paper carefully to perform the task.
Gemini Robotics: Bringing AI to the physical world.
In 2023, we covered Google’s RT-2, which represented a notable step toward more generalized robotic capabilities by using Internet data to help robots understand language commands and adapt to new scenarios, then doubling performance on unseen tasks compared to its predecessor. Two years later, Gemini Robotics appears to have made another substantial leap forward, not just in understanding what to do but in executing complex physical manipulations that RT-2 explicitly couldn’t handle.
While RT-2 was limited to repurposing physical movements it had already practiced, Gemini Robotics reportedly demonstrates significantly enhanced dexterity that enables previously impossible tasks like origami folding and packing snacks into Zip-loc bags. This shift from robots that just understand commands to robots that can perform delicate physical tasks suggests DeepMind may have started solving one of robotics’ biggest challenges: getting robots to turn their “knowledge” into careful, precise movements in the real world.
Better generalized results
According to DeepMind, the new Gemini Robotics system demonstrates much stronger generalization, or the ability to perform novel tasks that it was not specifically trained to do, compared to its previous AI models. In its announcement, the company claims Gemini Robotics “more than doubles performance on a comprehensive generalization benchmark compared to other state-of-the-art vision-language-action models.” Generalization matters because robots that can adapt to new scenarios without specific training for each situation could one day work in unpredictable real-world environments.
That’s important because skepticism remains regarding how useful humanoid robots currently may be or how capable they really are. Tesla unveiled its Optimus Gen 3 robot last October, claiming the ability to complete many physical tasks, yet concerns persist over the authenticity of its autonomous AI capabilities after the company admitted that several robots in its splashy demo were controlled remotely by humans.
Here, Google is attempting to make the real thing: a generalist robot brain. With that goal in mind, the company announced a partnership with Austin, Texas-based Apptronik to”build the next generation of humanoid robots with Gemini 2.0.” While trained primarily on a bimanual robot platform called ALOHA 2, Google states that Gemini Robotics can control different robot types, from research-oriented Franka robotic arms to more complex humanoid systems like Apptronik’s Apollo robot.
Gemini Robotics: Dexterous skills.
While the humanoid robot approach is a relatively new application for Google’s generative AI models (from this cycle of technology based on LLMs), it’s worth noting that Google had previously acquired several robotics companies around 2013–2014 (including Boston Dynamics, which makes humanoid robots), but later sold them off. The new partnership with Apptronik appears to be a fresh approach to humanoid robotics rather than a direct continuation of those earlier efforts.
Other companies have been hard at work on humanoid robotics hardware, such as Figure AI (which secured significant funding for its humanoid robots in March 2024) and the aforementioned former Alphabet subsidiary Boston Dynamics (which introduced a flexible new Atlas robot last April), but a useful AI “driver” to make the robots truly useful has not yet emerged. On that front, Google has also granted limited access to the Gemini Robotics-ER through a “trusted tester” program to companies like Boston Dynamics, Agility Robotics, and Enchanted Tools.
Safety and limitations
For safety considerations, Google mentions a “layered, holistic approach” that maintains traditional robot safety measures like collision avoidance and force limitations. The company describes developing a “Robot Constitution” framework inspired by Isaac Asimov’s Three Laws of Robotics and releasing a dataset unsurprisingly called “ASIMOV” to help researchers evaluate safety implications of robotic actions.
This new ASIMOV dataset represents Google’s attempt to create standardized ways to assess robot safety beyond physical harm prevention. The dataset appears designed to help researchers test how well AI models understand the potential consequences of actions a robot might take in various scenarios. According to Google’s announcement, the dataset will “help researchers to rigorously measure the safety implications of robotic actions in real-world scenarios.”
The company did not announce availability timelines or specific commercial applications for the new AI models, which remain in a research phase. While the demo videos Google shared depict advancements in AI-driven capabilities, the controlled research environments still leave open questions about how these systems would actually perform in unpredictable real-world settings.
While the Protoclone is a twitching, dangling robotic prototype right now, there’s a lot of tech packed into its body. Protoclone’s sensory system includes four depth cameras in its skull for vision, 70 inertial sensors to track joint positions, and 320 pressure sensors that provide force feedback. This system lets the robot react to visual input and learn by watching humans perform tasks.
As you can probably tell by the video, the current Protoclone prototype is still in an early developmental stage, requiring ceiling suspension for stability. Clone Robotics previously demonstrated components of this technology in 2022 with the release of its robotic hand, which used the same Myofiber muscle system.
Artificial Muscles Robotic Arm Full Range of Motion + Static Strength Test (V11).
A few months ago, Clone Robotics also showed off a robotic torso powered by the same technology.
Torso 2 by Clone with Actuated Abdomen.
Other companies’ robots typically use other types of actuators, such as solenoids and electric motors. Clone’s pressure-based muscle system is an interesting approach, though getting Protoclone to stand and balance without the need for suspension or umbilicals may still prove a challenge.
Clone Robotics plans to start its production with 279 units called Clone Alpha, with plans to open preorders later in 2025. The company has not announced pricing for these initial units, but given the engineering challenges still ahead, a functional release any time soon seems optimistic.
There’s a difference between knowing a word and knowing a concept.
Large language models like ChatGPT display conversational skills, but the problem is they don’t really understand the words they use. They are primarily systems that interact with data obtained from the real world but not the real world itself. Humans, on the other hand, associate language with experiences. We know what the word “hot” means because we’ve been burned at some point in our lives.
Is it possible to get an AI to achieve a human-like understanding of language? A team of researchers at the Okinawa Institute of Science and Technology built a brain-inspired AI model comprising multiple neural networks. The AI was very limited—it could learn a total of just five nouns and eight verbs. But their AI seems to have learned more than just those words; it learned the concepts behind them.
Babysitting robotic arms
“The inspiration for our model came from developmental psychology. We tried to emulate how infants learn and develop language,” says Prasanna Vijayaraghavan, a researcher at the Okinawa Institute of Science and Technology and the lead author of the study.
While the idea of teaching AIs the same way we teach little babies is not new—we applied it to standard neural nets that associated words with visuals. Researchers also tried teaching an AI using a video feed from a GoPro strapped to a human baby. The problem is babies do way more than just associate items with words when they learn. They touch everything—grasp things, manipulate them, throw stuff around, and this way, they learn to think and plan their actions in language. An abstract AI model couldn’t do any of that, so Vijayaraghavan’s team gave one an embodied experience—their AI was trained in an actual robot that could interact with the world.
Vijayaraghavan’s robot was a fairly simple system with an arm and a gripper that could pick objects up and move them around. Vision was provided by a simple RGB camera feeding videos in a somewhat crude 64×64 pixels resolution.
The robot and the camera were placed in a workspace, put in front of a white table with blocks painted green, yellow, red, purple, and blue. The robot’s task was to manipulate those blocks in response to simple prompts like “move red left,” “move blue right,” or “put red on blue.” All that didn’t seem particularly challenging. What was challenging, though, was building an AI that could process all those words and movements in a manner similar to humans. “I don’t want to say we tried to make the system biologically plausible,” Vijayaraghavan told Ars. “Let’s say we tried to draw inspiration from the human brain.”
Chasing free energy
The starting point for Vijayaraghavan’s team was the free energy principle, a hypothesis that the brain constantly makes predictions about the world based on internal models, then updates these predictions based on sensory input. The idea is that we first think of an action plan to achieve a desired goal, and then this plan is updated in real time based on what we experience during execution. This goal-directed planning scheme, if the hypothesis is correct, governs everything we do, from picking up a cup of coffee to landing a dream job.
All that is closely intertwined with language. Neuroscientists at the University of Parma found that motor areas in the brain got activated when the participants in their study listened to action-related sentences. To emulate that in a robot, Vijayaraghavan used four neural networks working in a closely interconnected system. The first was responsible for processing visual data coming from the camera. It was tightly integrated with a second neural net that handled proprioception: all the processes that ensured the robot was aware of its position and the movement of its body. This second neural net also built internal models of actions necessary to manipulate blocks on the table. Those two neural nets were additionally hooked up to visual memory and attention modules that enabled them to reliably focus on the chosen object and separate it from the image’s background.
The third neural net was relatively simple and processed language using vectorized representations of those “move red right” sentences. Finally, the fourth neural net worked as an associative layer and predicted the output of the previous three at every time step. “When we do an action, we don’t always have to verbalize it, but we have this verbalization in our minds at some point,” Vijayaraghavan says. The AI he and his team built was meant to do just that: seamlessly connect language, proprioception, action planning, and vision.
When the robotic brain was up and running, they started teaching it some of the possible combinations of commands and sequences of movements. But they didn’t teach it all of them.
The birth of compositionality
In 2016, Brenden Lake, a professor of psychology and data science, published a paper in which his team named a set of competencies machines need to master to truly learn and think like humans. One of them was compositionality: the ability to compose or decompose a whole into parts that can be reused. This reuse lets them generalize acquired knowledge to new tasks and situations. “The compositionality phase is when children learn to combine words to explain things. They [initially] learn the names of objects, the names of actions, but those are just single words. When they learn this compositionality concept, their ability to communicate kind of explodes,” Vijayaraghavan explains.
The AI his team built was made for this exact purpose: to see if it would develop compositionality. And it did.
Once the robot learned how certain commands and actions were connected, it also learned to generalize that knowledge to execute commands it never heard before. recognizing the names of actions it had not performed and then performing them on combinations of blocks it had never seen. Vijayaraghavan’s AI figured out the concept of moving something to the right or the left or putting an item on top of something. It could also combine words to name previously unseen actions, like putting a blue block on a red one.
While teaching robots to extract concepts from language has been done before, those efforts were focused on making them understand how words were used to describe visuals. Vijayaragha built on that to include proprioception and action planning, basically adding a layer that integrated sense and movement to the way his robot made sense of the world.
But some issues are yet to overcome. The AI had very limited workspace. The were only a few objects and all had a single, cubical shape. The vocabulary included only names of colors and actions, so no modifiers, adjectives, or adverbs. Finally, the robot had to learn around 80 percent of all possible combinations of nouns and verbs before it could generalize well to the remaining 20 percent. Its performance was worse when those ratios dropped to 60/40 and 40/60.
But it’s possible that just a bit more computing power could fix this. “What we had for this study was a single RTX 3090 GPU, so with the latest generation GPU, we could solve a lot of those issues,” Vijayaraghavan argued. That’s because the team hopes that adding more words and more actions won’t result in a dramatic need for computing power. “We want to scale the system up. We have a humanoid robot with cameras in its head and two hands that can do way more than a single robotic arm. So that’s the next step: using it in the real world with real world robots,” Vijayaraghavan said.
Jacek Krywko is a freelance science and technology writer who covers space exploration, artificial intelligence research, computer science, and all sorts of engineering wizardry.
This isn’t the first time scientists have looked to the mantis shrimp as an inspiration for robotics. In 2021, we reported on a Harvard researcher who developed a biomechanical model for the mantis shrimp’s mighty appendage and built a tiny robot to mimic that movement. What’s unusual in the mantis shrimp is that there is a one-millisecond delay between when the unlatching and the snapping action occurs.
The Harvard team identified four distinct striking phases and confirmed it’s the geometry of the mechanism that produces the rapid acceleration after the initial unlatching by the sclerites. The short delay may help reduce wear and tear of the latching mechanisms over repeated use.
New types of motion
The operating principle of the Hyperelastic Torque Reversal Mechanism (HeTRM) involves compressing an elastomeric joint until it reaches a critical point, where stored energy is instantaneously released. Credit: Science Robotics, 2025
Co-author Kyu-Jin Cho of Seoul National University became interested in soft robotics as a graduate student, when he participated in the RoboSoft Grand Challenge. Part of his research involved testing the strength of so-called “soft robotic manipulators,” a type often used in assembly lines for welding or painting, for example. He noticed some unintended deformations in the shape under applied force and realized that the underlying mechanism was similar to how the mantis shrimp punches or how fleas manage to jump so high and far relative to their size.
In fact, Cho’s team previously built a flea-inspired catapult mechanism for miniature jumping robots, using the Hyperelastic Torque Reversal Mechanism (HeTRM) his lab developed. Exploiting torque reversal usually involves incorporating complicated mechanical components. However, “I realized that applying [these] principles to soft robotics could enable the creation of new types of motion without complex mechanisms,” Cho said.
Now he’s built on that work to incorporate the HeTRM into a soft robotic arm that relies upon material properties rather than structural design. It’s basically a soft beam with alternating hyperelastic and rigid segments.
“Our robot is made of soft, stretchy materials, kind of like rubber,” said Cho. “Inside, it has a special part that stores energy and releases it all at once—BAM!—to make the robot move super fast. It works a bit like how a bent tree branch snaps back quickly or how a flea jumps really far. This robot can grab things like a hand, crawl across the floor, or even jump high, and it all happens just by pulling on a simple muscle.”
Fast and complex multi-finger movements generated by the hand exoskeleton. Credit: Shinichi Furuya
When it comes to fine-tuned motor skills like playing the piano, practice, they say, makes perfect. But expert musicians often experience a “ceiling effect,” in which their skill level plateaus after extensive training. Passive training using a robotic exoskeleton hand could help pianists overcome that ceiling effect, according to a paper published in the journal Science Robotics.
“I’m a pianist, but I [injured] my hand because of overpracticing,” coauthor Shinichi Furuya of Kabushiki Keisha Sony Computer Science Kenkyujo told New Scientist. “I was suffering from this dilemma, between overpracticing and the prevention of the injury, so then I thought, I have to think about some way to improve my skills without practicing.” Recalling that his former teachers used to place their hands over his to show him how to play more advanced pieces, he wondered if he could achieve the same effect with a robotic hand.
So Furuya et al. used a custom-made exoskeleton robot hand capable of moving individual fingers on the right hand independently, flexing and extending the joints as needed. Per the authors, prior studies with robotic exoskeletons focused on simpler movements, such as assisting in the movement of limbs stabilizing body posture, or helping grasp objects. That sets the custom robotic hand used in these latest experiments apart from those used for haptics in virtual environments.
A helping robot hand
A total of 118 pianists participated in three different experiments. In the first, 30 pianists performed a designated “chord trill” motor task with the piano at home every day for two weeks: first simultaneously striking D and F keys with the right index and ring fingers, then striking the E and G keys with the right middle and little fingers. “We used this task because it has been widely recognized as technically challenging to play quickly and accurately,” the authors explained. It appears in such classical pieces as Chopin’s Etude Op. 25. No. 6, Maurice Ravel’s “Ondine,” and the first movement of Beethoven’s Piano Sonata No. 3.
High-speed video of experiments on a robotic hula hooper, whose hourglass form holds the hoop up and in place.
Some version of the Hula-Hoop has been around for millennia, but the popular plastic version was introduced by Wham-O in the 1950s and quickly became a fad. Now, researchers have taken a closer look at the underlying physics of the toy, revealing that certain body types are better at keeping the spinning hoops elevated than others, according to a new paper published in the Proceedings of the National Academy of Sciences.
“We were surprised that an activity as popular, fun, and healthy as hula hooping wasn’t understood even at a basic physics level,” said co-author Leif Ristroph of New York University. “As we made progress on the research, we realized that the math and physics involved are very subtle, and the knowledge gained could be useful in inspiring engineering innovations, harvesting energy from vibrations, and improving in robotic positioners and movers used in industrial processing and manufacturing.”
Ristroph’s lab frequently addresses these kinds of colorful real-world puzzles. For instance, in 2018, Ristroph and colleagues fine-tuned the recipe for the perfect bubble based on experiments with soapy thin films. In 2021, the Ristroph lab looked into the formation processes underlying so-called “stone forests” common in certain regions of China and Madagascar.
In 2021, his lab built a working Tesla valve, in accordance with the inventor’s design, and measured the flow of water through the valve in both directions at various pressures. They found the water flowed about two times slower in the nonpreferred direction. In 2022, Ristroph studied the surpassingly complex aerodynamics of what makes a good paper airplane—specifically, what is needed for smooth gliding.
And last year, Ristroph’s lab cracked the conundrum of physicist Richard Feynman’s “reverse sprinkler” problem, concluding that the reverse sprinkler rotates a good 50 times slower than a regular sprinkler but operates along similar mechanisms. The secret is hidden inside the sprinkler, where there are jets that make it act like an inside-out rocket. The internal jets don’t collide head-on; rather, as water flows around the bends in the sprinkler arms, it is slung outward by centrifugal force, leading to asymmetric flow.
The RAVEN walks, it flies, it hops over obstacles, and it’s efficient.
The RAVEN in action. Credit: EPFL/Alain Herzog
Most drones on the market are rotary-wing quadcopters, which can conveniently land and take off almost anywhere. The problem is they are less energy-efficient than fixed-wing aircraft, which can fly greater distances and stay airborne for longer but need a runway, a dedicated launcher, or at least a good-fashioned throw to get to the skies.
To get past this limit, a team of Swiss researchers at the École Polytechnique Fédérale de Lausanne built a fixed-wing flying robot called RAVEN (Robotic Avian-inspired Vehicle for multiple ENvironments) with a peculiar bio-inspired landing gear: a pair of robotic bird-like legs. “The RAVEN robot can walk, hop over obstacles, and do a jumping takeoff like real birds,” says Won Dong Shin, an engineer leading the project.
Smart investments
The key challenge in attaching legs to drones was that they significantly increased mass and complexity. State-of-the-art robotic legs were designed for robots walking on the ground and were too bulky and heavy to even think about using on a flying machine. So, Shin’s team started their work by taking a closer look at what the leg mass budget looked like in various species of birds.
It turned out that the ratio of leg mass to the total body weight generally increased with size in birds. A carrion crow had legs weighing around 100 grams, which the team took as their point of reference.
The robotic legs built by Shin and his colleagues resembled a real bird’s legs quite closely. Simplifications introduced to save weight included skipping the knee joint and actuated toe joints, resulting in a two-segmented limb with 64 percent of the weight placed around the hip joint. The mechanism was powered by a standard drone propeller, with the ankle joint actuated through a system of pulleys and a timing belt. The robotic leg ended with a foot with three forward-facing toes and a single backward-facing hallux.
There were some more sophisticated bird-inspired design features, too. “I embedded a torsional spring in the ankle joint. When the robot’s leg is crouching, it stores the energy in that spring, and then when the leg stretches out, the spring works together with the motor to generate higher jumping speed,” says Shin. A real bird can store elastic energy in its muscle-tendon system during flexion and release it very rapidly during extension for a jumping takeoff. The spring’s job was to emulate this mechanism, and it worked pretty well—“It actually increased the jumping speed by 25 percent,” Shin says.
In the end, the robotic legs weighed around 230 grams, way more than the real ones in a carrion crow, but it turned out that was good enough for the RAVEN robot to walk, jump, take off, and fly.
Crow’s efficiency
The team calculated the necessary takeoff speed for two birds with body masses of 490 grams and a hair over 780 grams; these were 1.85 and 3.21 meters per second, respectively. Based on that, Shin figured the RAVEN robot would need to reach 2.5 meters per second to get airborne. Using the bird-like jumping takeoff strategy, it could reach that speed in just 0.17 seconds.
How did nature’s go-to takeoff procedure stack up against other ways to get to the skies? Other options included a falling takeoff, where you just push your aircraft off a cliff and let gravity do its thing, or standing takeoff, where you position the craft vertically and rely on the propeller to lift it upward. “When I was designing the experiments, I thought the jumping takeoff would be the least energy-efficient because it used extra juice from the battery to activate the legs,” Shin says. But he was in for a surprise.
“What we meant by energy efficiency was calculating the energy input and energy output. The energy output was the kinetic energy and the potential energy at the moment of takeoff, defined as the moment when the feet of the robot stop touching the ground,” Shin explains. The energy input was calculated by measuring the power used during takeoff.
The RAVEN takes flight.
“It turned out that the jumping takeoff was actually the most energy-efficient strategy. I didn’t expect that result. It was quite surprising”, Shin says.
The energy cost of the jumping takeoff was slightly higher than that of the other two strategies, but not by much. It required 7.9 percent more juice than the standing takeoff and 6.9 percent more than the falling takeoff. At the same time, it generated much higher acceleration, so you got way better bang for the buck (at least as far as energy was concerned). Overall, jumping with bird-like legs was 9.7 times more efficient than standing takeoff and 4.9 times more efficient than falling takeoff.
One caveat with the team’s calculations was that a fixed-wing drone with a more conventional design, one using wheels or a launcher, would be much more efficient in traditional takeoff strategies than a legged RAVEN robot. “But when you think about it, birds, too, would fly much better without legs. And yet they need them to move on the ground or hunt their prey. You trade some of the in-flight efficiency for more functions,” Shin claims. And the legs offered plenty of functions.
Obstacles ahead
To demonstrate the versatility of their legged flying robot, Shin’s team put it through a series of tasks that would be impossible to complete with a standard drone. Their benchmark mission scenario involved traversing a path with a low ceiling, jumping over a gap, and hopping onto an obstacle. “Assuming an erect position with the tail touching the ground, the robot could walk and remain stable even without advanced controllers,” Shin claims. Walking solved the problem of moving under low ceilings. Jumping over gaps and onto obstacles was done by using the mechanism used for takeoff: torsion springs and actuators. RAVEN could jump over an 11-centimeter-wide gap and onto an obstacle 26-centimeter-high.
But Shin says RAVEN will need way more work before it truly shines. “At this stage, the robot cannot clear all those obstacles in one go. We had to reprogram it for each of the obstacles separately,” Shin says. The problem is the control system in RAVEN is not adaptive; the actuators in the legs perform predefined sets of motions to send the robot on a trajectory the team figured out through computer simulations. If there was something blocking the way, RAVEN would have crashed into it.
Another, perhaps more striking limitation is that RAVEN can’t use its legs to land. But this is something Shin and his colleagues want to work on in the future.
“We want to implement some sensors, perhaps vision or haptic sensors. This way, we’re going to know where the landing site is, how many meters away from it we are, and so on,” Shin says. Another modification that’s on the way for RAVEN is foldable wings that the robot will use to squeeze through tight spaces. “Flapping wings would also be a very interesting topic. They are very important for landing, too, because birds decelerate first with their wings, not with their legs. With flapping wings, this is going to be a really bird-like robot,” Shin claims.
All this is intended to prepare RAVEN for search and rescue missions. The idea is legged flying robots would reach disaster-struck areas quickly, land, traverse difficult terrain on foot if necessary, and then take off like birds. “Another application is delivering parcels. Here in Switzerland, I often see helicopters delivering them to people living high up in the mountains, which I think is quite costly. A bird-like drone could do that more efficiently,” Shin suggested.
John is Ars Technica’s science editor. He has a Bachelor of Arts in Biochemistry from Columbia University, and a Ph.D. in Molecular and Cell Biology from the University of California, Berkeley. When physically separated from his keyboard, he tends to seek out a bicycle, or a scenic location for communing with his hiking boots.
There’s a common popular science demonstration involving “soap boats,” in which liquid soap poured onto the surface of water creates a propulsive flow driven by gradients in surface tension. But it doesn’t last very long since the soapy surfactants rapidly saturate the water surface, eliminating that surface tension. Using ethanol to create similar “cocktail boats” can significantly extend the effect because the alcohol evaporates rather than saturating the water.
That simple classroom demonstration could also be used to propel tiny robotic devices across liquid surfaces to carry out various environmental or industrial tasks, according to a preprint posted to the physics arXiv. The authors also exploited the so-called “Cheerios effect” as a means of self-assembly to create clusters of tiny ethanol-powered robots.
As previously reported, those who love their Cheerios for breakfast are well acquainted with how those last few tasty little “O”s tend to clump together in the bowl: either drifting to the center or to the outer edges. The “Cheerios effect is found throughout nature, such as in grains of pollen (or, alternatively, mosquito eggs or beetles) floating on top of a pond; small coins floating in a bowl of water; or fire ants clumping together to form life-saving rafts during floods. A 2005 paper in the American Journal of Physics outlined the underlying physics, identifying the culprit as a combination of buoyancy, surface tension, and the so-called “meniscus effect.”
It all adds up to a type of capillary action. Basically, the mass of the Cheerios is insufficient to break the milk’s surface tension. But it’s enough to put a tiny dent in the surface of the milk in the bowl, such that if two Cheerios are sufficiently close, the curved surface in the liquid (meniscus) will cause them to naturally drift toward each other. The “dents” merge and the “O”s clump together. Add another Cheerio into the mix, and it, too, will follow the curvature in the milk to drift toward its fellow “O”s.
Physicists made the first direct measurements of the various forces at work in the phenomenon in 2019. And they found one extra factor underlying the Cheerios effect: The disks tilted toward each other as they drifted closer in the water. So the disks pushed harder against the water’s surface, resulting in a pushback from the liquid. That’s what leads to an increase in the attraction between the two disks.
On Wednesday, researchers reported that they had developed a drone they’re calling the CoulombFly, which is capable of self-powered hovering for as long as the Sun is shining. The drone, which is shaped like no aerial vehicle you’ve ever seen before, combines solar cells, a voltage converter, and an electrostatic motor to drive a helicopter-like propeller—with all components having been optimized for a balance of efficiency and light weight.
Before people get excited about buying one, the list of caveats is extensive. There’s no onboard control hardware, and the drone isn’t capable of directed flight anyway, meaning it would drift on the breeze if ever set loose outdoors. Lots of the components appear quite fragile, as well. However, the design can be miniaturized, and the researchers built a version that weighs only 9 milligrams.
Built around a motor
One key to this development was the researchers’ recognition that most drones use electromagnetic motors, which involve lots of metal coils that add significant weight to any system. So, the team behind the work decided to focus on developing a lightweight electrostatic motor. These rely on charge attraction and repulsion to power the motor, as opposed to magnetic interactions.
The motor the researchers developed is quite large relative to the size of the drone. It consists of an inner ring of stationary charged plates called the stator. These plates are composed of a thin carbon-fiber plate covered in aluminum foil. When in operation, neighboring plates have opposite charges. A ring of 64 rotating plates surrounds that.
The motor starts operating when the plates in the outer ring are charged. Since one of the nearby plates on the stator will be guaranteed to have the opposite charge, the pull will start the rotating ring turning. When the plates of the stator and rotor reach their closest approach, thin wires will make contact, allowing charges to transfer between them. This ensures that the stator and rotor plates now have the same charge, converting the attraction to a repulsion. This keeps the rotor moving, and guarantees that the rotor’s plate now has the opposite charge from the next stator plate down the line.
These systems typically require very little in the way of amperage to operate. But they do require a large voltage difference between the plates (something we’ll come back to).
When hooked up to a 10-centimeter, eight-bladed propeller, the system could produce a maximum lift of 5.8 grams. This gave the researchers clear weight targets when designing the remaining components.
Ready to hover
The solar power cells were made of a thin film of gallium arsenide, which is far more expensive than other photovoltaic materials, but offers a higher efficiency (30 percent conversion compared to numbers that are typically in the mid-20s). This tends to provide the opposite of what the system needs: reasonable current at a relatively low voltage. So, the system also needed a high-voltage power converter.
Here, the researchers sacrificed efficiency for low weight, arranging a bunch of voltage converters in series to create a system that weighs just 1.13 grams, but steps the voltage up from 4.5 V all the way to 9.0 kV. But it does so with a power conversion efficiency of just 24 percent.
The resulting CoulombFly is dominated by the large cylindrical motor, which is topped by the propeller. Suspended below that is a platform with the solar cells on one side, balanced out by the long, thin power converter on the other.
Meet the CoulombFly.
To test their system, the researchers simply opened a window on a sunny day in Beijing. Starting at noon, the drone took off and hovered for over an hour, and all indications are that it would have continued to do so for as long as the sunlight provided enough power.
The total system required just over half a watt of power to stay aloft. Given a total mass of 4 grams, that works out to a lift-to-power efficiency of 7.6 grams per watt. But a lot of that power is lost during the voltage conversion. If you focus on the motor alone, it only requires 0.14 watts, giving it a lift-to-power efficiency of over 30 grams per watt.
The researchers provide a long list of things they could do to optimize the design, including increasing the motor’s torque and propeller’s lift, placing the solar cells on structural components, and boosting the efficiency of the voltage converter. But one thing they don’t have to optimize is the vehicle’s size since they already built a miniaturized version that’s only 8 millimeters high and weighs just 9 milligrams but is able to generate a milliwatt of power that turns its propeller at over 15,000 rpm.
Again, all this is done without any onboard control circuitry or the hardware needed to move the machine anywhere—they’re basically flying these in cages to keep them from wandering off on the breeze. But there seems to be enough leeway in the weight that some additional hardware should be possible, especially if they manage some of the potential optimizations they mentioned.
