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.
Traversing sparse 3D structures requires humanoid robots to perceive thin, overhanging geometry while executing agile, accurate whole-body motions. We study this problem through monkey-bar traversal, where the robot must jump to the structure, traverse it through sparse bar interactions, and land safely.
The list of obstacles that you can traverse to escape a robot is getting shorter.
9/11 was the first documented use of robots for urban search and rescue and helped create the field of disaster robotics. Personnel began assembling on the afternoon of September 11 and worked the pile from late on September 11 through October 2, when the last available robot failed. The robots found no survivors, but they located remains and helped search for routes through the rubble toward basements and stairwells where trapped firefighters might have gone.
Unitree majorly fully open-sources the UnifoLM-WLA-1.0 embodied foundation model, achieving new SOTA results across multiple benchmarks among open-source models worldwide. A single model coordinates desktop and whole-body mobile manipulation, supporting cross-task and cross-end-effector generalization, driven by one model, whole-body coordination.
Compliance is very important in physical interaction. In this work, we show how a multi-lined aerial robot uses its centroid and joint motion to achieve hybrid impedance—admittance control in contact-rich aerial manipulation tasks such as surface sliding. This work will be presented in IEEE IROS 2026.
Achieving agile and generalized legged locomotion across terrains requires tight integration of perception and control, especially under occlusions and sparse footholds. Existing methods have demonstrated agility on parkour courses but often rely on end-to-end sensorimotor models with limited generalization and interpretability. By contrast, methods targeting generalized locomotion typically exhibit limited agility and struggle with visual occlusions. We introduce a unified reinforcement learning (RL) framework for agile and generalized locomotion that incorporates a novel attention-based map encoder in the control policy.
I suspect that this demo avoids many of the things that are actually difficult about doing dishes. Not just the water and the slippery soapiness, but also identifying when a dish is dirty as well as when it is actually clean.
AI has transformed the digital world. It writes our code, generates our images, reasons in our language. But the physical world—the plants that make our power, our fuel, our steel, and chemicals—it has barely touched. ANYbotics CEO and co-founder Péter Fankhauser on the bet behind the company: Why legged robots turned out to be the way into the world’s most demanding industrial plants, what it took to certify one for explosive atmospheres after experts called it impossible, and where autonomous industrial work goes next.
Dexterous manipulation remains one of the biggest barriers keeping robots from successfully tackling a wide range of everyday tasks. A sense of touch could be the key, but a lack of quality data has held back progress. This is now starting to change as academic labs and startups race to build new tactile datasets and techniques to put them to use.
Over the last few years, vision-language-action (VLA) models have significantly improved the ability of robots to carry out complex tasks involving objects and environments they’ve never encountered before. Pretrained on huge amounts of images, video, and text, and then fine-tuned on a smaller number of teleoperated robot demonstrations, these VLAs can guide robots through a growing range of everyday jobs—like folding laundry, tidying living rooms, and even operating kitchen gadgets—using just a video feed and natural language instructions.
But robots still struggle with tasks that require fine-grained hand control, such as handling deformable materials or manipulating small objects—plugging in a USB cable or turning a key in a lock, for example. That’s partly because VLAs ignore one of the primary sources of information humans rely on in these situations: tactile feedback.
Manipulating Like Humans
“Most dexterous manipulation can be done by humans with their eyes closed,” says Trevor Darrell, professor of computer science at the University of California, Berkeley. “Understanding force, slip, and precise grasping is not something that can be done well with traditional vision sensors.”
However, making effective use of tactile sensors is difficult. Tactile sensor data has very different characteristics to the image data VLAs are normally trained on, and tactile datasets lag far behind the internet-scale of many vision and language datasets. To get around this, Darrell’s team devised a way to first pretrain a model on existing datasets before giving it a sense of touch by training a specialist submodel on 100 hours of specially collected, high-quality tactile data, including demonstrations of common actions like wiping, grasping, twisting, or pouring using more than 200 different household objects.
Explore an interactive visualizer of a small portion of the T-Rex dataset. T-Rex
Putting the tactile data to use was not straightforward. The goal was for a robot to be able to use the tactile signal to correct its grip in real time as it manipulated objects. But this requires reaction times faster than most vision-language models operate at. This mismatch is a significant challenge, Darrell says, so the team used separate submodels, known as “experts,” to handle high-level actions and low-level tactile control in a way that’s quick enough for the tactile feedback to be useful.
The action expert produces motion plans, while the tactile expert, which operates four times faster, uses tactile feedback to adjust the motion plan in real time based on what the robot is feeling as it goes. The model was then fine-tuned on about 100 teleoperated demonstrations of relatively complex manipulation tasks, such as screwing in a light bulb, applying toothpaste to a toothbrush, or transferring an egg between trays, where it averaged a success rate of 65 percent across 12 tasks—nearly double the best VLA model.
Data Diversity
One limitation, admits Darrell, is that his data comes from a single instance of robotic hardware. Robot hands range from fully articulated five-finger designs to simple pincer grippers, and tactile sensors can rely on fundamentally different physics, from measuring changes in resistance to recording images of a soft gel pad deforming. That makes most tactile AI research sensor-specific, says Chengbo Yuan, a master’s student at Tsinghua University in Beijing, and makes it hard to share data and transfer learnings between groups.
Yuan recently set out to tackle this problem by aggregating more than 3,000 hours of tactile robotic data from publicly available datasets, covering 21 sensor types and a variety of robot embodiments. Yuan says they were inspired by efforts like the Open X-Embodiment collaboration, which pooled data from many robots and led to models that generalize to hardware not used in training. Yuan’s team then designed a hardware-agnostic model that can train on this diverse data by converting each sensor’s output into a shared format and mapping it onto labeled positions on a template of a human hand. This model was much more successful than a baseline model, even on hardware it had never encountered before. Yuan puts that down to it acquiring “some kind of common sense of tactile knowledge,” by training on such diverse setups.
Chasing Scale
Despite the promising results, Yuan thinks more tactile data is needed, and his group is now leading an 80-institution collaboration to collate a larger set of teleoperated demonstrations using a standardized approach to tactile data collection and processing. In the meantime, Fudan University in Shanghai and its spin-out NeoteAI have already produced a tactile dataset an order of magnitude larger than previous efforts. Using a proprietary sensor attached to a variety of robotic arms and a handheld gripper operated by humans, they have collected more than 30,000 hours of demonstrations with synchronized visual and tactile data.
The researchers used this data to train a model that doesn’t just react to touch, but also proactively predicts what the robot should be feeling to help guide and assess actions, significantly improving performance. Shunlin Lu, a postdoc researcher at Fudan University and CTO of NeoteAI, says the results are clear evidence that access to large-scale and diverse tactile data leads to significant performance gains.
Robot manipulation policies with a tactile component offer improved performance on a variety of real-world tasks.NeoteAI
Another approach to scaling tactile data could be to piggyback on the vast quantities of visual robotics data already collected. Researchers at the University of Southern California, in Los Angeles, recently released a model that learned to infer tactile information from visual data, by training it on more than 2,700 demonstrations of everyday manipulation using a handheld gripper that records both tactile data and images from a camera on the device. The model learned associations between images of the gripper coming into contact with objects and the amount of pressure felt by the tactile sensors at that moment, giving even robots without tactile sensors a rudimentary sense of touch that the researchers showed to be particularly useful for contact-rich manipulation tasks. But their broader ambition is to use the generator to add tactile data to existing vision datasets.
How much tactile data will be required for breakthroughs in dexterous tasks remains unclear. So far, tactile training’s main contribution has been to make robots more efficient learners at tasks already within reach like picking and placing objects, says Yuan, and he suspects new algorithms may be required to tackle problems truly impossible without touch.
Long Cheng of the Chinese Academy of Sciences in Beijing also thinks raw data is no panacea. “Data is good,” he says. “But how to use them correctly is another issue.” The problem, he notes, is that vision provides a continuous, high-bandwidth stream of pixels, while tactile signals are sparse and intermittent, so models learn to ignore them. His solution, being presented at IROS 2026 later this month, is a model that predicts what a robot will feel from vision alone and then compares it against real tactile input. A large gap between the two means the sensor is detecting something the robot would otherwise miss, so these surprising signals are amplified while predictable ones are dampened. Across five contact-rich tasks, the approach averaged 62.8 percent success against 28.2 percent for the same model without touch.
Lu is more confident that data scaling could have similar benefits to those seen in areas like language and vision. He guesses closer to 100,000 hours, collected in varied, real-world settings rather than in the lab, could unlock new capabilities. Either way, the field now has some early signs that larger tactile datasets and smarter ways to use them can give robots a significant boost on some of the most challenging tasks. “I think tactile intelligence is actually the next step for physical AI,” Lu says.
You sit down and put your arm in the cradle. You press a button. The machine takes it from there.
A near-infrared light sweeps your inner elbow, hunting for a vein. A puff of alcohol hits your skin. An ultrasound probe glides across your arm, mapping how deep the vessel runs and which way it bends. Doppler captures the direction of blood flow to rule out the artery.
The cuff tightens around your upper arm. The needle comes down and pierces the skin. Your blood flows into the collection tubes, each one tipped end over end nine times—no more, no less. The needle withdraws. You get a bandage. No human ever touched you.
This is what it’s like to have blood taken by Aletta, the first autonomous blood-draw device authorized for use in the United States. Developed by the Dutch medical robotics firm Vitestro, the system combines imaging technologies with advanced robotics and AI to do by algorithm what a human phlebotomist—a trained health care professional who finds veins and draws blood by hand—does by feel.
“You have to tip your cap to them,” says Max Balter, a surgical-robotics specialist at Medtronic who worked on autonomous blood-draw systems during grad school in the mid-2010s. “The engineering that they have is incredible…and with their FDA clearance, it moves the whole industry forward.”
Aletta Boosts Lab Capacity Amid Shortages
In a clinical trial involving more than 1,600 people in the Netherlands, Aletta successfully drew blood on the first attempt in 94.5 percent of cases, even among those with hard-to-access veins, people with obesity, and the elderly. When Aletta failed to identify a suitable vein, the patient was referred for conventional phlebotomy.
“It’s exceptional performance,” says Joe El-Khoury, a clinical chemist at Yale who was not involved in the Dutch trial. “It’s definitely as good if not better” than a typical professional phlebotomist.
Complications were minimal, with multiple built-in safeguards to detect problems, such as sensors that track arm movement and needle position, and to halt the process should something go awry. And for those whose veins proved too challenging, a phlebotomist remains on hand to take over when needed.
Notably, because a single phlebotomist can supervise up to three Aletta machines, the system should go a long way toward “helping clinical labs address the critical operational challenges related to the staffing shortages of phlebotomists,” says Luuk Giesen, chief medical officer of Vitestro.
That’s no small challenge in a profession with a median annual turnover rate of nearly 25 percent and a vacancy rate of close to 10 percent, according to surveys of medical laboratories that draw mostly from U.S. institutions. The resulting staffing shortages can limit labs’ capacity to meet demand for routine diagnostic testing of blood counts, cholesterol, metabolic markers, and more. Aletta could address that bottleneck.
Addressing Skin Tone Bias in Blood Draw AI
The promise of greater capacity, however, comes with a caveat: Aletta still fails in roughly one case out of 20. Who are those people?
Some may simply have elusive or unusually deep veins. But a more significant obstacle could be skin pigmentation. In particular, the melanin in darker skin can interfere with the near-infrared light Aletta uses to first map the veins near the skin’s surface and identify promising puncture sites. The technique relies on hemoglobin absorbing the light differently from surrounding tissue, and darker skin tones can absorb more of that light before it reaches the camera, thereby reducing the contrast.
Some experts have raised concerns that Aletta’s infrared sensors will perform poorly for people with darker skin tones, but Vitestro says that its machine also includes an ultrasound sensor, in part to mitigate that risk.Vitestro
Giesen recognizes that the issue could affect first-pass imaging, but notes that the main determinant of vein selection and needle placement is the ultrasound system, which relies on sound rather than light and should not be affected by skin pigmentation in the same way. “Ultrasound is skin-tone agnostic,” he says, adding that Vitestro has unpublished data showing no effect of skin tone on the system’s performance.
But given the history of racial disparities in medical devices—particularly optical technologies such as pulse oximeters, which can be less accurate in people with darker skin and went largely unrecognized as a problem for decades—such claims warrant evidence, says El-Khoury, who has written about the issue.
Brooke Katzman, a clinical chemist at the Mayo Clinic who is collaborating with Vitestro, also wants more evidence that samples collected by the robot are as suitable for testing as those drawn by hand.
The Dutch trial reported little damage to red blood cells, but other measures of sample quality, including clotted tubes, insufficient blood, and proper tube filling, still need to be assessed, as do the results of routine laboratory tests themselves. Katzman plans to launch a U.S.-based trial next year to collect just that sort of data.
“We’re going to do our due diligence,” she says. “Like any instrument we would bring into the lab, we’re going to put it through its paces before using it clinically.”
The Future of Automated Blood Testing
Aletta takes its name from the 19th-century physician Aletta Jacobs, the first female doctor in the Netherlands and founder of what is widely considered the world’s first birth-control clinic.
That nod to history is fitting for a technology that builds on decades of research in robotic phlebotomy by groups in Europe, the United States, and China, and MagicNurse. Yet few pushed the concept as far as biomedical engineer Martin Yarmush of Rutgers University in New Jersey, in whose lab Medtronic’s Balter completed his Ph.D.
In one version of their platform, the Rutgers team even coupled their robot to a benchtop blood analyzer, allowing it to draw samples and then measure levels of infection-fighting immune cells and oxygen-carrying red blood cells—all within minutes.
That all-in system never made it out of laboratory testing. And VascuLogic, the company spun out to commercialize the platform, is long defunct—though others, including ROPHAI, BHealthCare, and MagicNurse, continue to work in the space. But the Rutgers proof-of-concept demonstration points toward the tantalizing possibility of fully automated blood testing at the point of care, with robots handling everything from the needle stick to the analysis.
It is, in some ways, the promise that Theranos made—but built on conventional, validated laboratory technology rather than the dubious science and deception that brought that particular company down.
“I have no doubt that is the future,” says Gregory Retzinger, a clinical pathologist at the Northwestern University Feinberg School of Medicine, in Chicago, who collaborates with Vitestro and has tried the Aletta device himself. (“It was painless, it was fast,” he says.)
For now, Giesen says Vitestro is keeping its ambitions—and its machine—focused on the blood-collection process itself, though he believes Aletta could ultimately do far more. The company plans to launch Aletta in Europe next year, with the U.S. market to follow.
Imagine you are trapped under rubble after an earthquake and you see an electronics-covered cockroach with a spring-loaded needle on its back scuttling toward you. Although the sight might be unnerving, to say the least, this prototype paramedic cyborg, or “Paraborg,” might one day help deliver lifesaving aid to disaster victims who might be otherwise impossible to reach.
The Hardest Problems in Robotics
For decades, scientists have sought to develop cyborg insects as “a shortcut around some of the hardest problems in robotics,” says T. Thang Vo-Doan, director of the University of Queensland’s Biorobotics Lab in Brisbane, Australia, which just published a paper on the Paraborgs.
The University of Queensland
Building an insect-size robot “that can move reliably through rubble, climb over irregular surfaces, recover from falls, carry its own power, and still have room for useful sensors is extraordinarily difficult,” Vo-Doan says. An insect already comes with much of that mobility built in, so instead of trying to create artificial versions of every part of an insect’s body from scratch, researchers can graft an electronic interface onto an insect to make use of its existing capabilities.
Previously, scientists have shown they could steer cyborg insects such as beetles and moths. This prior work largely focused on controlling their movements to serve as passive sensor platforms.
In 2023, as Vo-Doan and fellow researcher Thanh Nho Do were talking about search-and-rescue cyborg insects shortly before that year’s IEEE International Conference on Robotics and Automation (ICRA), they asked, “What happens after an insect finds a trapped victim?” Vo-Doan recalls. “Could it go beyond locating someone and actually provide some form of assistance while rescuers are still trying to reach them?”
Giant Cockroaches to the Rescue
To answer this question, the roboticists experimented with giant burrowing cockroaches (Macropanesthia rhinoceros), which are native to Australia. The researchers needed an insect capable of carrying a large payload (over half its weight), and at roughly 40 grams in size, this species is the world’s heaviest species of roach. A larger insect is also easier to operate on to implant cybernetic interfaces.
The cockroaches were saddled with lightweight electronics that included electrodes implanted into both their antennae and small tail-like appendages known as cerci. Wirelessly activating these electrodes with a handheld gaming controller could steer the roaches left or right, spur them forward, or stop them from moving.
The insects were also equipped with either a wireless camera or a remote-controlled injector, which used a spring to launch a drug-filled syringe at a nearby target. A chemical reaction inside the syringe then generated a puff of carbon dioxide, which exerted pressure within the syringe to inject its payload into a target.
Paraborgs are designed to work in teams, with some carrying cameras and others carrying injectors with potentially lifesaving medications.The University of Queensland
The scientists decided not to load both a camera and an injector onto a single roach because the combined weight and bulk could impair their mobility on complex terrain. Having both sets of electronics would also increase energy demands, resulting in reduced operation time. Instead, the researchers envision a swarm approach with the Paraborgs, with different specialized cyborgs performing complementary roles.
In proof-of-concept tests, the scientists were able to successfully navigate the Paraborgs over a 2.5-meter course past three checkpoints before launching their needles at an 8-by-10-centimeter silicone target. In 25 trials, the cockroaches completed the course every single time and succeeded at injecting the target 72 percent of the time. “The long-term goal is to combine the insect’s advanced locomotion with sensing and intervention capabilities so we can reach and help more people, more quickly,” Vo-Doan says.
While the Paraborgs can be steered remotely, the cockroaches themselves are still very much alive and able to use their skills as bugs to navigate through complex terrain.The University of Queensland
The researchers acknowledge that “for someone who is already trapped or injured, seeing a cyborg insect approaching could understandably be a little surprising or unsettling at first,” Vo-Doan says. Ways to make it clear these insects were part of rescue efforts might include flashing lights, recognizable emergency markings, “or perhaps a tiny speaker delivering a simple message such as, ‘Help is on the way,’ ” Vo-Doan adds. “Making people feel comfortable with the technology is just as important as making it work.”
Practical Paraborgs
In the future, Vo-Doan and his colleagues aim to address practical issues with the Paraborg. These include compensating for the movements of victims, establishing reliable wireless communications inside collapsed structures, and guiding the cyborgs as they climb over and squeeze through complex environments filled with rubble and dust. Autonomy will become increasingly important for these insects, particularly if the scientists want to operate multiple cyborgs at the same time, he says.
In addition, a fundamental challenge when it comes to working with cyborg insects is that they are living creatures with minds of their own. “We are not piloting them like conventional wheeled robots,” Vo-Doan says. “Electrical stimulation influences their direction, but the insect still generates and controls much of its own locomotion.” To deal with this unreliability, the Paraborgs will need better onboard systems to pinpoint their positions and monitor their actions so researchers can recognize when an insect has deviated from its course or become less responsive.
“We are not suggesting that this is a medical device ready to be used on people today,” Vo-Doan says. “Real disaster sites are full of unstable debris, narrow gaps, and communication difficulties, so we need to understand how the insect, electronics, and injector all perform under those conditions. There are also important questions around drug choice and dosage, sterility, needle safety, reliability, and regulation.”
Ultimately, such research into cyborg cockroaches may help inform robot design, explains Vo-Doan. These insects “can give us useful capabilities sooner while, at the same time, helping us develop the fully artificial systems of the future.”
The scientists detailed their findings last month in the journal Advanced 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.
Stabilizing unsecured payloads against the inherent oscillations of dynamic bipedal locomotion remains a critical engineering bottleneck for humanoids in unstructured environments. To solve this, we introduce ReST-RL, a hierarchical reinforcement-learning architecture that explicitly decouples locomotion from payload stabilization. Successfully deployed on the Unitree G1 humanoid hardware, this modular approach demonstrates highly reliable zero-shot sim-to-real generalization across various objects and external force disturbances.
Online, humanoid robots are very impressive to watch, but behind the scenes, most of those movements are carefully choreographed. Researchers in Carnegie Mellon University’s Safe AI Lab are instead teaching robots how to adapt. Their system, called APEX, allows a humanoid robot to navigate obstacles using adaptive, full-body maneuvers.
Researchers from North Carolina State University have created teardrop-shaped soft robots that leap upward or forward when exposed to infrared light—and will keep jumping as long as the light is present.
The robots are made of a liquid-crystal elastomer ribbon shaped like a teardrop, with a thin aluminum tube shaped like a V at one end. When exposed to light from an infrared lamp, the surface of the ribbon contracts, causing the ribbon to rotate. The stiff V at one end of the robot prevents the ribbon from simply rolling in place, causing the ribbon to twist tighter and tighter. This stores energy until the twist reaches a critical point when the ribbon releases that energy, causing the V at one end of the teardrop to snap downward and strike the surface. This launches the teardrop into the air.
I’ll be honest—I was prepared to be underwhelmed by the DARPA Lift Challenge, but there was such creativity in the heavy-lift drone designs that I’m excited for it to come back in 2028.
Humans use not only muscle signals but also stretched skin around joints as a cue for proprioception. To mimic this biological mechanism, we developed a three-layer joint-covering skin with 44 pressure- and stretch-sensitive elements for the musculoskeletal humanoid Musashi-W.
In Turpan, China—known as the City of Fire—summer ground temperatures can exceed 50 °C. During the grape harvest, farmers traditionally carry heavy baskets back and forth under the intense heat, while every extra minute in the sun can affect the freshness of the fruit. This year, the DEEP Robotics Lynx M20S joined the harvest.
This video showcases the achievements of the first OH! GYM! Project cohort, a group of university and graduate students who explored, developed, and deployed their own humanoid behaviors using the open-source AI Sapiens K1 platform. Over the course of one month, the students experienced the complete process of humanoid development—from creating motions in simulation to transferring them onto a physical robot through repeated Sim2Real experiments.
In modern automated manufacturing, six-axis articulated robots perform high-speed, multidirectional maneuvers under demanding operational cycles. However, as robot arms swivel, rotate, and extend, the electrical cables, fiber optics, and pneumatic hoses supplying them endure severe mechanical stress. Torsional twist, rapid acceleration, and repeated contact with machine structures often lead to premature conductor fatigue, insulation breakdown, and costly unplanned production halts.
To overcome these multi-axis motion challenges, the Tsubaki KabelSchlepp Robotrax System provides a specialized three-dimensional cable carrier engineered specifically for complex robotic motion.
Managing High Tensile Forces With Central Steel Technology
Conventional cable carriers often transfer operational movement stress directly onto internal electrical lines and hoses. The Robotrax system changes this dynamic through a central steel cable that runs through the core of every chain link.
The Robotrax system’s central steel cable absorbs the primary tensile loads and preserves conductor integrity, dramatically extending cable service life.
When robot arms undergo rapid directional shifts and accelerations up to 10 g, this internal steel cable absorbs the primary tensile loads. By isolating electrical and fluid lines from pulling forces, the design preserves conductor integrity and dramatically extends cable service life. Mechanics can easily calibrate and adjust system tension using an integrated clamping piece, ensuring consistent mechanical support throughout long operational cycles.
Spherical Link Design and Modular Cable Routing
The foundation of the Robotrax system lies in its open, single-piece plastic links featuring spherical snap-on connections on both sides. This geometry allows the carrier to flex smoothly across three axes, providing radial rotation of up to ±450 degrees per meter depending on the model size.
To optimize internal organization, carrier links contain up to three distinct chambers. This physical separation prevents signal interference and mechanical abrasion between heavy power lines, sensitive data channels, and fluid hoses. For standard models (R040 through R100), technicians can press cables directly into the carrier without tools, drastically reducing installation and maintenance time. Larger configurations, such as the R140X, incorporate swiveling crossbars with snap locks alongside vertical and horizontal dividers for customized interior partitioning.
ROBOTRAX System
Steel cable for transferring extremely high tensile forces
Tension piece for locking the chain links
Type with toolless opening swivel crossbars and divider module available
Open design – Fast cable laying as the cables are simply pressed in – Easy checking of all cables
Special plastic for long service life
Protective covers or heat shields made from different materials are available for different environmental conditions
Quick-release bracket for fixing and continuation
Strain relief with LineFix clamps
Protection against hard impacts, excessive abrasion and premature wear as well as limitation of the bending radius through protector
Active Retraction and Impact Protection
Large robot work envelopes and high-speed motion trajectories can cause loose cable carrier loops to swing and strike the robot body. To eliminate these destructive collisions, Tsubaki KabelSchlepp integrates the Pull Back Unit (PBU).
The PBU serves as an active retraction mechanism that maintains optimal tension on the cable carrier throughout the entire motion cycle. By preventing excess slack and eliminating interfering contours, the PBU minimizes collision risks across complex movement paths. The unit requires zero maintenance on its retraction element and offers standard mounting configurations for leading industrial robot platforms, including KUKA, ABB, and FANUC.
Tsubaki KabelSchlepp’s Pull Back Unit maintains optimal tension on the cable carrier and minimizes collision risks across complex movement paths.
Additionally, external protectors can be retrofitted onto individual chain links. These durable impact shields limit the minimum bending radius to prevent over-flexing while shielding the chain body from severe external abrasion. If wear occurs, technicians simply replace the modular protector rather than the entire cable carrier assembly.
Built for Demanding Industrial Environments
From automotive welding cells to high-speed machining centers, Robotrax systems adapt to severe working conditions through tailored protective accessories:
Heat Shields: Aluminum-coated textile fiber covers protect against radiated heat, hot weld spatter, and flying sparks.
Protective Covers: Coated polyester sleeves shield sensitive lines against aggressive cutting fluids, hydraulic oils, paint overspray, and abrasive dust.
LineFix Strain Relief: Multi-layer clamping devices anchor cables securely at both ends to prevent axial displacement during intense motion.
By combining central load absorption, multi-axis flexibility, and active retraction control, the Robotrax system offers plant engineers and system integrators a reliable path toward maximizing robot uptime and reducing total operational costs.
On a good day, the rock quarry in central Texas is about 370,000 kilometers (230,000 miles) from the moon. But last February, when Rishi Jangale watched his 1.8-meter-wide, 150-kilogram inflatable robot roll effortlessly over rocks, gravel, and wet clay, his imagination turned the quarry into the lunar surface instead.
Jangale is an upbeat mechanical engineer nearing the end of his Ph.D. at Texas A&M University, in College Station, Texas. He and his labmates have been working on this big tan “RoboBall” for about five years. Their goal: create a vehicle capable of exploring some of the most inaccessible terrain in our solar system, such as the 21 km-wide Shackleton crater on the moon’s south pole.
The Shackleton crater is 4 km deep and contains many smaller, deeper craters within. Some parts of the crater never see the sun, and within these perpetually dark, frigid pockets lie mysterious substances that planetary scientists have long struggled to examine, including layers of ancient lunar geology and stores of frozen water that could potentially support a future lunar base.
“The moon is like an archive of what happened to the Earth,” says Sara Russell, a cosmic mineralogist with London’s Natural History Museum who is not involved with Texas A&M’s work. “Robotic collection works brilliantly well, and it’s great to see that being explored more in this context.”
“NASA is not going to let astronauts get anywhere near these craters, because if someone falls in, you’re not going to be able to get them out,” Jangale says. “So we thought, what better shape to roll down a hill than a ball?” In a recent paper published in IEEE Transactions on Field Robotics, Jangale’s team reports on the design of RoboBall, a hypothetical lunar mission, and the results from initial tests in the Texas quarry.
Ambrose figured that a ball could address a pesky mobility risk that robots face on lunar terrain, especially in low gravity: tipping over. An inflatable sphere can’t tip over, and the form factor also insulates its internal components from sharp rocks, dust, and the huge temperature swings from over 93 °C in sunlit spots to minus 240 °C in the shade inside lunar craters. Ambrose imagined a wheeled rover parking at a crater’s edge and releasing a RoboBall to explore its depths. Unlike a small tethered rover, the RoboBall wouldn’t roll its way back up—but with no strings attached, it would have a far greater range to collect geological samples, and then be able to launch them back to the rover outside the crater with small rockets.
NASA hasn’t brought lunar samples back to Earth in over 50 years. Some morsels of moon geology find their way to Earth as meteorites, but Russell says these lack the “gold standard” field work—context about where that sample actually came from. Even as NASA reboots its crewed moon missions, many lunar sites remain inaccessible.
In 2022, Ambrose’s lab finished a proof of concept, the 0.5-meter-wide RoboBall II. Creating the full-size RoboBall III then took about 11 months. “We were building these robots really quickly,” Jangale says. “Ambrose really encourages us to use and break these robots.” And designs did go awry. Jangale remembers software errors and a drivetrain that proved too weak to roll over soft bumps in initial tests.
RoboBall drives by moving a pendulum within its shell, shifting its entire center of mass. On flat ground, if the pendulum’s arm points forward, the shell rolls forward to compensate, and as long as the pendulum keeps the center of mass in front of the center of the ball, RoboBall will keep rolling forward. If the pendulum leans a few degrees to the left or right, RoboBall steers left or right to match. “The robot wants to go where you point the pendulum,” Jangale says. The 340-pound ball is just soft enough to bounce lightly over bumpy obstacles, but its slight overpressure keeps it relatively firm. On steep slopes, this mechanism also lets the ball control its downhill speed by simply angling the pendulum uphill.
“The beauty here is the simplicity,” says Hiro Ono, an aerospace engineer who worked on robot mobility at NASA’s Jet Propulsion Lab for 13 years before joining Georgia Tech. For space robots, Ono describes simplicity in terms of the number of actuators. RoboBall has just two, and both are fully inside of the shell, shielded from environmental risk factors like dust, a feature that Ono describes as “unique.”
After the first quarry tests, it took about seven months for the team to design and build an upgraded RoboBall III with 2.5 times more torque—enough to fling itself over small obstacles and roll up 20 degree slopes.
A small rocket can launch out of the center of the RoboBall to return a sample back to a rover outside the crater.R. Jangale, D. Pravecek, et al.
Getting Mission Ready
In the new paper, the remotely operated RoboBall III descended the quarry’s slopes, navigated soft terrain, and launched hypothetical payloads back out of the crater with small rockets. Powered by a large battery, the robot would inflate itself during a lunar mission and charge up from a robotic rover at the edge of a crater before heading out on its own.
RoboBall is probably not the right platform for all kinds of missions—its slightly bumbling nature means that it’s not ideal if you want to collect a sample from a very specific rock, for example. For now, the team hopes to work with scientists designing lunar science instruments to physically fit into RoboBall’s carry-on-luggage-size interior, while also fitting in with how RoboBall operates. “The robot is not the mission,” Jangale says. “The robot is a way for you to complete the mission.”
The current version of RoboBall cost roughly $250,000 to build, but is not quite ready for the moon in its current form. While its gold-treated aluminum parts are appropriate for space missions, its other materials are not. Space-grade electronics will cost more, and the ball’s shell—made from a material tough enough to roll over steel shards and withstand minus 184 °C temperatures—has never been evaluated in lunar extremes. Aside from materials questions, the team plans to engineer the ball to adapt how it drives on varying slopes autonomously. They also hope to collaborate with government agencies or spaceflight contractors to keep refining the robot’s design for a real, eventual mission.
Later this year, Texas A&M will open a new facility in Houston with the world’s largest indoor simulated moon and Mars landscapes. The facility is only 190 km from Jangale’s quarry. It’s still about 370,000 km from the moon, but it’s going to help Jangale get his robot quite a bit closer.
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.
Nvidia just paid US $12.9 billion for the company that acquired Pollen Robotics, and this must be why.
Meet Microduck. 🦆 The 25-centimeter, 780-gram robot that waddles, falls, gets back up, and learns new tricks.
Packed inside: 15 degrees of freedom, a front camera, an 8x8 lidar, two IMUs, mics, a speaker, NFC, Wi-Fi, and Bluetooth.
Out of the box, Microduck already walks, sits, crouches, roller skates, picks up objects with its articulated beak, and recovers from falls on its own. Drive it with a game controller, plug-in accessories, and NFC tagged objects, run autonomous behaviors, or gather several Microducks for races and football.
Software fully open source. Ready for whatever you throw at it.
On pre-order for an astonishingly low $399, and ships before Christmas.
Most fish-inspired robots are built for one size and one job, so scaling them up or down usually means starting from scratch. A team of engineers says it has found a way to solve that problem. They’ve unveiled ScaFi, a robot modeled on fish like cod and mackerel.
Martin writes, “We’re a small robotics team in Czechia, Europe, building practical hardware around the Unitree G1. Here’s a short demo of our lightweight gripper picking up a strawberry; the gripper weighs under 200 grams and is designed for simple, sensitive manipulation without adding a complex multifinger hand.
EmoLo brings emotion-inspired expressive locomotion to Open Duck Mini V2, a low-cost, open-source bipedal robot inspired by Disney’s BDX droids. With a single reinforcement learning policy, the robot can generate distinct walking styles associated with different emotional expressions, showing how characterful and expressive whole-body motion can be achieved on an accessible robotic platform.
If it’s possible for a robot with a completely immobile face to look frustrated, this robot absolutely does, starting at three minutes into this video.
Noble Machines deployed its first general-purpose robots to a Fortune Global 500 industrial customer within 18 months of the company’s launch and met its first delivery milestone, made possible by its AI-driven whole-body control and industry-leading end-to-end autonomy.
We’ve reduced the time it takes to go from physical prompt → robot behavior. The faster anyone can teach a robot to do something new, the easier it becomes to scale physical work.
TRON 2 × Wuji Hand 2 handles TCM pharmacy work: picking, weighing, grinding, and packaging. The omnidirectional base frees the hands, while precise gripping and dual-arm force control enable midair operations.
From about 2017, individuals began to truly connect with the initial wave of companion robots. These devices had personality, moved around, joked, and answered when you spoke to them. Most early companion robots, however, were still limited by simple voice-command interactions and narrow functionality. Once the novelty wore off, many ended up sitting unused on shelves. As some of those companies went out of business and turned off their servers, many owners likened it to losing a pet.
What Ollobot describes as “gentle intelligence” is a useful way to think about where the serious work in this category is going. Not toward more powerful assistants, but toward more present ones.
The problem companion robots were trying to solve
Loneliness is not a niche issue. According to one study, nearly one out of three elderly adults resides alone, meaning they do not have daily companions. Research also shows that children whose parents have migrated for work, leaving them in the care of relatives, were 2.5 times more likely to experience loneliness than children whose parents remain with them. Among working adults living alone in urban environments, similar patterns of social isolation emerge, even if they are less visible.
Over the years, technology has time and again attempted to solve this problem via video calls, smart speakers, and messaging apps without much success. Those tools are geared towards communication between people that already have relationships. They do not create presence. They schedule it. That is the gap that a new generation of AI companion robots is being engineered to fill.
Today’s AI robots are different
Today’s companion robots are not just cute and cuddly. They are designed with psychological research, clinical insight and long-term interaction models to be truly useful in real homes.
Three fundamental shifts define the current generation:
From reactive to proactive response. Older robots relied on you speaking to them, but modern robots monitor a room with cameras, microphones, and surroundings sensors to initiate interactions without your input, and they can pick up on your emotions.
From function-oriented to emotion-oriented design. The original pitch for companion robots was about what they could do. The question driving the serious work now is how they make you feel, which is a harder engineering problem and a more honest framing of what the product is actually for.
From standalone hardware to connected ecosystems. Leading brands are creating platforms rather than devices with software included as a built-in layer and remote access from the beginning.
The global AI companion market size was valued at US $36.8 billion in 2025 and is projected to grow from $48 billion in 2026 to $318 billion by 2033, at a compound annual growth rate of 31 percent from 2026 to 2033.
Three household scenarios and interaction models
Ollobot’s advanced AI family companion robot OlloNi SS1 addresses a number of gaps in what existing technology offers.
Elderly individuals living alone. The combination of proactive interaction, fall detection, and persistent presence addresses both safety and companionship without the social overhead of asking family members to check in more frequently.
Children in households where parents work far from home. The SS1 functions as a consistent companion that already knows a child, their preferences, their moods, and their routines. The remote connection features allow parents to stay present without requiring a scheduled call, and the life recording system gives them a passive window into their child’s days that feels less clinical than a monitoring camera.
Single professionals living alone in cities. The SS1 adapts to daily routines, builds up a preference model over time, and provides ambient social presence without demands.
OlloNi SS1 adapts to daily routines over time.Ollobot
What OlloNi SS1 is doing differently?
Ollobot’s goal in building intelligent companion robots is to address the gaps in technology and capability, using innovation not to automate tasks but to fill emotional voids.
Much of the robotics industry has historically pursued human imitation — machines that speak, look, or behave like people. The SS1 is instead designed around familiarity and long-term coexistence rather than realism.
The system integrates multiple subsystems operating in parallel, including visual perception, audio processing, mobility control, and interaction management. It is equipped with a multi-chip AI 4K vision module capable of facial recognition and motion tracking. One small but revealing detail is the inclusion of a physical privacy cover for the camera — a mechanical solution to concerns that software settings alone may not fully resolve.
OlloNi SS1 can actively integrate into family activities, and it can autonomously move closer to capture memorable moments or reposition itself to remain engaged in ongoing interactions.Ollobot
The robot supports advanced mobility across multiple indoor surfaces, including wooden floors, ceramic tiles, and low-pile carpets, with slope climbing capability up to 3.5 degrees. Rather than remaining in a fixed location, it can move naturally throughout the home to stay close to household members as daily activities unfold.
For example, the OlloNi SS1 may greet family members when they arrive home, follow an older adult from the living room to the kitchen while continuing a conversation, remind a child to take a study break after a prolonged period of inactivity, or notice that someone appears unusually quiet and gently check in. During family activities, it can autonomously move closer to capture memorable moments or reposition itself to remain engaged in ongoing interactions.
The robot continues to evolve over time, with over-the-air updates that deliver new features, performance improvements, and AI enhancements
It also incorporates fall detection with optimized accuracy for safety monitoring scenarios. A 6-microphone array enables omnidirectional voice pickup with an effective voice capture range of up to 5 meters, supporting reliable wake-word detection and far-field interaction.
To support continuous companionship, much of the robot’s AI processing takes place directly on the device through its “heart module” architecture, with 16 GB of memory and 64 GB of local storage. This enables the system to retain household memories, recognize familiar faces, and respond with lower latency, making interactions feel more natural even during everyday routines.
Because companion robots are expected to remain available throughout the day rather than only during brief interactions, the SS1 is designed for extended operation, offering up to 12 hours of standby time and around 5 hours of active interaction on a single charge. This allows it to accompany users through meals, conversations, playtime, and other daily activities without frequent interruptions.
To support engaging interactions, much of the robot’s AI processing takes place directly on the device through its “heart module” architecture.Ollobot
Like the relationships it is designed to build, the robot continues to evolve over time. Running on Android OS with over-the-air (OTA) updates, the system continuously receives new features, performance improvements, and AI enhancements, allowing its capabilities to grow alongside the household it serves.
The robot’s behavioral model also improves over time. Rather than reacting to isolated commands, it attempts to establish a baseline understanding of household routines and individuals. Changes in behavior — prolonged quietness, unusual inactivity, or emotional cues — become triggers for interaction.
Presence instead of utility
Several features in the OlloNi SS1 illustrate this emphasis on presence and continuity in its interactions.
The system can identify different household members, including pets, and adapt responses accordingly. Remote communication features allow family members to connect through the device without treating every interaction like a scheduled call. Environmental sensors support contextual reminders tied to weather or room conditions.
Its “2+1” multi-display configuration is also designed around emotional communication. Two circular side displays function as expressive “emotional eyes,” while a separate primary display handles information and structured interaction. The separation allows emotional signaling and functional communication to operate independently, creating more intuitive nonverbal interaction even when no dialogue is taking place.
The robot’s behavioral model improves over time. Rather than reacting to isolated commands, it attempts to establish a baseline understanding of household routines and individuals.Ollobot
The SS1 also includes an automated life-recording system built on facial recognition and behavioral-event detection that can capture moments such as laughter, physical closeness, or group interaction automatically. An integrated AI vlog engine can then organize those moments into edited short-form videos with automated sequencing and soundtrack generation. The design intent is to preserve spontaneous domestic moments without requiring active documentation behavior from users.
An integrated AI vlog engine can organize recorded moments into edited short-form videos with automated sequencing and soundtrack generation
Visual data is processed primarily on the device through the SS1’s on-device AI architecture, with household memories stored locally and managed within Ollobot’s proprietary ecosystem instead of being shared with third-party smart home platforms. Access to recordings and live feeds is restricted to authorized users through the companion app, while encrypted communication helps protect data during remote access. Users also retain direct control over recording preferences, and the physical camera privacy cover provides an additional hardware-level safeguard whenever visual monitoring is not desired.
Remote communication is similarly structured around persistence rather than transaction. Traditional video calls are episodic and screen-bound; the SS1 instead acts as a continuously present interface embedded inside the household environment. Through autonomous mobility, environmental awareness, and persistent household memory, remote family members interact with an ongoing domestic context.
The larger shift to “gentle intelligence”
Ultimately, gentle intelligence is not about making robots behave more like humans — it is about helping them fit more naturally into human lives. Each OlloNi SS1 unit develops a unique behavioral profile based on its household. Two units running in different homes for a year will have become meaningfully different from each other, shaped by the specific people, habits, and rhythms of where they live.
That kind of long-term personalization is what early companion robots never had. It is also what makes the difference between a product that ends up on a shelf and one that actually earns its place in a home.
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.
This is very, very cool. But I’m trying to think of what the commercial use case will be, you know? I guess, high speed, incredibly dangerous package delivery to second-floor windows or something...?
Humans have a remarkable ability to perform new physical skills from only one or a few examples. Our latest robot foundation model, GEN-1.5, exhibits the beginnings of that same ability: It can learn a new task in seconds, from a single example, without gradient updates or fine-tuning. It displays broad capabilities across one-shot and few-shots learning from demonstration, as well as zero-shot physical generalization. Although the tasks are simple and short-horizon, this is the first model we know for which one-shot and few-shots learning of physical skills have emerged at scale. We view these results as a significant step toward our mission of building general intelligence for the physical world.
I will make the cautionary point that for many of these “the model figured it out” tasks, the blog post can only say that there was no relevant pretraining data “to the best of our knowledge.”
As a professional bagpiper who definitely pays very close attention to whatever that annoying tapping noise is coming from the back of the band, I can attest to this group of robot drummers being absolutely top-notch.
What does it take for an aerial robot to move through a sequence of arbitrary poses—fast, precisely, and continuously? Rather than teaching the robot a behavior from data, we asked how far a first-principles analytical model could take us. Through a collaboration between the AIMS Group at the Hong Kong Polytechnic University and DRAGON Lab at the University of Tokyo, we developed the first sequential-convex-programming-based trajectory-optimization framework for generalized multirotors, covering both conventional and omnidirectional platforms.
Gravis Robotics brings robotic intelligence to heavy construction machines. Our retrofit kit, the Gravis Rack, turns off-the-shelf hydraulic machines into robots. Cameras, lidar, and onboard compute lets your machine see and understand its surroundings, and learning-based control lets it work close to its limits, moving more dirt with full, fast cycles.
Robust brachiation requires precise hand movements to grasp and release bars together with highly coordinated whole-body motion. To address this challenge, we propose a learning-based framework centered on waypoint-guided reinforcement learning (WGRL). WGRL guides the end effector through waypoints while allowing RL to explore and generate dynamic whole-body behaviors. With this approach, the learned policy demonstrated robust brachiation across diverse courses with different bar heights, spacings, and orientations in sim-to-sim experiments. In the real world, our life-size dual-arm robot successfully traversed four consecutive bars.
This work introduces a novel approach to physical human-robot interaction (pHRI) by leveraging the joint torque sensors of standard collaborative robots. By mounting a passive, uninstrumented plexiglass touchpad to the robot’s flange, we transform the robot into a handwriting-based input interface.
Microspines are one of many ways to enable robots to latch onto surfaces like walls and ceilings. Now roboticists in Canada are using the mini spikes to get drones to land on a more challenging, remote surface: icebergs.
Like a spider, the Ice Dart can land on and latch onto steep, slippery surfaces such as icebergs and glaciers—an increasingly useful capability as activity in the Arctic increases. The drone can grip onto icy slopes of nearly 60 degrees, which is way beyond what most humans could manage without special equipment.
In a recent study, researchers explained how they developed the Ice Dart drone with a special landing gear that absorbs the impact of a hard landing while holding the drone in place with tiny spines that penetrate and grip the ice.
Published in IEEE Transactions on Field Robotics, the study describes how the Ice Dart was able to land on icebergs and a glacier in southeast Iceland. Tests took place amid persistent winds and temperatures of 0 to 10 °C along the ruggedly breathtaking Fjallsjökull (pronounced “FYATLS-yuh-kuutl”) glacier, which empties into a lagoon filled with icebergs. The drone was able to successfully perch at speeds of up to 3 meters per second and slopes of up to 58 degrees, with a success rate of 100 percent even in wind speeds of 30 km/h.
The researchers were motivated by a desire to allow drones to land almost anywhere in the world, since the availability of safe landing sites is one of the primary limitations on where and how drones can operate. The researchers already have a history of developing drones that can land on fast-moving trucks as well as trailers, boats, and steep roofs.
Ice Perching
“The ability to land rather than hover can fundamentally change how drones are used in the field,” says Alexis Lussier Desbiens, a professor of engineering at Université de Sherbrooke, in Sherbrooke, Quebec, Canada, who coauthored the study. “Once a drone has landed, energy consumption drops dramatically, allowing much longer observation periods with a small aircraft. The drone also becomes completely silent and can even reduce or eliminate its thermal and RF signature by shutting down major onboard systems.”
Landing on icebergs specifically allows drones to monitor them for days or months, producing more detailed observation than a quick aerial surveillance mission. This could simplify iceberg tracking compared to methods such as helicopter deployment, dropped instruments, or dart-like tracking devices, and provide another data layer to satellite and ship-based iceberg detection, according to the researchers. It could also provide a means of monitoring icebergs that are otherwise untrackable.
With its carbon-fiber construction, the Ice Dart drone weighs just 2.65 kg and has four legs arranged in an X shape, attached to its body with a pivot joint. Used in the group’s previous drone research, this landing gear disperses energy to reduce impact and overcomes multiple engineering challenges. The friction shock absorbers consist of 38 disks that generate friction torque as the legs move up and down upon impact. This lowers the UAV’s center of mass and helps spread out the kinetic energy of landing, but the real trick comes in the form of two retractable spines on each foot—one for uphill and one for downhill grip. The larger spine engages on the more heavily loaded downhill feet, and the smaller, thinner spine engages more easily on the uphill feet, even under very low loads on steep slopes. The spines only penetrate the ice as the suspension compresses, generating grip and protecting them from high-impact forces.
“The inspiration for the retractable spines in the feet came from looking at a cat’s claws and their ability to deploy only when needed,” says Isaac Tunney, a Université de Sherbrooke postdoc in mechanical and robotics engineering who was lead author of the paper. “I wanted to create feet that would naturally and passively engage their spines in the ice at the right moment, regardless of the drone’s orientation, the surface geometry, or the ice conditions.”
Arctic Surveillance
William D. Harcourt is a researcher at the University of Aberdeen, in Aberdeen, Scotland, focused on Arctic glaciers, snow, and sea ice, as well as the use of remote sensing and machine learning techniques. Harcourt was not involved in the study, but he sees several potentially interesting applications of the technology.
“Near the front of tidewater glaciers, these systems could enable measurement of stress and strain and help us understand calving processes,” Harcourt says. “Drones can be used as a mobile GPS, literally acting as a receiver on the ice, but the system would need to solve tilting issues as 3D change measurements usually required the antenna to be horizontal. However, if these problems can be solved, it could be used to track iceberg movements.”
The researchers want to continue developing the Ice Dart technology for real-world applications, including autonomous landing site selection and an emergency takeoff capability to be used if an iceberg rolls over or breaks apart. This August, the drone will be deployed during a Canadian Arctic mission to land on icebergs, collect data, and help validate ship-based iceberg-detection systems.
Humanoids desperately need to stop making YouTube videos and get a job already, and Persona AI is one of the few humanoid companies that seems to be entirely focused on making that happen. Persona AI’s approach has been to carefully select a job that is economically viable for robots right now, and they’ve found one that was also the job of one of the very first industrial robots ever sold: welding.
As of our first conversation in 2024, Persona had committed to building an economically viable humanoid, but they hadn’t yet figured out where their focus was going to be. “We were all over the place,” Radford says. “Warehousing, automotive, we probably even mentioned the home.” These are the same environments with the same sorts of potential applications that basically every other humanoid robotics company is attempting to make economically viable, and despite an ever more exhaustive number of demonstrations, so far none have succeeded at any sort of useful scale.
The challenge for Persona, and really for every robotics company, is that it’s not enough that you have a robot that is simply capable of doing a task. It’s also not enough that your robot can do that task in a way that is efficient, reliable, and safe. What’s required is that your robot can make money for both you and your customer. Most humanoid companies seek to achieve this by targeting baseline “unskilled” human labor.
Persona did not see economic viability in the unskilled labor approach, Radford says. “We started forming this thesis around skilled trades and tool usage.” Persona is targeting much more expensive skilled labor with its robots, and the reason why this is feasible is because their entry point focuses on the kind of skills that robots are especially good at. “I like to call it ‘last-mover advantage.’ We’ve seen everything that everybody’s doing, and we’ve decided that there’s a different way.”
The first task that Persona’s humanoid is focusing on is welding—using a handheld tool to connect one piece of metal to another. “Tool use is pretty difficult,” Pratt says. “And we want to use the same tools that humans do, which makes it more difficult.” That difficulty is offset somewhat by the fact that Persona’s humanoid will first focus on making long, linear welds that are relatively uncomplicated. “This is not the hardest style of weld,” Radford says, “but in shipbuilding you need a lot of them—hundreds of kilometers of linear welds per ship.”
Currently, Persona has two public partnerships: one with HD Hyundai, which is the world’s largest shipbuilder, and the other with POSCO, one of the largest steel producers in the world, both in Korea. Persona declined to get into detail, but Radford says that broadly speaking, the company is interested in customers who can support ‘hundreds’ of robots per location.
Shipyard welding is an enticing application for Persona because there is a deficit of skilled (and highly paid) workers, it’s taxing physical labor, and it’s a comparatively easy skill for a humanoid to learn.
The welding process is skilled in a very robot-friendly way. Because you can only weld as fast as metal melts, the top speed for the task is an easily manageable centimeter per second. And making a high quality weld involves millimeter-scale repeated motions, which robots excel at, especially over long periods of time—whereas humans tend to get tired or bored. Pratt expects that for these uncomplicated welds, performing on par with humans—if not eventually better—will be achievable soon.
Shipyards make a compelling case for a humanoid with legs, as opposed to a more stable wheeled base. “These open-air shipyards are a couple hundred meters long, with horizontal and vertical spars that you have to step over all the time,” Radford says. “You’ve got to work on the ground, overhead, and through portholes.” Persona considered other form factors, like four legs (or even more), but determined a two-legged robot would be the least disruptive to existing shipyard rhythms.
The Economic Viability of Humanoids
Deploying their robots in shipyards specifically brings additional advantages for Persona. The safety concerns that come with bipedal robots—such as potentially falling over on a human worker—are lessened because a shipyard environment is staffed with workers who are trained to work around potentially dangerous industrial equipment. The company is also less sensitive to competitive pricing because no other company is pursuing the use case. “We’re now in an industry where the value added by our robot can be high enough that we don’t have to cut corners on quality and features in order to reduce the price,” Pratt says. “With a robot for the home, for example, there would be a lot of competition and a ton of price pressure.”
The added value for shipbuilders, Radford explains, doesn’t come from replacing humans with robots. “Our current partnerships are running at a significant backlog, and they’re labor-constrained. So we want to help our customers’ top line, not necessarily their bottom line.” In other words, rather than trying to argue that their robots will lower shipbuilding costs, Persona is instead arguing that their robots will allow more ships to be built. “Even if our robot was more expensive than a human, that would still be valuable to these companies, because it could unlock additional revenue,” Radford says. And when the additional skilled labor does not exist, Persona’s robots could be the next-best option for shipbuilders who need to scale.
Shipyards are environments where legs are necessary for a robot to be useful.CFOTO/Future Publishing/Getty Images
Persona’s Multipurpose Future
In the current commercial humanoid climate, where the emphasis seems to be on developing a “general purpose” robot (whatever that means) that will somehow justify itself through some undefined scale in some equally undefined and perpetually receding future, Persona stands out with their focus on a seemingly viable, near-term, and very specific business case. It hasn’t been easy, though. “It hurts us a little bit,” Radford says. “We’ve been told that we’re not thinking big enough.”
But a tool-using heavy industrial humanoid has plenty of future applications, many of which can be expanded from the welding skill even within shipyards. “Shipbuilding is a great beachhead,” Pratt says. “There are tons of adjacent markets, like grinding, painting, and other kinds of fabrication.” Persona’s ambition, Radford adds, is to be “the largest repository of industrial skills.”
It’s going to take time to get there. That time will be needed to collect tens of thousands of hours of expert demonstration data, create high-fidelity simulations, and conduct real-world testing. And however promising Persona’s approach may seem, the company still has to prove that its idea for an economically viable robotics company can be realized. It’s the same challenge that every humanoid robot company is facing. “A lot of the technical problems are the same no matter whether you’re in a house or a shipyard,” Pratt says. “Everybody’s got a great team and smart people, and we’re all knocking these problems out together.”
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.
Speaking from experience, I can tell you that the best part of any DARPA challenge is when things go horribly wrong. And after you enjoy all the crashes (followed by all of the battery fires), get caught up with the DARPA Lift Challenge with video recaps of the final few days.
This tiny little robot called STEMbot (as in stem, not STEM) can climb up and around plant stems to check for pests. It’s not very fast, but it sure is adorable.
How can a legged robot organize its own walking while maintaining a desired direction? In this work, we present a Differential Adaptive Steering (DAST) mechanism for directional adaptation in legged robots under decentralized adaptive control.
If we as a society would just accept that the appropriate place to store clothing is in a pile on the floor, robots would have a much easier time of it.
Objects constructed by robots are ubiquitous. If you’ve used a car, household appliance, or smartphone today, you’ve used an object constructed at least in part by robots. The more products that manufacturers want to produce (and consumers want to consume) at lower costs, the more industrial robots will be needed.
There are over 4 million industrial robots in use worldwide, according to the International Federation of Robotics. And researchers predict that number will grow to over 16 million by 2030, as manufacturing rapidly increases. But what’s going to happen when they start breaking down? A new system designed by researchers at the Karlsruhe Institute of Technology (KIT), in Karlsruhe, Germany, can predict the defect in a broken product and disassemble it while protecting valuable parts from damage. To continue robotic development sustainably, the industry should prepare for the dismantling, recycling, and rebuilding of our robotic systems.
The system consists of a predictive algorithm that guesses how a product is broken, along with robotic manipulators that actually take the broken product apart. At every stage of the process, the system checks to see if the results align with its predictions, and updates its methods if necessary. For example, in the video below, the system begins by unscrewing a broken component. To simulate a stuck screw, the researcher replaces the screw. When the system observes the screw still in place, it switches to milling away material to remove the part.
Building a product with new parts is easy, says Jan Baumgärtner, one of the designers of the system. Each step is clearly outlined, and there are no expected deviations. But taking apart something that’s broken is unpredictable. “We can imagine 100 ways that something can go wrong.” And if you start taking something apart without knowing how it broke, you might have to undo part of your work when you find the problem. For example, if you have to unscrew 100 screws holding two parts together, but the last screw is stuck, you’ll have wasted time unscrewing all those screws when you should have used a different method to remove the part in the first place.
How to Take Apart a Product
KIT’s robotic disassembly system relies on a CAD model of the broken product and of each part, so it can see how the parts should behave and understand if anything is out of the ordinary. It also uses a mathematical model to predict the damage done to a broken part.
When you give the system a broken device and a CAD model, it first guesses how each part of the broken device should move. The axes each part can move along are called degrees of freedom (for example, a screw should rotate, but not move side to side). The disassembler nudges each part to see if it moves as expected. Based on how the part actually moves, it then uses the mathematical model to predict what went wrong with the part: A corroded part might move less than you think it should, a loose screw may move more, and a deformed part might have different degrees of freedom than expected.
At the beginning of disassembly, the system formulates a plan. It guesses what might be wrong with the device it’s taking apart, and then can change its guess based on observing each piece it takes apart. For example, if there was a screw loose in the part, that might be hard to guess from an initial photograph of the broken part. But when the system moves the screw, it will notice that it can move in more ways than a screw should move, and take that loose screw into account when deconstructing the device. You can also tell the disassembly system which parts are most important to salvage intact from a broken device, and it can adjust its strategy to preserve those specific parts.
The Automated Circular Economy
Baumgärtner’s motivation behind the design of the robotic disassembler is to help create a circular economy, where old devices are repaired instead of thrown away, reducing waste. “The big future is saving our planet,” he says.
Baumgärtner envisions scaling up this one system, composed of a few robotic arms, to have many robotic disassembler arms, each with different tools. These arms will specialize in a different part of the disassembly process so that an entire factory could use different robotic limbs to disassemble a wide range of products. Think of an industrial robot factory that creates cars, but instead is specialized to take them apart. Or, as he puts it, “as a giant robot with 100 arms.”
Ultimately, if this system works as intended, it would be a fully automated way of extracting a broken part from a system, replacing it, and rebuilding the device. Then the circular economy would really shine, as people replaced broken parts in old devices instead of buying new ones all the time. “That’s why we need to think about scaling this,” he says. “Because it means it becomes so cheap that it’s cheaper to repair this [electronic device] than to produce it. That’s the goal.”
This research was presented at the IEEE International Conference on Robotics and Automation (ICRA) 2026 in Vienna.
This story was updated 11 August 2026 to clarify that the disassembly system works for products in general, not only robots.
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.
The DARPA Lift Challenge is taking place through this weekend. There are a couple of very brief overview videos from the past couple of days, which are only really interesting because they give you a quick look at some utterly bizarre heavy-lift drone designs. If you like what you see, DARPA has recorded livestreams of the entire event so far. We’ve posted one of those at the end of this section, and if you want to be impressed by some super-weird drones, check out this and this.
When NASA’s SkyFall helicopters take to the Martian skies, one of their tasks will be to hunt for frozen water—a critical resource for future astronauts—using ground-penetrating radar. For that radar to work, the rotorcraft will carry a flexible, fabric-based antenna that extends below the aircraft without interfering with landings or breaking at touchdown.
We’ve improved how GEN-1 learns to adapt to new actuators and new robots at the lowest level, with up to 10-20x gains on internal benchmarks. This significantly boosts performance on high-precision tasks like disassembling parts from a NIST board.
We present SonicFly, a passive aeroacoustic perception framework that enables one unmanned aerial vehicle (UAV) to estimate and follow another using only the leader’s intrinsic flight sound.
The U.S. Federal Communications Commission (FCC) “Covered List,” originally published in 2021, identifies communications equipment and services that it says pose a threat to national security. On 28 July, the FCC added mobile, communicating robots weighing more than 2 kilograms and power inverters commonly used in solar panels to the list, meaning that new products from any foreign country in these categories are no longer eligible for import.
The move is a Department of Defense–driven expansion of scattered federal efforts to further limit U.S. exposure to potentially sensitive Chinese technology, but it may impose major changes on the robotics industry in allied countries, too.
All foreign-produced advanced robotic devices pose an unacceptable risk to the national security of the United States and to the safety and security of U.S. persons…unless the [Department of Defense determines that] a given foreign-produced advanced robotic device, or a class of such devices, does not pose such risks.
There are two important definitions here. The first is what an “advanced robotic device” is, and the second is what “unacceptable risk” means. Drones already went through their own round of this sort of regulation, so they’re exempt from this particular restriction, as are connected vehicles and medical devices. As far as the FCC is concerned, “advanced robotic devices” are mobile systems that incorporate on-board sensing and communications and have some amount of autonomy. There are a couple of loopholes, including systems weighing under 2 kilograms and any system that communicates at less than 200 kilobits per second, which opens up some creative possibilities. It’s important to note that this applies to new devices; those already certified are not restricted for sale or use.
As to the risks, the U.S. government says that foreign advanced robotic devices represent “a cybersecurity risk that threatens the security of critical infrastructure and thus the safety and security of U.S. persons.” There seem to be two main points to the justification, found in Appendix C. The first is that mobile robots are important to both the economy and the military, so the United States needs its own supply chain and industrial base rather than relying on foreign manufacturers. And second, mobile robots monitor critical infrastructure in sensitive locations, making them a security risk.
The Country That Must Not Be Named
As part of its justification for why foreign robots are a security risk, the DOD cites IEEE Spectrum’s article on a critical vulnerability in robots from Unitree, based in Hangzhou, China, along with several other news articles and reports about Chinese robotics. And despite the FCC swearing up and down that this action is “country neutral” and “not targeted at any country or countries,” U.S. national security sources told Spectrum that the perceived threat is obviously China. That’s how China feels about it, too, per a Chinese Ministry of Commerce 29 July press conference (translation of the first quote here):
On the surface, the FCC’s measures fly the banner of “non-discrimination,” but in substance they discriminate against and suppress Chinese enterprises and products…
China firmly opposes the U.S. overstretching the concept of national security and going after Chinese companies. Protectionism does not make the U.S. more competitive and will only hurt the interests of U.S. companies and consumers. China will continue to do what is necessary to firmly defend the legitimate and lawful rights and interests of Chinese companies.
It’s unclear what China is going to do about this—but how about the rest of the world? How can foreign companies that make advanced robotic devices get them cleared for FCC authorization? Among many, many other things, you’ll need to provide “a detailed, time-bound plan to establish or expand manufacturing in the United States for the advanced robotic device.”
Because China also produces a large fraction of robot components, even for robots assembled in the United States, it will have strong leverage in any related negotiations until U.S. robotics companies further diversify their supply chains.
Applicants must also submit their applications to the DOD and FCC by 1 January 2028, which is unfortunate for anyone who wants to develop an advanced robotic device after that point.
Robotics Industry Reactions
This is all very new, and reactions from the robotics community have been mixed.
Some American robotics companies may benefit in the local market from the newfound lack of competition in the commercial market. Brendan Schulman,Boston Dynamics’ vice president of policy, wrote an enthusiastic endorsement of the ban on LinkedIn: “I sense that this is just the first round in a series of policies that will define the success and growth of the industry for decades to come.” On the other hand, third-country buyers may just stick to Chinese products, as they generally have for drones and electric cars.
But not all companies expect major changes from the new regulation. American customers “need to know they can audit the technology, get support quickly, and keep the system operating without depending on a fragile overseas supply chain,” Nic Radford, the CEO of the U.S. humanoid robotics company Persona, tells IEEE Spectrum. In other words, he figures some customers wouldn’t have wanted Chinese humanoids anyway.
Philipp Frey, vice president of strategy for the Swiss quadruped company ANYbotics, agrees. He says their enterprise customers in the United States “increasingly evaluate robots on long-term reliability, cybersecurity, software capability, safety certification, serviceability, and ecosystem integration, not on hardware cost alone.”
ANYbotics also plans to apply for conditional approval of future products, Frey says. That will involve a national-security review by the DOD or the Department of Homeland Security, disclosing company beneficial ownership, supply-chain risks, and declaring a plan for establishing a significant manufacturing presence in the United States.
Gavin Kenneally, CEO of the U.S. quadruped company Ghost Robotics, is more explicit about the risks that Chinese robot strategy poses to the United States. “Active and purposeful spyware is deployed inside the U.S. on Chinese robots. Examples of predatory pricing abound. And this isn’t just a competition between U.S. and Chinese robotics companies; it’s between private U.S. companies and China’s coordinated national strategy,” Kenneally tells Spectrum. “If today’s announcement encourages stronger cybersecurity and a more level competitive environment, that’s good for customers and good for the robotics industry.”
So is an industry-wide ban the best way to guard against threats? American approaches to Chinese technology security risks have been “ad hoc and fragmented,” wrote the Brookings Institution sociologist Kyle Chan in a report published 9 July. Chan called for the Bureau of Industry and Security, part of the Department of Commerce, to centralize federal information gathering and decision-making on how to handle risky foreign devices. He also called for better public input mechanisms for these issues, and a continuous, proportionate process that tightened or relaxed targeted import restrictions in response to well-defined risks.
That would allow American industry to continue benefiting from partnerships with Chinese manufacturers in less sensitive links of the supply chain, Chan argues. Those links will evolve over time, requiring continued assessment, but without those partnerships, crude bans “could make it more difficult for American startups and researchers to develop new software and end up slowing innovation across the U.S. robotics ecosystem,” he writes.
For a while there, it seemed as though robotics as a whole was stuck in a mad rush towards building humanoid robots mostly because it was very possible (and very lucrative) to do so, even without near-term goals that were necessarily realistic. Some of the magic of those first couple of years of the humanoid explosion has stuck around, but there’s also been an industry-wide sobering leading to pointed questions about practicality and value. In other words, starting a commercial humanoid company now is a much different proposition than it would have been just a few years ago.
On 15 July, Walden Robotics emerged from stealth with US $300 million in funding at a valuation of $1.1 billion. Walden is a spinout of Toyota Research Institute (TRI), and it’s spent the last 10 or so years working on hard problems in robotics with the goal of transitioning from research to real-world applications. That seems like the amount of time and experience that it might reasonably take to develop a practical and value-driven approach to deploying general-purpose humanoid robots, and Walden has chosen an excellent starting point by skipping the legs.
“It’s ironic,” says Walden cofounder and CEO Russ Tedrake. “I thought about legs for 20 years; that’s the class I teach at MIT. There are many reasons to build a robot with legs. But the question is, what’s the addressable market? And what percentage of it is covered by a wheeled base?” It’s this focused, practical thinking that sets Walden somewhat apart from many (if not most) of the other companies in this space. Rather than developing a robot first and searching for a viable commercial use case second, Walden instead identified applications where robots can provide value now, and designed a robot that could safely and efficiently meet those needs.
Walden Robotics
Walden Robotics’ Manufacturing Focus
Russ Tedrake is the CEO and cofounder of Walden Robotics.Walden Robotics
Tedrake is light on the details about what specific applications Walden is targeting at this point (citing confidentiality with current commercial partners). Manufacturing and logistics environments where there are a lot of relatively simple and repetitive tasks that aren’t friendly to conveyor belts and preprogrammed robot arms are a good bet. Even in these environments, however, robots still have to find a useful niche because they’re going up against human workers who are more flexible while also cheaper to employ. So the question is: How do you make an argument to a customer that a robot is actually a better solution than their existing human workers?
“You need to find applications with high utilization—where the robot is used 24 hours a day, 7 days a week,” says Tedrake. “Manufacturing is a global imperative right now, and it makes the economics work.” Economic viability is a necessary condition, but it’s not a sufficient one for Walden, or for their partnership with Toyota. People are a big part of Walden’s plan, too.
One of Walden’s major strengths is the company’s partnership with Toyota, which is not all that surprising given that Walden is a spinout from TRI, which is Toyota’s Silicon Valley–based R&D arm. “Toyota was very proud of the work we had done at TRI, and was ready to go big in this space,” says Tedrake. “Part of the excitement of having Toyota as a partner is that their culture is deeply people-first. When talking to Toyota’s leadership, I was never asked how much money this is going to make, but I was asked how it will improve the quality of life for all people.”
The robot’s chonky design allows it to meet the high-payload requirements of useful manufacturing work.Walden Robotics
In this context, at least in the short term, Walden’s approach to improving the quality of life for people is to take over those aforementioned repetitive manufacturing tasks with robots. Tedrake hopes that this will lead to workplaces where skilled craftspeople are able to do even more with their hard-earned expertise, increasing their efficiency, productivity, and happiness all at the same time—a noble goal, although there’s only so much Walden itself can do to make this happen, and not all customers will share Toyota’s priorities.
Wheeled Humanoid Robots in Factories
Many other humanoidroboticscompanies are also targeting these logistics and manufacturing spaces with general-purpose robots, and they’re doing so by making robots that are as humanlike as possible. The theory is that a humanoid form factor is necessary when operating in human environments. And there are certainly arguments in favor of a humanoid with legs—stairs exist, for one, and legged robots have a smaller footprint compared with ones that have wheels.
But a large wheeled base offers some significant advantages, as Tedrake points out. You’re incentivized to cram the base full of batteries, since more weight near the floor keeps the robot stable, which also solves the problem of running out of power during the middle of the workday. More importantly, a statically stable robot that moves around on a wheeled base can bypass the safety challenges that are currently keeping legged humanoids physically separated from real humans—most prominently, the fact that legged robots can fall over. “Factories already have autonomous mobile [wheeled] robots,” explains Tedrake. “They already have safety cases built around AMRs. You can piggyback on that with a wheeled base.”
Simple, rugged grippers make the robot suitable for commercial deployment.Walden Robotics
Walden’s perspective on manipulation is similar. Many humanoid companies are using five-fingered hands that are highly dexterous but also highly complex, which Tedrake believes is not a pragmatic approach in the context of commercial deployments. “There’s a question of what you need to do the tasks, but the real question is just durability,” Tedrake says. “We have been deployed in a Toyota factory, and at the end of the week, the hands take a beating, so we built hands that can take that. I have not seen a more dexterous hand that could have done the work our hand has done.”
Walden’s long-term plan is to build “general-purpose robots.” It’s not always clear what a general-purpose robot is, because (I would argue) nobody is quite sure what “general purpose” means. It’s certainly not referring to robots that can do everything; I think the closest we can get are robots that can be taught to do a useful number of different skills, which is why I prefer the term “multipurpose.” It’s a little pedantic, I know, but I think the distinction is important because it moderates expectations in the near term.
Part of where Walden’s optimism towards general purposeness comes from is TRI’s earlier research on diffusion policy, which helps robots learn new skills more quickly by leveraging previously learned skills as a foundation. “Fundamentally, multitasking is a way to get to a general-purpose robot,” Tedrake says. “I believe there is a single platform that can do a lot of tasks that are of high value for real customers. That will give us the experience we need to give birth to this deployed general-purpose capability.”
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.
Introducing Gemini Robotics 2—the intelligence layer powering the next generation of truly adaptable robots. As it takes its first literal steps, this major advance unlocks intelligent whole-body control, advanced dexterity, and multirobot collaboration.
Humans routinely communicate through abstractions of their bodies, including shadows, silhouettes, and reflections. Here, we present a robotic system capable of dynamic shadow expression using a 21-degrees-of-freedom dexterous hand with compliant soft skin and a learned shadow self-model.
Imagine running your fingertip over the surface of a U.S. penny. You would feel the ridges of the raised letters and numbers, Abe Lincoln’s bearded side profile, and, if it’s tails, the fluted columns of the Lincoln Memorial. Getting a robot to sense the same things is an imposing task, often requiring gathering data on pressure and force at many spatial locations at once. But that’s just what a team of scientists in Europe has now managed to do, using an unusual, colorful robotic skin that provides high-resolution sensing in real time.
“To be honest, when they showed us this, we thought it was, and pardon my French, [expletive] cool, because it’s a distinctly different approach,” recalled Rich Walker, director of Shadow Robot, the U.K.’s longest-running robot company, which primarily focuses on robotic hands.
The research team, which hails from Queen Mary University of London, the University of Florence, the University of Trieste, and the University of Trento, designed a robotic fingertip with a synthetic skin that reflects different colors of light in response to mechanical deformation. By reading the light reflected off the skin, the fingertip generates maps of topology, strain, and contact pressure. The team has already used the sensor to generate maps of a human fingertip, a penny, and a leaf.
Giacomo Sasso, a postdoctoral research associate in the lab of Federico Carpi at Queen Mary University of London, came up with the idea for the sensor. He had been researching optics when he stumbled upon an interesting paper published in the journal Nature. It described the “mechanochromic material” that would eventually make up the reflector in the skin.
Following the method described in Nature, Sasso exposed a light-sensitive film to a 5-megawatt, 635-nanometer (red) laser for seven minutes. The laser beam creates an interference pattern which causes the film to polymerize in alternating densities, creating layers with different refractive indices.
This structure is called a Bragg reflector. The alternating densities and refractive indices in the polymer cause specific wavelengths of light to be reflected. When the reflector is deformed by contact with an object, its layers are stretched, becoming thinner and reflecting light of a different wavelength.
It took Sasso less than a week to re-create the material in the lab. “From there, we started seeing how we could translate these color patterns into something that was useful for us,” he says. Soon, they realized that the color produced by the material was all they needed to be able to sense the topology of objects.
In the robotic finger, the Bragg reflector is sandwiched between a layer of silicone, which protects it from the outside, and a transparent, fingertip-shaped silicone finger with a camera and LED light embedded inside of it.
The light from the LED shines through the clear polymer of the finger. When the fingertip is deformed by an object, the reflector bounces light back to the camera, with wavelengths depending on the level of deformation—red for least deformation, shifting to green, and then to blue when most deformed.
The team also made adjustments to increase the sensitivity of the skin and help the camera to better read color differences. The silicone of the outer layer of the fingertip is colored black to increase the color contrast, allowing the camera to better translate color into the morphology. The rigidity of the camera inside the finger also enhances the deformation of the reflector, producing greater differences in reflected wavelengths.
After all that optimization, the finger provided 100-micrometer resolution with no computational latency, the researchers determined.
What robot fingers need
Human skin takes in a variety of tactile information in order to successfully move and manipulate objects, including temperature, texture, pressure, and vibration. But engineering a robot to do the same is challenging because of spatial constraints. There often isn’t enough room in a robotic fingertip to incorporate more than one type of sensor. The question then becomes: Which type of sensor should be used?
“And the answer to that is…that’s a really hard question. No one knows yet,” says Walker. Carpi’s team’s robotic finger is exciting because it presents yet another option for roboticists to experiment with, Walker says.
Although Carpi’s team isn’t the first to use soft materials for tactile sensing, its technology is unique because it is able to extract quantitative information about depth and size from the topologic maps it generates. According to Walker, most sensors can only generate topological maps, which reveal the relative sizes of object features.
To Sasso, another key advantage of this robotic finger is that it embeds tactile sensing directly into the material of the finger, rather than using taxels, or pixels that measure force or pressure at specific spatial points.
“The core aspect of the sensor is that we’re essentially [moving toward] having the sensing element at the material level,” he says. “The camera, which is a very highly optimized electronic component, is translating whatever the material is already doing directly into digital signals.”
Michael Wang, co-founder and chief scientist at Daimon Robotics, which, unlike Shadow Robot, primarily uses vision-based sensing, echoed Walker’s sentiment that it’s beneficial to explore new methods of sensing, which may bring unique advantages. But he also explained that soft materials often face challenges with durability, and that the significance of the team’s work would be revealed when the finger is integrated into real robot hands.
“The practical and useful benefits, especially in the context of robot hands, remain to be tested and validated,” he says.
When the materials of soft sensors, like the silicone in Carpi’s team’s fingertip, become eroded or damaged after repeated use, the signals measured by the sensors may not reflect objects’ topography as well.
“Especially if you have the electronics embedded into the material layer itself, that becomes a very challenging engineering problem. And I haven’t seen [many] good soft electronics materials that really can undergo long periods of usage,” Wang says.
However, because the Bragg reflector isn’t in direct contact with objects itself, the outer layer of silicone material acts as a protective barrier, Sasso says. The silicone can also be made more durable using certain chemical coatings, according to Wang.
The team has already been talking to companies that could potentially employ the new sensor. They also hope to improve the sensor so that it can sense objects that don’t lie flat on surfaces. That could open up its use in surgical instruments that require precise contact mapping of tissues and organs, Carpi says.
“There are significant developments that we expect with a clear path toward transition to real world applications,” he says.
Atop a lab bench, Cornell Tech postdoctoral researcher Yifan He positions the lens of an optical receiver almost a meter away from an LED emitting a beam of red light. The computer monitor attached to the receiver takes a beat to refresh, then displays an array of squares that resemble a QR code.
When you hold your phone camera up to a QR code, light strikes the image sensor as only a first step to revealing the data hidden behind the black-and-white matrix. The receiver here is doing something different: directly altering its own memory using the photocurrents produced by the beamed array of light. And unlike the data behind a QR code, which might point to a simple web address, this optical code could convey the parameters of an AI model.
The new receiver design, presented last month at the IEEE/JSAP Symposium on VLSI Technology & Circuits in Honolulu, seeks to reduce the burden of increasing memory demands on AI systems. Shining data down onto processors could lower the energy typically required for data centers, self-driving cars, and even “edge” applications like AI-powered robots, researchers say.
“People are designing all sorts of different AI chips,” says Jae-sun Seo, an associate professor of electrical and computer engineering at Cornell Tech, in New York City. These processors don’t often have room for all the parameters that make up AI models, so the additional data is stored in dynamic RAM (DRAM). The electrical connections commonly used to move the data between the DRAM and the processor create cost and efficiency concerns when systems scale up. “That’s one of the major bottlenecks.”
Optical links move data at high bandwidth with less energy loss than metal wires, but today’s optical receivers undercut that advantage by relying on power-hungry analog circuits to convert light to electronic bits. The group’s new tech would instead receive rapid flashes of digital QR-code-like matrices so that chips can tweak model parameters without those analog circuits, enabling fully digital optical communication that would consume less energy.
“This is a really important problem,” says Dennis Sylvester, an IEEE Fellow who chairs the University of Michigan’s electrical and computer engineering department and was not involved in the work. “It’s got massive commercial implications. This solution is a clever way of dealing with it.”
Jae-sun Seo [left] and Yifan He have developed a receiver that can edit memory in response to QR-code-like arrays of light.Alex Music
How Light “Flips” Memory to Power AI
Processors have a bit of built-in static RAM (SRAM), but not enough to allow an AI model to run independently. While SRAM is the faster of the two memory options, DRAM can store more data in the same footprint.
In the new system, the DRAM sits with the transmitter, and the receiver is part of the processor’s SRAM. The transmitter beams the data to the array of SRAM cells, which in this case are modified to contain photodiodes. Light hitting each photodiode creates a current to flip binary values in the SRAM.
Creating a link between the light and receiver requires calibration, because you can’t expect them to be perfectly aligned or perpendicular to each other. So the chip references a data frame that has information about the expected position of each pixel of data and uses that frame to ensure it can receive the real data, He says. “Ideally the best way is to have direct, point-to-point space between the transmitter and the receiver,” Seo adds, “but even if it’s slightly tilted, we have this calibration circuit.”
For applications in real-world settings, the researchers say they will need to build an optical transmitter that can alter the light matrix millions of times per second, transferring gigabits per second. The transmitter that I saw in He and Seo’s lab is only a proof of concept, emitting a static 14-by-14-bit matrix through a metal mask over the light. The researchers say they are working with optics research groups to build a transmitter that is capable of rapidly changing the matrix.
The Future of Light-Based Memory Links
Michigan’s Sylvester says that the tech in its current form is likely far from commercialization because the individual photosensitive bit cells are larger than SRAM bit cells in conventional chips. Those larger cells mean the chip can fit less memory, a trade-off that he says could cancel out the added efficiency of the light-based approach.
Seo says that it’s part of the group’s ongoing efforts to shrink the bit cells, which can be achieved by optimizing the size of transistors and circuits and leveraging CMOS scaling.
Seo and He are looking at uses for the tech in robotics and other edge applications. One example is in AI-robot-powered warehouses and factories, which could use optical data transmission to save time and energy when updating the AI models in each robot. Additionally, microrobots, which are inherently memory-constrained due to their size, could one day benefit from the tech, though it would require a more size-conscious design.
“Edge AI is a big growth area, and in three, four, five years, you’re going to hear as much about that as you are with data centers, probably, as the intelligence migrates more and more into these devices that we have,” Sylvester says.
Login
