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.

The U.S. Food and Drug Administration gave Aletta the go-ahead on 19 August for use on adults in nonhospitalized settings. The decision follows the lead of European regulators, who authorized the device two years earlier.

“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.

The company thus claims on its website that the “technology works well for all skin tones,” an assertion echoed in the FDA press release announcing the authorization of Vitestro’s device.

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 their 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 in complex terrains. 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.

Humanoids Summit Seoul: 22–23 September 2026, SEOUL

IROS 2026: 27 September–1 October 2026, PITTSBURGH

CoRL 2026: 9–12 November 2026, AUSTIN

Enjoy today’s videos!

I know these videos from Agility can be a little bit silly, but the couch drag in this one is impressive.

[ Agility Robotics ]

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.

[ SteadyTray ]

Thanks, Ioana!

Figure is scaling compute so that its robots can...uh...have their compute scaled, I guess?

Solving for a robot in every home is not a data-and-compute problem, it’s a safety-and-cost problem.

[ Figure ]

The most important thing to know about this gripper is that koalas have two thumbs on each hand.

[ RAI Institute ]

DARPA Triage Challenge Finals are in November!

[ DARPA ]

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.

[ CMU ]

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.

[ NC State ]

Thanks, Ship!

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.

[ DARPA Lift Challenge ]

Thank you, Christian, for attempting to talk some sense into the internet.

[ Christian Hubicki ]

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.

It’s not a replicant, but one day, it will be.

[ University of Tokyo ]

Thanks, Akihiro!

Having mobility issues with your robot? Just staple it to the end of an industrial robotic arm. Problem solved!

[ LimX Dynamics ]

But what if I am the sort of person who needs to speak to a manager?

[ Sharpa ]

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.

[ DEEP Robotics ]

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.

[ ROBOTIS ]

This article is brought to you by Tsubaki KabelSchlepp.

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

1. Steel cable for transferring extremely high tensile forces
2. Tension piece for locking the chain links
3. Type with toolless opening swivel crossbars and divider module available
4. Open design
   – Fast cable laying as the cables are simply pressed in
   – Easy checking of all cables
5. Special plastic for long service life
6. Protective covers or heat shields made from different materials are available for different environmental conditions
7. Quick-release bracket for fixing and continuation
8. Strain relief with LineFix clamps
9. 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.

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.

Getting Rolling

RoboBall is the brainchild of Jangale’s advisor, former NASA robotics engineer Robert Ambrose, who first conceived of the design in 2003.

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.

A robotic rover would ferry RoboBall across the lunar surface to the edge of a crater.R. Jangale, D. Pravecek, et al.

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.

Texas A&M RAD Lab/YouTube

How to Slow Your Roll

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.

Humanoids Summit Seoul: 22–23 September 2026, SEOUL

IROS 2026: 27 September–1 October 2026, PITTSBURGH

CoRL 2026: 9–12 November 2026, AUSTIN, TEXAS

Enjoy today’s videos!

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.

[ Microduck ]

Thanks, Matthieu!

If you’ve chosen to ignore all the earlier DARPA Lift Challenge videos that we’ve posted, now you can get all caught up in about five minutes.

[ DARPA ]

You had me at “54-gram robot that out-jumps a kangaroo.”

[ IEEE Transactions on Robotics ]

Sometimes, you just need a video like this.

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.

[ New York University ]

Thanks, Leah!

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.

[ Sentio Robotix ]

Thanks, Martin!

Hybrid visual markers that are useful for both cameras and lidar is a neat idea.

[ Hello Robot ]

Thanks, Binit!

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.

[ EmoLo ]

Thanks, Masato!

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.

[ DLR RM ]

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.

[ Noble Machines ]

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.

[ Generalist ]

I know this video is mostly a gimmick, but I would totally rent a moderately heavy lift quadruped for a couple of days to help with a move.

[ DEEP Robotics ]

Is taking two minutes to excellently fold a shirt too long, or do we even care how long it takes, as long as it’s a robot doing it?

[ Tokyo Robotics ]

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.

[ LimX Dynamics ]

Person who genuinely knows things about robots, Christian Hubicki, explains everything about robots smashing into walls.

[ Christian Hubicki ]

This article is brought to you by Ollobot.

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:

1. 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.
2. 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.
3. 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.

Learn more at ollobot.com.

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.

Humanoids Summit Seoul: 22–23 September 2026, SEOUL

IROS 2026: 27 September–1 October 2026, PITTSBURGH

CoRL 2026: 9–12 November 2026, AUSTIN

Enjoy today’s videos!

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...?

[ Unitree ]

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.”

[ Generalist ]

BeanBot is a robot inspired by Mexican jumping beans, and I need say no more.

[ IIT ]

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.

[ AgileX Robotics ]

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.

[ DRAGON Lab ]

Thanks, Moju!

This is a nifty idea that adapts a kind of interface frequently used for robot training and uses it for human training instead.

[ MIT ]

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.

[ Gravis Robotics ]

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.

[ EVARL ]

Thanks, Ayumu!

Well, here’s a different approach to welding in shipyards with robots.

[ Kawasaki ]

We should have a lot more robots in agriculture, if only they’d lettuce.

[ Flexiv ]

We’ve all had refs like these.

[ PHYBOT ]

I got stuck after the first 15 seconds of this video trying to imagine what any of these home humanoids would usefully do if they dropped a glass.

[ Zhejiang Humanoid ]

Shakey the Robot doesn’t get enough love.

[ SRI ]

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.

[ TS-Robotics ]

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

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.

IEEE Spectrum first spoke with Persona two years ago, shortly after it was founded by Nicolaus Radford and Jerry Pratt. Radford led the Valkyrie program at NASA’s Johnson Space Center back in the day and was also the founder of Nauticus Robotics, while Pratt led IHMC’s DARPA Robotics Challenge team before spending a couple of years as CTO of Figure.

The Challenge of Humanoids

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.”

Persona AI/YouTube

A Humanoid for Shipyard Welding

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.

Actuate 2026: 18–19 August 2026, SAN FRANCISCO

IROS 2026: 27 September–1 October 2026, PITTSBURGH

Humanoids Summit Seoul: 22–23 September 2026, SEOUL

Enjoy today’s videos!

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.

[ DARPA Lift Challenge ]

Drone delivery: coming soon to a moving vehicle (or perhaps even through an open window) near you.

[ HKUST Aerial Robotics Group ]

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.

[ STEMbot ]

Monumental’s robots delivered the brickwork for a semi-detached home, laying around 20,000 bricks in a new community.

[ Monumental ]

Meet the world’s most “truss’t-worthy” robot.

[ Modlab University of Pennsylvania ]

Stanford BDML and Honeybee Robotics propose a payload to test gecko-inspired adhesives in spaaace!

[ NASA ]

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.

[ BRAIN VISTEC ]

I do not care even a little bit if a robot fails (safely, of course), as long as it recovers from that failure.

[ Sanctuary AI ]

Even for a robot that doesn’t drink champagne, those are some pretty light pours.

[ Kawasaki Robotics ]

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.

[ LimX Dynamics ]

To be fair, this is also the speed at which I fold shirts.

[ Sharpa ]

Our DR02 humanoid robot takes on the stairs with stable, controlled movement—steady steps, steady progress.

[ DEEP Robotics ]

Two words: structural minifridge. Or is it mini fridge...? Whatever, THREE words.

[ AgileX ]

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.

Actuate 2026: 18–19 August 2026, SAN FRANCISCO

IROS 2026: 27 September–1 October 2026, PITTSBURGH

Humanoids Summit Seoul: 22–23 September 2026, SEOUL

Enjoy today’s videos!

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.

[ DARPA Lift Challenge ]

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.

[ NASA ]

Why would you even want a five-fingered humanoid hand when you could have something so much better?

[ Flexiv ]

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.

[ Generalist ]

This is certainly one of the best-looking humanoid robots out there.

[ Generative Bionics ]

A little on the technical side, but the concept here is important, I think: being able to control an assistive robot through touch.

[ Tac-Nav ]

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.

[ General Robotics Lab ]

Okay, but... Get a job?

[ ROBOTIS ]

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.

The FCC’s announcement says:

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.

RELATED: Proposed Chinese Robot Ban Is Latest U.S. Tech Sovereignty Move

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 humanoid robotics companies 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.

Actuate 2026: 18–19 August 2026, SAN FRANCISCO

IROS 2026: 27 September–1 October 2026, PITTSBURGH

Humanoids Summit Seoul: 22–23 September 2026, SEOUL

Enjoy today’s videos!

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.

[ Google DeepMind ]

THE ROADMAP! NOOOOO!

[ Agility ]

Videos like this always make me wonder how repairable these robots are. Very, I would hope.

[ Unitree ]

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.

[ General Robotics Lab ]

Human-to-quadruped motion transfer is an odd concept, but I’m here for it.

[ Disney Research ]

Meet Stretch 4.0—the one-armed, three-wheeled robot that can navigate your home safely. Would you rather a humanoid robot or Stretch?

[ Hello Robot ]

And now, this, for some reason.

[ PNDbotics ]

I’m not sure we’re allowed to be impressed if you resize a badminton court to accommodate your robot.

[ PHYBOT ]

Golden eagles care not for drones.

[ Team BlackSheep ]

University of Southern California researchers work with NASA and others to train robot dogs for planetary exploration on Mars, the moon, and beyond!

[ Research in Applied Decisions: RAD Lab ]

Thanks, Cristina!

WABOT-1 was arguably the birth of the humanoid robot in Japan. We’ve come a long way, and it’s good to be reminded where we started.

[ Takanishi Lab ]

If only this video was at 1x instead of 5x we could have had 15 hours of Memo folding laundry.

[ Sunday Robotics ]

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.

RELATED: “This DIY Bipedal Robot Used Pneumatic “Air-Muscles” Instead of Motors”

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.

RELATED: Robot Hand Manipulates Complex Objects by Touch Alone

“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.

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.

Summer School on Multi-Robot Systems: 29 July–4 August 2026, PRAGUE

Actuate 2026: 18–19 August 2026, SAN FRANCISCO

IROS 2026: 27 September–1 October 2026, PITTSBURGH

Humanoids Summit Seoul: 22–23 September 2026, SEOUL

Enjoy today’s videos!

In just six months, our team turned GENE.01 into a fully functional humanoid platform that can walk, sense and interact. Its full-body multimodal skin perceives touch, proximity, force, and temperature, bringing Physical AI closer to safe and natural collaboration with people. Not a render. Not a concept. This is GENE.01. The future of Physical AI is taking its first steps.

[ Generative Bionics ]

Why create robot intelligence for just one hand, when we could have it learn from many? GEN-1, our latest embodied foundation model, now supports a broad range of end effectors from 5-finger hands, to specialized tools, and everything in between. Each hand is a different sensorimotor interface by which GEN-1 experiences the physical world. Scaling pretraining across thousands of these interfaces teaches GEN-1 a universal physical common sense that transfers to new hands and new ways to grasp, push, pull, twist, and more.

And to illustrate this concept, a surprise spatula.

[ Generalist ]

This paper presents the design, fabrication, and flight validation of a flat-packable flying wing built primarily from corrugated cardboard. The aircraft is manufactured from three laser-cut sheets and assembled through a fold-and-lock architecture that forms load-bearing wing structures with minimal tooling and no permanent fasteners. The full airframe can be assembled in under 15 minutes, demonstrating strong potential for rapid deployment, low-cost logistics, and scalable field use.

[ AIR Lab ]

A $14,000 open-source data-collection system that includes beat-down capability.

[ MEVION ]

Thanks, Kento!

Together with Niantic Spatial and Nvidia, [we] can now scan a real deployment site with off-the-shelf hardware, reconstruct it into a photorealistic Gaussian splat, and run massively parallel RL [reinforcement learning] training. The policies trained in our Gym environment then transfer zero-shot to the real robot and environments they were trained for. This enables faster deployment of more capable and robust policies for the end user.

[ Flexion ]

I don’t know why, but the version of Tron 2 with the stubby little legs is just adorable.

[ LimX Dynamics ]

Uh, get a real job already...?

[ PNDbotics ]

Well, I guess we can all stop asking what humanoid robots are good for.

[ EngineAI ]

I think the right thing to do here is only post the disclaimer included with this video: “This film is a conceptual creative production, and certain scenes are presented for demonstration purposes only and do not represent the actual in-store operating process. The final store environment, robot appearance, and functionality are subject to the actual deployment. During actual operations, the robot will autonomously perform only designated preparation steps for specified ice cream products, and its hands will be fitted with protective gloves that comply with applicable food safety requirements.”

[ Sharpa ]

Drone delivery is now an essential part of the South West London Pathology (SWLP) modernization agenda. Since February 2026, our highly automated aircraft have been delivering urgent NHS samples across south west London, with service up to 85% faster than ground transport. We are thrilled to be part of this initiative, supporting clinicians in providing timely, effective care for patients and contributing to a greener, more resilient NHS.

[ Wing ]

Take a closer look at what’s next for the Aurora Driver. Designed to move freight farther, faster, and more efficiently, this next generation of the Aurora Driver delivers greater performance, built to last one million miles and cut hardware cost in half.

[ Aurora ]

How does a robot learn to recognize an object it’s never encountered? In this case, a demo can be worth a thousand words. Short human demonstrations can be used to create fully automated training datasets, sidestepping the prompting limitations that hold back vision-language models. Rather than describing objects with language, the system tracks what a person touches and manipulates during a demo, follows those objects through time, and clusters detections to handle objects merging or splitting apart in the scene. This bypasses a core weakness of VLMs, which struggle to reliably detect unusual or novel objects even with repeated, carefully engineered prompts.

[ Robotics and AI Institute ]

Like many engineers, Sarah Downs says she knew she wanted to pursue a STEM career from a young age. As a teenager, she discovered robotics through her Tulsa, Okla., middle school’s First Lego League team, and she fell in love with the field, she says. Downs participated in the international robotics program from 2014 to 2016.

Watching PBS specials on NASA Mars rovers Spirit and Opportunity, and seeing the live broadcast of the Curiosity rover launch in 2011, inspired the teen to dream of a career working with NASA.

Sarah Downs

MEMBER GRADE

Graduate student member

UNIVERSITY

Texas A&M University in College Station

MAJOR

Electric engineering

This year the IEEE graduate student member achieved that dream. For her final project as a master’s degree candidate in electrical engineering at the University of Tulsa, she worked on an algorithm in collaboration with NASA and the U.S. Air Force.

The algorithm she developed enables a robot assembling satellites in space to insert an antenna into the correct spot, addressing robotics’s classic peg-in-hole problem of inserting an object into its corresponding hole.

Now a Ph.D. student in electrical engineering at Texas A&M University in College Station, Downs is continuing her research on satellite assembly and manipulation “but on a much larger scale,” she says.

Following a childhood passion

Downs grew up in the Tulsa area. Her father, who died from a heart attack in 2015 when she was 13, was a safety advisor in the oil and gas industry. Her mother stayed home to take care of her brother, who has autism. After her father died, her mother went back to college to earn a bachelor’s degree in business so she could support the family.

“We didn’t have much income, and my mom was always worried about money,” Downs says. “That made me more aware of having a successful career, in a monetary sense.”

From then on, whenever she considered her future career, having a decent salary to support the family was high on her list.

By pursuing a career in robotics, she says, she can follow her passion while obtaining financial security.

In high school, Downs joined the First robotics club, where she found herself drawn to the electrical components used in the machines she and her classmates built.

During her final two years of high school, she participated in an extension program at Tulsa Tech, a training school. She spent half her day in high school classes and the other half taking engineering courses at the vocational school.

After graduating in 2020, she accepted scholarships to attend the University of Tulsa. She began her freshman year at UTulsa not knowing whether she wanted to major in electrical or mechanical engineering, she says, adding that her love of working with small systems helped her choose EE.

For her senior year capstone project, she and two of her classmates designed a lunar lander exhibit for the Tulsa Air and Space Museum. They created an interactive game that simulates missions on lunar and martian surfaces. Four celestial bodies—the moon, Venus, Mars, and Titan—are listed across three computer monitors. Using a game controller, museum visitors can explore the virtual surface of each one. The exhibit is still on display.

Downs earned her bachelor’s degree in electrical engineering in 2024 and continued her education at the university’s EE master’s degree program.

Both more and less complicated than people think

When Downs began her graduate studies, she was supposed to be part of a NASA robotics project for two years. But when a delay in government funding postponed the project’s start, she instead spent her first year in the school’s Institute for Robotics and Autonomy, then newly launched. Its main focus is developing robots to assist people who have mobility challenges.

Inspired by her grandmother, who was wheelchair-bound due to severe arthritis, Downs developed a robotic arm that helps older people and wheelchair users live independently. The arm was able to identify and place objects in the appropriate locations inside the home, such as unloading certain groceries from a shopping bag and placing them on a shelf or in separate containers.

Before the start of her sophomore year in 2025, the NASA project finally secured government funding. She developed a robot that achieves the peg-in-hole task without using any vision systems. Typically, cameras help guide robots’ satellite-assembly work. But in the harsh, remote environment of outer space, cameras might malfunction or encounter delays.

“Don’t stop asking questions. Especially in engineering, don’t pretend like you know everything, because science is about constantly wanting to learn and listen.”

Rather than using cameras, Downs’s robotic arm deploys a force-based insertion process to sense position and orientation of objects in the arm’s environment. The robot loosely grips an antenna and, with a torque sensor on its gripper, “feels” the force feedback of where the satellite and antenna are in relation to each other. The robot then guides the antenna assembly into a target opening on its satellite and maintains the position during adhesion.

Adding to the complexity, the robot performs its task in zero gravity.

“Without gravity, you now have to consider the arm’s reaction torques on the satellite to avoid flinging it into space,” Downs says. Any motion from the arm during the insertion process, especially from increased forces, could cause the satellite to continue movement in that direction.

To combat that, Downs is performing calculations for the project to direct targeted reverse thrusts and counter the force of the robot’s motions.

Her graduate project captures the simple yet complex nature of robotics that she finds fascinating, she says.

“I think robots are both more and also less complicated than people think,” she says. “Really, all you need to start programming a robot is its Denavit-Hartenberg parameters, and you can do a lot with that,” she says, referencing the four values used to describe the position and orientation of a robotic arm and manipulators. Even with different grippers and degrees of freedom, “fundamentally, all robot manipulators start there,” she says.

“But,” she adds, “we’re still learning so much about how robots interact with their environment. Even something simple to us, like manipulating a pen, is still incredibly complex for robots.”

Downs is completing her doctoral thesis in the Robotic Space Simulator project at Texas A&M’s Robotics and Automation Design (RAD) Lab, which specializes in developing machines that can survive in extreme environments. It collaborates with NASA.

Her thesis advisor is Robert Ambrose, a NASA veteran who launched the RAD Lab in 2022. The IEEE member is set to serve as associate director of the school’s Space Institute, due to open this year in Houston. The research facility is being built next to the Johnson Space Center.

After earning her Ph.D., Downs says, she hopes to one day work for NASA, developing rovers that collect samples from Mars or robotic arms that perform tasks on space stations.

To learn more about robots, check out IEEE Spectrum’s guide.

Getting out of the engineering bubble

Downs joined IEEE in 2020 as a freshman at UTulsa to get more involved in electrical engineering events on campus. At the time, the COVID-19 pandemic kept clubs and organizations from meeting in person.

She was active in her school’s IEEE student branch and was elected as its 2022–2024 president. Under her leadership, the branch went from having a few events to hosting one every two weeks.

They included lunch-and-learn sessions and dinners that connected students with professional engineers and the university’s alumni. Downs also organized hands-on workshops on soldering, 3D printing, CAD modeling, and résumé-building.

Her efforts helped increase the branch’s executive board membership from roughly five students to 25 in 2023. The same year, her soldering workshop attracted about 80 students.

She says she enjoyed working with IEEE, especially “engaging with alumni and learning from engineers.”

IEEE is a great resource for networking opportunities, she says, noting that “during the COVID-19 pandemic, engineering students stayed in their bubbles.” IEEE events helped the students make connections that could serve them well, she says.

“Networking is very important, especially in today’s tough job market,” she says. “It’s a lot about who you know and how people observe your work ethic.”

Downs, who now serves as an IEEE graduate advisor for UTulsa’s student branch, says she has seen firsthand how the school’s student branch network has benefited its student members.

“A lot of them have found jobs” because of IEEE, she says.

The working and networking of an engineer

As the IEEE graduate advisor for UTulsa’s student branch, Downs noticed that many engineering undergraduates finish college without any hands-on experience, whether it be a project or an internship.

“Their résumés are very sparse, and they have no proof of their technical skills,” she says. She herself completed a facilities engineering internship at Tulsa International Airport’s American Airlines maintenance facility after her sophomore year of college. And she was an electrical engineering intern at Flight Safety International outside Tulsa after her junior year and after she graduated. The company designs, builds, and maintains its own flight simulators.

Her advice to undergraduates is to hone and demonstrate both their hard and soft skills by working on research projects or even personal passion projects.

“A Raspberry Pi doesn’t cost that much, and you can start working with that immediately,” she says. Students also can take part in engineering interest groups and professional organizations at their school, she adds.

“Put yourself out there and join a research team,” she says. “It’s a great way to show people that you’re a good person to work with and you’d do a good job in the field.”

She adds that it’s also a fine way to keep learning—which is what drew her to a field that has developed only within the past century.

“We’re still constantly learning about robots,” she says.

“Don’t stop asking questions,” she advises students. “Especially in engineering, don’t pretend like you know everything, because science is about constantly wanting to learn and listen.”

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.

Summer School on Multi-Robot Systems: 29 July–4 August 2026, PRAGUE

Actuate 2026: 18–19 August 2026, SAN FRANCISCO

IROS 2026: 27 September–1 October 2026, PITTSBURGH

Humanoids Summit Seoul: 22–23 September 2026, SEOUL

Enjoy today’s videos!

In this work, we present a systematic evaluation of contemporary humanoid technology for laparoscopic surgical tasks. We develop a humanoid-based laparoscopic teleoperation framework using general-purpose instruments and assess its capabilities through benchtop characterization, dry-lab user studies spanning diverse surgical experience levels, and in vivo porcine studies. Across these evaluations, we quantify technical feasibility, task performance, and clinical readiness relative to established surgical platforms. Together, our study provides an evidence-based assessment of the current capabilities and limitations of humanoids for surgical applications, highlighting both their promise and the key technical challenges that must be addressed before clinical deployment.

[ UC San Diego ]

Thanks, Ioana!

Today, we preview ACT-2, the first robotics model to achieve reliability by unifying broad generalization with high performance.

Sunday also has this 3-hour video (!) of Memo folding laundry in “never seen environments.” Let’s just not ask, because we almost certainly don’t want to know.

[ Sunday Robotics ]

Spot is not the first quadruped to try its legs at last few meters package delivery, but the challenge is not really those last few meters—it’s going to be not driving the human coworker nuts, is my guess.

[ Boston Dynamics ]

Quadrupedal locomotion in complex environments requires multiple motor skills, stable gait transitions, and perceptive control over a broad range of speeds. APT-RL (Action Pretrained Transformer-based Reinforcement Learning) is a unified framework for high-speed, multiskill locomotion. A single policy selects and transitions between gaits and motor skills using only onboard perception and computation. In real-world experiments, KAIST HOUND traversed stairs, hurdles, stepping-stones, gaps, and fallen branches. It reached an instantaneous peak speed of 4.25 meters per second while traversing a 60-centimeter step and 6 m/s during a drop-down transition on a three-step staircase.

[ KAIST DRCD Lab ]

We will have much more on this next week.

[ Walden Robotics ]

Today, we introduce Lumo-2, our next-generation latent world-action model for generalist embodied robot learning.

[ Astribot ]

Following Atlas’s first-of-its-kind live performance at the FIFA World Cup 2026, we caught up with Seth Davis, senior program manager, to learn how this demonstration came together and what it takes to succeed in the field (and on the pitch).

[ Boston Dynamics ]

No teleoperation. No cuts. Long take. One of the world’s few complete demonstrations of long-horizon mobile manipulation, bringing fully autonomous humanoid robots another step closer to us.

[ LimX ]

Thanks, Jinyan!

Impressive. But get a job.

[ MagicLab ]

We saw some footage of this last week, but here’s a much better video.

Wing-propelled diving birds flap their wings to move through air and water, yet the wing morphology and kinematics that enable this behavior remain poorly understood because of the difficulty of collecting in situ data. The impact of flapping frequency, wing size, and stiffness on locomotion in—and transition between—the two media are still unknown. We compared data from diving birds against experiments using a flapping-wing robot capable of flying, swimming, plunge diving, and exiting the water. We show that frequency adaptation, flexible wings, and powerful actuation enable seamless transitions without folding wings or legs, that large wings enhance flight without substantially reducing underwater efficiency, and that tail-body distance and egress angle affect water exit. These results clarify how birds (and robots) balance multifluid locomotion constraints.

[ EPFL LIS ]

There are many words that I would never, ever use to describe a drone. Stealthy. Subtle. Whatever the opposite of obnoxious is. Much of this is because of the giant angry bee sound that drones tend to make, but it’s also the way that they look in flight: With uncannily linear movements and an even less canny ability to hover perfectly still, they tend to draw the eye as affronts to nature.

In a paper presented this week at Robotics Science and Systems 2026 in Sydney, roboticists from Northwestern University, Evanston, Ill., demonstrated a drone called Phantom Twist that is essentially invisible to humans, being an order of magnitude more difficult to see in flight than a typical quadrotor. They accomplished this with the aid of computational design, and while the resulting hardware is, I would argue, also an order of magnitude more of an affront to nature than a typical quadrotor represents, it’s pretty amazing how well it works.

Phantom Twist spins so fast, it’s practically invisible.Michael Rubenstein/Northwestern University

The trick here is easy to see, even if the drone isn’t. By spinning in flight at between 15 and 25 hertz, Phantom Twist takes advantage of humans’ decidedly mediocre visual system to turn a solid spinning object into an opaque smear. Human eyes take some amount of time (typically about 100 milliseconds) to integrate what we see before sending the full scene off to our brains for processing. Moving objects can cause problems for this system, because if the movement is fast enough, our eyes are forced to average that motion across the scene, combining it with whatever is in the background and resulting in a transparent blur. This effect is called persistence of vision. For something that spins like Phantom Twist, that motion blur comes from the drone’s rapid rotation, and it works because most of the drone is cleverly designed to be empty space.

Drones that spin in flight are nothing new—we’ve covered a bunch of them in the past, including Picolissimo and any number of samara drones inspired by the spinning flight of maple seeds. What makes Phantom Twist unique, and also very odd, is that the design was computationally optimized for low visibility.

Controlling how drones like this fly

Before we get into that, though, a quick note about how drones like this can even fly controllably, because it’s not at all obvious. With just a single motor and no control surfaces, the only possible control input is through the motor itself, and by pulsing the motor speed up or down at just the right time during each rotation, the drone can translate in any direction. Altitude control comes from changing overall motor thrust, and the drone‘s spinning nature makes it passively stable.

Carbon fiber rods connect batteries, a controller, some counterweights, and a motor and propeller. The research robot also includes optical tracking tags.Michael Rubenstein/Northwestern University

The bits that you need for this kind of drone include the motor and propeller, a couple of batteries, a controller, some counterweights (which could be replaced with more batteries or payload), 0.8-mm carbon fiber rods to tie it all together, and a connector for the handheld launcher that gets the whole thing up to speed. The actual arrangement of these components is surprisingly flexible, and that’s where the invisibility comes in.

“The design space is high dimensional,” explains Northwestern’s Michael Rubenstein. “It’s very difficult for a human to reason through all the trade-offs between the physical constraints required for stable flight and the visual appearance of the spinning drone, and I don’t think we would have easily arrived at this low-visibility design ourselves.”

The visibility (or not) of Phantom Twist is primarily driven by the extent to which different components line up with each other from the perspective of someone looking at the drone. The more components that line up with each other as the drone flies, the less background you see through the spinning drone, and the more visible the drone becomes. Because you might be looking at the drone from a number of different angles, and also because the drone has to be stable enough for controlled flight, there are a bunch of different things that need to be optimized all at once, which is why computational design is effective here.

Phantom Twist’s final design was generated using an iterative optimizer which had a goal of minimizing a metric called learned perceptual image patch similarity, or LPIPS, while making sure that the design could still physically work. LPIPS is the difference between two images: a background image, and a background image with an overlay of the simulated spinning drone. The smaller that difference is, the more invisible that design is. It’s tricky for a human to consider all of the variables at once, but Rubenstein says that the final design does make intuitive sense, because “the automated pipeline prefers placements where components don’t visually overlap as it spins, or where the components are too close to the center of rotation.”

Two iterations of Phantom Twist drones are shown with their handheld launching mechanisms. The better-optimized version [bottom row] relocates the launcher interface to remove components that are too close to the central axis, making them more visible.Michael Rubenstein/Northwestern University

Out of a starting set of around 20,000 feasible Phantom Twist configurations, the optimized design (the one that you see or don’t see in the pictures and videos) has a LPIPS score of 0.0104. A human-designed Phantom Twist is about twice as visible, with a LPIPS score of around 0.2, and a conventional quadrotor (of the same size) would be over 10 times more visible. And there’s still a bit more optimization that could be done with the electrical wiring as well as increasing the baseline transparency of the components themselves.

Phantom Twist is currently controlled using an optical tracking system, which means that it’s not yet capable of flying outside of a controlled environment. But Rubenstein has built other drones along similar principles in the past, which have successfully flown outside, and he’s optimistic about using those techniques to break Phantom Twist out of the lab. The spinning behavior might even enable some useful sensing capabilities, he says. “An interesting possibility is mounting a camera on the spinning body. As the vehicle rotates, it could capture imagery in every direction, effectively creating a 360-degree view of its surroundings that could be used for onboard navigation and control.”

As for what a drone like Phantom Twist could be used for—assuming that the sound can be mitigated somewhat (and there are potential approaches to making that happen), a stealthy microdrone could do all sorts of things with covert surveillance being the most obvious application. For his part, Rubenstein says that he’s personally excited about the potential for watching wildlife, “where a less-intrusive drone could observe animals while minimizing its impact on their natural behavior.” The elephants in particular would certainly appreciate that.

For a deeper dive into all the particulars of this project, read the paper: Computational Design of a Low-Visibility UAV Using a Human-Aligned Perceptual Metric, by Jingxian Wang, Chen Yu, David Matthews, Emma Alexander, Sam Kriegman, and Michael Rubenstein from Northwestern University, which is being presented this week at RSS 2026 in Sydney.

This article is brought to you by X Square Robot.

Large language models gave artificial intelligence a working recipe. Pretrain a large model on broad data, and general capability follows. Robotics has no such recipe. Robotics systems have long been assembled from separate perception, planning, and control parts that rarely add up to intelligence a robot can carry from one task to another, or one machine to another. The central problem in embodied AI is to find the equivalent recipe, and the field does not yet agree on what it is.

X Square Robot, a Chinese embodied-AI company, has made an unusually explicit bet. It argues that the recipe is an integrated stack, spanning the data a robot learns from, a world model for predicting changes in the physical world, and an action model that brings together perception, planning, reasoning, and decision-making to generate executable robot behavior. The company also believes that the stack should be built and released in the open.

X Square Robot shares its vision of bringing robots into real homes.X Square Robot

X Square Robot’s embodied AI stack

What holds the stack together is a small set of principles rather than a single overarching model.

• The first is that the basic unit of robot data is an interaction, not a trajectory; a demonstration is successful only if it changes the world as intended, not simply because the joints moved.
• The second is that pretraining should yield usable capability, not just an initialization for later fine-tuning.
• The third is that behavior should be modeled around physical events rather than fixed slices of time.

These principles make the layers interdependent, since the same robot-free data that trains the action model is also structured to feed the world model. It is worth being precise, though. The company describes the world model and the action model as complementary but independent model families that share a code base. Both sit within its broader World Unified Model, which it has presented as an architecture for training vision, language, action, and physical prediction together.

Robot learning data: Engineering for quality and cost, not scale

For the X Square Robot team, one of the biggest constraints on general-purpose robots is the cost and quality of interaction data, not the number of parameters. To address that, the company built its Universal Manipulation Interface (UMI) data collection system, QUANXTA Zero Series. It works by collecting demonstrations from people wearing a rig with dual grippers rather than teleoperating a robot. This approach is not itself new, and builds on established methods for robot-free data capture. What sets it apart are two engineering choices.

The first is quality control, and it is the most distinctive part. Rather than accepting recorded trajectories as they are, the system runs a closed inspection loop, and its notable step is physical playback. A sample of trajectories is replayed on the real robot, and only those that actually complete the task count as valid. That makes the validity rate a measured quantity rather than an assumption. For example, a gripper that closes a fraction of a second too early still looks like a grasp in the data, yet it has pushed the object away, so it shouldn’t be classified as valid. A smaller clean dataset can be worth more than a larger noisy one.

The second choice is how lower-cost human data and scarce robot data are combined. The company pretrains on a large volume of robot-free demonstrations to build general representations, then adds a small amount of real-robot data as an anchor to the specific machine’s dynamics. It reports that this reaches performance comparable to an all-robot dataset at roughly a 20-fold lower cost of collection, driven mainly by how much cheaper the wearable rig is than a teleoperation setup.

The resulting dataset is deliberately model-agnostic, formatted to feed both action models and world models. The caveat is that the strongest results are measured on the company’s own robots and data-collection pipelines. Broader independent testing will help confirm and extend these promising results across a wider range of settings.

A world model organized around events

In developing its world model, called WALL-WM, X Square Robot took a differentiated approach. Most action models predict a fixed-length chunk of motion from the current image and instruction. That is convenient, but it segments behavior into fixed-duration windows, so the boundaries fall where elapsed time dictates rather than where one action ends and the next begins. WALL-WM instead treats an action-grounded semantic event as its unit: a coherent piece of behavior such as reaching, grasping, or placing, something that can be named in language, seen in video, and executed as motion.

WALL-WM’s design reflects a specific concern about not discarding what large video models already know. To achieve that, a text-to-video model is coupled to a freshly initialized action network that reads from the video features without overwriting them, which preserves the visual prior. From that one process, it offers two modes. An event mode runs in variable-length segments and suits reasoning over long horizons, while a fixed-length mode produces the steady, real-time output a controller needs. That places WALL-WM between mainstream chunk-based action models and pure video world models, keeping the predictive character of a world model while still yielding executable control.

In a series of experiments, the company relied on a generalization test that is more specific than most. A model trained on a limited dataset was evaluated on long-horizon tasks in unseen settings and, on the company’s real-robot benchmark, reportedly outscored baselines that had been fine-tuned on related data. That is a meaningful result if it holds. For now, it is measured on the company’s own benchmark. With the code now being released, the broader community will have the opportunity to test, reproduce, and build on them across more settings.

A policy that runs before fine-tuning, and action tokens with meaning

The action layer carries two connected ideas. The first is a requirement the company sets for itself with Wall-OSS-0.5, its vision-language-action model: The pretrained model should run on a real robot before any task-specific fine-tuning.

The interest is less in the scores than in the design behind them. The model trains three objectives together, namely discrete action tokens, language grounding, and continuous action generation. And it keeps gradients flowing through all of them rather than freezing parts of the network as some rival designs do. It’s also a more strict method, since it reports untuned behavior such as approaching, grasping, and recovering, including on a deformable task held out of training.

The second idea is the action interface itself, called X-Tokenizer. Most systems that turn continuous motion into discrete tokens produce codes that the language model cannot interpret. X-Tokenizer reframes tokenization as learning a semantic interface, so that the top-level code stands for the intent of a motion while lower-level codes carry finer detail, all aligned with the language model’s own features.

A useful consequence is stability. Adding noise to an action barely moves the intent code, which is what lets one tokenizer to be reused across robots without re-tuning. The tokenizer inside the production action model is a related variant of this approach. Together, the two ideas give the action layer something rather powerful: capability that transfers.

The future of embodied AI stacks

X Square Robot is betting that its unique approach combining three layers, each specialized in solving a key part of the problem, will stand out from other embodied AI stacks. The physical-playback step that grounds data quality is uncommon and sensible. The reframing of world modeling around events, with one backbone serving both reasoning and control, is a genuinely distinct approach. And the pairing of a deployable pretraining standard with a tokenizer designed as a semantic interface gives the action layer unusual coherence.

X Square Robot’s valuation has climbed above 20 billion yuan (about US $2.9 billion), suggesting that investors increasingly view data infrastructure, foundation models, and scalable training systems as long-term differentiators in embodied AI.

The next phase will bring broader validation. Much of the current evidence comes from X Square’s own robots and benchmarks. With the world model code now being made public, and as the community begins to test, reproduce, and build on the work, the reported capabilities will be tested across more robots, tasks, and settings.

X Square Robot’s recent funding rounds reflect similar confidence. The company’s valuation has climbed above 20 billion yuan (about US $2.9 billion), suggesting that investors increasingly view data infrastructure, foundation models, and scalable training systems as long-term differentiators in embodied AI.

What’s next for X Square Robot

To learn more about its future plans, the following Q&A with the X Square Robot team further explores the company’s technology, strategy, and vision.

What made now the right moment, technically, to commit to this stack? What recently became possible that wasn’t possible a couple of years ago?

It is not one breakthrough but several trends maturing together. Foundation models gave us a shared representation across vision, language, and action, so we can model what a robot sees, what it is asked to do, and how its actions change the world in one framework, rather than as separate perception, planning, and control modules.

Compute and infrastructure are finally sufficient for large-scale pretraining over long-horizon, multi-embodiment data. Just as importantly, we realized that data, not model size, is the real bottleneck for general robots—what is scarce is diverse, high-quality, reproducible interaction data. And world modeling has become practical. The useful question is no longer how to predict a few seconds of video, but how to understand the ways actions change objects, contacts, and task states. Two years ago these ingredients existed separately. Today they are mature enough to work as one system.

“We realized that data, not model size, is the real bottleneck for general robots—what is scarce is diverse, high-quality, reproducible interaction data. And world modeling has become practical.”

Your data system captures demonstrations with a wearable VR rig and custom grippers rather than teleoperating robots. What was wrong with standard teleoperation?

Teleoperation is built around controlling the robot. It forces the operator to work within the machine’s kinematics, latency, and viewpoint, and the resulting demonstrations are slower, stiffer, and less diverse. We built our system around capturing human skill instead. Manipulation is really about contact, timing, finger coordination, and recovery, not just the path the hand takes, and a wearable rig records those before the behavior is compressed onto one particular robot. It also breaks teleoperation’s expensive scaling law, in which every demonstration needs a robot.

People can generate rich data independently of any robot, and the crucial property is that those demonstrations can still be replayed and executed on a physical robot through the model. Mobility is convenient, but that replay is the real point, because it is what lets the same data be reused across different platforms.

In X Square Robot’s approach, demonstrations can be replayed and executed on a physical robot through the AI model, allowing the same data to be reused across different platforms.X Square Robot

X Square Robot reports that its pipeline has roughly an 85 percent data-validity rate. Why is quality control such an underrated bottleneck?

Because errors in robot data are far more expensive than in language data. A small timing or contact error can change what a demonstration means. If a gripper closes a fraction of a second too early, the motion still looks like a grasp, but physically it has pushed the object away. A dataset that mixes failures and accidental successes teaches ambiguity, not skill, because the real unit is the interaction, not the trajectory.

So we run automated inspection, kinematic checks, and physical replay, where we play a sample of trajectories back on the real robot and count only the ones that actually complete the task. Data quality sets the ceiling on how good a policy can be. In our experience a smaller, cleaner dataset often beats a much larger, noisier one, which is why we treat quality control as part of the model, not a preprocessing afterthought.

The model runs in both “event mode” and “chunk mode.” When does each matter?

Both matter, for different reasons. The physical world changes through events—when contact occurs, a grasp forms, or an object slips—not in fixed-frame windows. Event mode concentrates the model’s attention on those moments, and it matters most for long-horizon tasks, like clearing a table, where progress is a sequence of semantic events rather than a smooth stream. It runs in variable-length segments that follow the task rather than a clock. Chunk mode matters for deployment. Real controllers need a stable, real-time interface, and fixed-length chunks integrate cleanly with existing control systems.

We organize learning around events in the first place because a fixed window can split one motion in half or merge two together, which turns training into short-horizon pattern matching and weakens the model on long tasks. So the world model’s job is to connect event-level understanding, which is where the reasoning happens, with a fixed-length output a real robot can actually run.

Why make “deployable before fine-tuning” the criterion?

Pretraining should produce capability, not just a good starting point. If a model is only useful after heavy fine-tuning, then most of the intelligence still lives in the downstream supervision, not in the foundation model. Deployable before fine-tuning is a more honest test of what pretraining actually learned. A well-pretrained robot should already know how to approach, grasp, move, avoid obstacles, and correct itself. Fine-tuning should adapt it to a specific task or robot, not create the ability from nothing. It is also a practical requirement. A robot in a home or a workplace shouldn’t need a brand-new dataset and a new policy every time the task changes, so a foundation model that already carries general skill, and some ability to recover, is the minimum bar for something genuinely useful in the real world.

What is the most challenging part of cross-embodiment learning?

Robots differ in control frequency, delay, compliance, sensing precision, and contact dynamics, so the same instruction can require different action decompositions and recovery strategies, and a behavior that works on one arm cannot simply be copied to another. Cross-embodiment learning needs an intermediate abstraction, lower than language but higher than joint angles: how you approach an object, how you make contact, how you apply force, and how you recover from a mistake.

When we say cross-embodiment, the main capability we mean is multi-embodiment generalization: transferring across robots, training on many embodiments at once, and adapting to different kinematics. Human-to-robot transfer and other techniques are specific approaches to that goal.

“A robot in a home or workplace shouldn’t need a new dataset and policy every time the task changes. A useful foundation model should already carry general skills and the ability to recover.”

What would you most like to see other researchers attempt to reproduce or stress-test?

Three things, above all. Whether event-level representations really generalize beyond our own datasets, across more tasks, scenes, objects, embodiments, and failure conditions. Whether pretraining stays effective on robots the model never saw during training, or whether its capability is still too tightly coupled to what it has already seen. And whether real-robot evaluation can become a shared language for the field, so that we compare not just success rates but the reasons systems fail, where an instruction was misread, where perception broke down, or where recovery fell short. Robotics has been driven too often by impressive demonstrations, and real progress comes from results that are reproducible and diagnosable.

What capability is still missing before robots become dependable in homes?

Benchmarks measure competence, like whether a model can finish a task. Homes demand reliability, safe and consistent operation over time in a place that changes every day, with objects moving, instructions that are vague, and people interrupting. The missing piece is not a higher one-time success rate: it is robust recovery. A dependable home robot has to know when it is uncertain, when to slow down, when to ask for help, and how to bring the world back to a safe state after it drops something or misunderstands a request.

In a real home, failure recovery matters more than raw success, because the home does not reset itself. Homes also demand careful personalization, learning a household’s routines and preferences over time, with safety and trust as first principles. That combination, not any single skill, separates a capable demonstration from a robot people can live with.

X Square Robot’s approach is that, in a real home, failure recovery matters more than raw success, because the home does not reset itself and it demands careful personalization, with safety and trust as first principles. X Square Robot

How do the open-source components fit into X Square Robot’s World Unified Model direction?

We see these releases as layers of the World Unified Model direction rather than isolated projects. Wall-OSS-0.5, the action model, asks whether an open vision-language-action model can gain directly measurable capability from large-scale pretraining, so it is the capability layer. WALL-WM, the world model, asks how a robot should understand change in the world, shifting from fixed windows to event-level modeling, so it is the representation layer. The data system supplies the interaction data that both of them learn from.

Together they form a loop in which models produce capability, world models organize understanding, and the open-source community drives reproduction and improvement. World Unified Model is the broader architecture those layers support, bringing vision, language, action, and physical prediction together.

We are releasing these pieces openly because embodied intelligence cannot be solved by one organization; it needs many embodiments, many real tasks, and broad feedback, and the long-term goal is a stack that keeps learning and ultimately moves robots from laboratory demonstrations toward reliable everyday use.

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.

RSS 2026: 13–17 July 2026, SYDNEY

Summer School on Multi-Robot Systems: 29 July–4 August 2026, PRAGUE

Actuate 2026: 18–19 August 2026, SAN FRANCISCO

IROS 2026: 27 September–1 October 2026, PITTSBURGH

Humanoids Summit Seoul: 22–23 September 2026, SEOUL

Enjoy today’s videos!

For the first time, two full teams of humanoid robots played an 11-vs-11 soccer match on hardware, bringing one of robotics’ most ambitious long-term visions closer to reality. Never before have two full-sized humanoid robot teams played a soccer game against each other.

[ RoboCup ]

Engineers at MIT and EPFL in Lausanne, Switzerland, have designed a robot that can swim underwater and flap out of the water to continue flying through the air, much like a diving bird. The robot can help scientists study the mechanics that enable these actions in aquatic aviators and may help launch a new class of aerial-aquatic drones and vehicles.

[ MIT ]

We’re excited to announce our breakthrough robotic hands for the NEO platform: hands that match or exceed human-level dexterity, strength, safety, and reliability. Designed from the ground up, these 25-DoF hands combine 25 fully actuated degrees of freedom with a tendon-driven system, rich tactile sensing, and built-in compliance. The result is a hand capable of true in-hand manipulation, precision tool use, and delicate interaction.

[ 1X ]

This match, Tech United played against IRIS at the midsize league at RoboCup 2026 in Incheon, South Korea.

[ Tech United ]

Atlas arrived pitchside at NYNJ Stadium in front of 80,000 people gathered to see Brazil vs. Norway. After performing some of the sport’s most memorable player celebrations, Atlas helped kick off the second half by delivering the match ball!

[ Boston Dynamics ]

Navigating discrete terrain such as stepping stones remains a major challenge for legged robots. Conventional approaches often rely on dense environment reconstruction from cameras or lidar, which can be affected by latency, occlusions, and significant computational overhead. We show that proximity sensors integrated into the bottom of a quadruped’s feet enable safe, terrain-seeking autonomous locomotion.

[ Paper ]

On this holiday, Digit is on grill duty. It turns out precise force control is good for more than payload handling. Happy 4th of July from all of us at Agility.

[ Agility ]

We’ve created GEN-1, our latest milestone in scaling robot learning. We believe it to be the first general-purpose AI model that crosses a new performance threshold: mastery of simple physical tasks. It improves average success rates to 99 percent on tasks where previous models achieve 64 percent, completes tasks roughly 3x faster than state-of-the-art, and requires only one hour of robot data for each of these results. GEN-1 unlocks commercial viability across a broad range of applications—and while it cannot solve all tasks today, it is a significant step toward our mission of creating generalist intelligence for the physical world.

[ Generalist ]

Four years at Figure.

[ Figure ]

Reachy Mini is becoming your real AI companion. The Conversation App makes it able to talk fluently with you, help you with your to-do list, remind you of important tasks, and even chat about music. Long-term memory, voice interaction, always ready to help.

[ Reachy Mini ]

Is this sort of thing now a real job for humanoid robots, then?

[ Unitree ]

Quite a story, but is it a real job?

[ EngineAI ]

If you have a cute animal logo for your research, I will always share it.

[ BIEVR-LIO ]

This is very delicate work, although the real challenge would be picking those nuts out of a jumbled bin full of randomly sized nuts, which is how most of us live our lives.

[ Sanctuary ]

Not for me, thank you, although I’m not saying that most of the other humanoid robots out there are any better looking, fundamentally.

[ UBTECH ]

Robotics professor Dr. Christian Hubicki judges robot soccer skills while knowing very little about soccer himself.

[ ORL ]

In this presentation, Brendan Schulman, vice president of policy at Boston Dynamics, outlines the critical role of government engagement in driving the success of the humanoid robotics industry. He demonstrates how legged robots like the Spot quadruped and Atlas humanoid are moving beyond factory settings to deliver real-world value in infrastructure inspection, industrial manufacturing, and public safety. Schulman highlights the intersection of AI and robotics, showcasing how large behavioral models and reinforcement learning enable robots to navigate slippery floors and autonomously avoid workplace hazards. Ultimately, he calls for a proactive national robotics strategy focused on workforce training, safety standards, and ethical frameworks to support supply chain resilience and global competitiveness.

[ Humanoids Summit ]

Borys Drozhak has a vision: a front line almost free of humans, patrolled by flying drones and ground robots, and continuously monitored by AI-controlled sensor networks. And it’s not a pipe dream. Ukrainian roboticists have made major strides in that direction over the past four years. Remotely controlled ground vehicles fitted with machine guns and grenade launchers now patrol the no-man’s-land straddling the front, part of a robotic legion that has stymied Russia’s territorial ambitions so far this year.

Drozhak is a co-founder and CEO of RoverTech, which manufactures the Zmiy, one of Ukraine’s most successful ground robots. Zmiy, Ukrainian for snake, is an 800-kilogram (1,700-pound) rover, 2.15 by 1.5 meters in size, with 75-centimeter diameter wheels. The Zmiy comes in various configurations—for demining, logistics, fighting fires, firing a machine gun, or launching grenades.

According to Drozhak, the uncrewed ground vehicle (UGV) is a record-breaker among Ukrainian ground robots. It’s engineered to be nearly noiseless and emit as little heat as possible, helping it to elude Russia’s intelligence, surveillance, and reconnaissance (ISR) drones. As a result, a Zmiy rover completes on average 57 missions across the kill zone before being destroyed. The kill zone is the roughly 35-kilometer-wide swath of land that straddles the front line; its width is variable and determined mainly by the growing range of the drones.

“Usually, a UGV on the battlefield lasts about seven missions,” Drozhak says. “The Zmiy is quite a bit bigger and stronger” in comparison with most other UGVs, “and can make it back even if two of its wheels get destroyed.”

Drozhak is a software engineer turned roboticist whose story is echoed everywhere in the Ukrainian defense establishment. Before the Russian invasion, he was living a quiet life in Ireland, working for an international software development firm. He returned home shortly after the war began to help defend his homeland. Together with his friend, Vasyl Korenovskyi, who had been a mining engineer, he founded RoverTech with the goal of building robots to perform some of the most dangerous tasks in the war zone. In 2023, they rolled out their first product—the Zmiy de-miner. Earlier this year, one of RoverTech’s assault UGVs was part of a widely reported operation that forced a group of Russian soldiers to surrender without the presence of any Ukrainian troops. Such feats, Drozhak insists, are not rare on Ukrainian battlefields these days.

UGVs are the latest chapter in the military-technology race spurred by the war in Ukraine. Scores of Ukrainian startups have developed dozens of different small ground robots, each with typically multiple variants, over the past three years. They’re mostly replacing human-driven tanks and other military vehicles that used to crisscross the war zone. These remotely controlled robotic vehicles cost a few tens of thousands of dollars apiece compared to millions for a traditional tank, and they can be tweaked and modified in frontline workshops to serve the most urgent needs.

Zelenskyy Orders Up 50,000 More UGVs

In April, Ukraine’s President Volodymyr Zelenskyy signed an order for the government to procure 50,000 UGVs for Ukraine’s military forces by the end of 2026. That’s more than three times as many as the government purchased in 2025 and a massive increase from the 2,000 procured in 2024, according to defense analyst Marc C. Lange.

The rise of UGVs, Lange explains, is a direct response to the warfighting revolution ushered in by the speedy evolution of uncrewed aerial vehicles that came to define the war in Ukraine.

As the number of drones zooming above the front line rose and their range increased, the battlefield became completely transparent. Today, anything that enters the kill zone gets hit by a first-person view (FPV) kamikaze drone within minutes.

“Any armored formation, any resupply and logistics vehicle, and any manned formation anywhere near the edge of the battle area has between seconds to a low amount of minutes before it gets turned to dust,” Lange says. “The Ukrainians were losing drivers. Traditional methods of evacuating injured soldiers became impossible. That space is basically unsurvivable.”

Ukraine, suffering from a shortage of infantry, has taken that problem more seriously than Russia, which has a larger pool of fresh recruits to draw from. UGVs began ferrying supplies to troops at frontline positions in 2024. Gradually, they took over the complex and risky evacuations of the wounded, using special enclosures to protect the soldier being transported. But this year, Lange says, is “the year of the assault UGV.”

Emerging Ukrainian tactics combine UGVs with real-time reconnaissance and surveillance from aerial drones, which discover enemy troops, often under cover of night. The reconnaissance data are then used by remote operators who guide UGVs as they stalk, corner, and shoot to kill. Oleg Fedoryshyn, the head of research and design at DevDroid, another prominent Ukrainian UGV developer, said the ground robots can be controlled from as far as 100 kilometers away using Starlink connectivity, LTE networks, or mesh-networked military radio systems. The UGVs can also carry strike UAVs (uncrewed aerial vehicles), serve as communication relays for drones, or carry and launch communication relay drones that further extend the range of the attack vehicles. The UGV can lurk in position for up to one week without needing a battery charge, Fedoryshyn said, and wait for the enemy to move closer.

“It’s better than to put people there,” he notes. “A guy with a machine gun is always the first target for the enemy.”

The Droid TW 12.7, by DevDroid, is shown here outfitted with a 0.50-caliber M2 Browning machine gun that can be aimed and fired by a remote operator using a tablet and an encrypted communications link.DevDroid

Fedoryshyn estimates that UGVs could eventually help cut the number of soldiers needed along the front line by 30 to 40 percent. Drozhak is even more ambitious. He envisions a future front line that’s entirely automated, relying on sensors and other systems that are only occasionally serviced by humans.

A guy with a machine gun is always the first target for the enemy.

“Right now, we need a lot of UGVs because there are people on the front line and we need to deliver supplies to them,” he says. “But we can substitute many of them with sensor systems, servicing robots, and UGVs, and then we will not need that many for logistics. At some point, we could have only robots in the kill zone.”

Ukraine, with a prewar population of around 41 million, has lost over 150,000 fighters in the war since 2022, according to estimates by the Center for Strategic and International Studies and others. Many thousands of others have been mutilated or permanently disabled. Even those who return without physical injuries suffer lasting psychological trauma. Drozhak dreams that a future robot army would put an end to the ability of autocratic regimes worldwide to brutalize their neighbors.

“There will be no need to push people on the battlefield anymore,” says Drozhak, the RoverTech CEO. “Once we achieve that in Ukraine, any country with a decent economy would be able to defend themselves just with technology.”

RoverTech’s Tarantula active-protection system, which uses acoustic and visual sensors combined with AI algorithms to detect approaching killer drones, is the first step in that direction, he declares.

“The future battlefield will rely on networks of robotic sensors and autonomous systems that can continuously monitor dangerous areas, provide early warning, and reduce the need for soldiers to expose themselves to direct threats,” he says. “Human operators will remain responsible for critical decisions, but increasingly advanced sensing technologies will help move people away from the most dangerous positions on the battlefield.”

Why UGVs Are Vulnerable

Militaries around the world were looking at UGVs prior to Russia’s 2022 invasion of Ukraine. But those were quite different, explains Samuel Bendett, a defense analyst at the consultancy CNA. They were larger, more complex, and conceived to operate in smaller numbers. The more compact forms now seen in Ukraine are the result of an evolution that paralleled that of the first-person view (FPV) attack drones. Both needed to be cheap as they don’t last long and small to be less conspicuous. Now, the West is trying to understand the overall role of UGVs in future warfare. So far, in Bendett’s view, the impact of UGVs on warfare isn’t as profound as that of the FPVs and other aerial drones.

“Not every terrain would be applicable to using a UGV,” Bendett explains. “So far, a lot fewer countries are seeking to integrate them into their combat operations than UAVs, which very much democratized the way of enabling short-range to mid-range strikes against adversaries.”

UGVs, he points out, are much more susceptible to communication disruptions than UAVs, while being less suitable for autonomous operations and swarming due to the complexity of ground terrain.

“With UAVs, communication is much easier,” according to Bendett. “There are no interferences between the ground station and the UAV save the distance, Earth’s curvature, and the radio horizon. But on Earth, there’s lots of different obstacles that interfere with radio signals.”

Most UGVs rely on Starlink as the first choice for operator control, but even that comes with problems. Starlink signals are easily disrupted by trees and buildings. And Russia, having been cut off from Starlink, is working hard to find ways to jam the system.

On top of that, Lange says, as UAV autonomy progresses, UGVs could be left behind. The reason is that UGVs are likely to remain dependent on operator communication links for some time and will therefore be vulnerable to enemy UAVs that can’t be stopped by jamming systems that still provide some protection today.

“The low production cost of strike drones will mean that UGVs will have to endure a barrage of strikes that might be too much,” Lange says. “The question is whether you can make UGVs more survivable on the front line both in terms of command and control and the actual survivability of that many strikes.”

Still, he thinks there’s “no path back from UGVs.” The idea of distributing a whole range of tasks, performed in the past by a single large and expensive tank, to a fleet of small, cheap UGVs provides more resilience against the omnipresent drones. Moreover, although many international commentators now say that Russia appears to be losing, the war grinds on—and so does the cat-and-mouse game of lethal innovation.

Toshio Fukuda has been blazing trails for most of his career. He is considered to be one of the most prolific scholars in robotics, writing more than 2,000 research papers and authoring several books on the field. He’s an influential figure thanks to his pioneering work developing biomedical robotic systems, industrial robots, micro-nano robotics, mechatronics, and AI-driven automation.

Fukuda launched one of the first robotics conferences, the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). It is still popular almost 40 years later.

Toshio Fukuda

Employer

Egypt-Japan University of Science and Technology, in Alexandria

Title

Professor and vice president of research

Member grade

Life Fellow

Alma maters

Waseda University, in Tokyo; University of Tokyo

An IEEE Life Fellow, he is a professor emeritus in the department of micro-nano systems engineering and a visiting professor at Nagoya University, in Japan, where he taught for nearly 25 years. Currently, he is a vice president of research at the Egypt-Japan University of Science and Technology, in Alexandria, Egypt.

Within IEEE, Fukuda has held top volunteer positions including the organization’s highest office: He served as IEEE president in 2020, becoming the first person of Asian descent to hold the role.

He’s a former program director of Japan’s Moonshot program, which by 2050 intends to develop advanced AI robots.

Born in Japan, Fukuda has been recognized by the country for his contributions to science with two of its highest awards: the Medal of Honor with a purple ribbon in 2015 and the Order of the Sacred Treasure in 2022.

IEEE honored him with this year’s Richard M. Emberson Award for “distinguished service advancing the technical objectives of IEEE, especially in the area of robotics.” The IEEE Board-level award is sponsored by the IEEE Technical Activities Board. Fukuda received the award on 24 April at a ceremony in New York City.

As a former IEEE president who has served as a master of ceremonies at several of the organization’s major award events, Fukuda noted that he is more accustomed to bestowing awards than receiving them.

“It’s very interesting to be on the receiving end,” he says.

The journey into robotics research

As a teenager, Fukuda spent his summer breaks teaching himself how to build things including transistor radios and steam engines.

“It was very nice to have a hands-on hobby and make these kinds of things myself,” he says. His experimentation led him to study engineering.

He earned a bachelor’s degree in engineering in 1971 from Waseda University, in Tokyo. He says one of his professors there—Ichiro Kato, regarded as the father of Japanese robotics research—was a good mentor who made a positive impact.

Fukuda’s research interests were robotics and mechatronics, a field that combines robotics, electronics, computer science, and control systems.

He went on to earn a master’s degree and a doctorate in science from the University of Tokyo, in 1971 and 1977. During those years, he also attended Yale, where he conducted research on advanced control theory in 1973.

He reflects fondly on his time at Yale: “It was a very nice environment and a kind of free-thinking atmosphere. It motivated me to study more.”

“IEEE doesn’t care who you are, what you do, what country you are from, or whether you are male or female. IEEE accepts people who have energy and passion.”

While at Yale, Fukuda served as an assistant to his advisor—which led him to consider a career in academia, he says, because he enjoyed the freedom that research work afforded him.

But he realized that such freedom comes with a price. University researchers are expected to raise the money that funds their work. He compares researchers to small-business owners who have to bring in money to keep their enterprise afloat.

That realization led him to select robotics as his field because he intended to develop technologies useful to industry, he says.

After earning his doctorate, he returned to Japan in 1977 to work as a research scientist at the government’s Mechanical Engineering Laboratory, later renamed the National Institute of Advanced Industrial Science and Technology, in Tsukuba.

“There was a lot of research going on at the lab, including practical robotics and theory,” he says.

He left Japan in 1979 to become a visiting research fellow at the University of Stuttgart, in Germany. During his year there, he studied systems, software problems, and related topics.

He returned to Japan and was hired as an associate professor of mechanical engineering at the Tokyo University of Science. He conducted research into practical uses for robots by visiting industrial plants. He decided to develop robots that inspect industrial equipment such as those used in assembly plants, oil refineries, and power stations—places that “can be hostile environments for humans,” he says.

His work drew interest from chemical, oil, and utility companies.

“I got a lot of money from them for this very practical application, which funded my research,” he says, laughing.

Developing popular robotic systems

Fukuda grew tired of making those robots, he says, so he switched to creating ones for scientific applications. He developed many techniques, but he probably is best known for his modular, cellular robotic systems (CEBOTs), which he introduced in 1985.

He has described how CEBOTs work in numerous papers published in the IEEE Xplore Digital Library.

The CEBOT system is composed of a number of autonomous robotic cells that stick together like interlocking Lego plastic bricks, he says.

Each cell is a fundamental modular unit that has a function. When a simple task is given, the system can analyze it and generate the structure of the cellular manipulator. The cells connect to and detach from each other through connection mechanisms and cooperate mutually, creating complex structures and configurations.

“You start developing from the component-wise to the cell-wise to a small functional unit—and then you come up with clusters that make bigger systems. We can make a society of robot beings like that,” he explained in his oral history published on the Engineering and Technology History Wiki. “It’s a distributed robotic system, a self-organized robotic system, and also an evolutionary robotic system.

“It’s also a fault-tolerant robot system because if something is wrong, you just remove those things and make a new one. You keep the system working. That’s a great thing.”

Today CEBOTs are used for a variety of tasks such as delivering medication in hospitals, assisting with planting crops, and transporting products in distribution centers. Check out IEEE Spectrum’s Robots Guide for news from the world of robotics.

In 1989 Fukuda joined Nagoya University as a professor of mechanical engineering and micro-nano systems engineering. During his 24-year career there, he was director of the university’s Center for Micro-Nano Mechatronics. He developed a long list of technologies at the university, including many for medical applications. He also conducted groundbreaking research into intelligent robotic systems and micro- and nano-robotics.

Another technology he is known for is brachiation robots, which he helped develop in 1988. He calls them monkey robots because they’re based on the pendulum-like movement of monkeys swinging from tree to tree. The gravity-based locomotion enables continuous movement.

Brachiation robots now are inspecting high-voltage transmission towers and bridges, searching damaged buildings for survivors, and performing maintenance on pipelines and cables.

Fukuda retired from the university in 2013 and was named professor emeritus.

He didn’t stay retired for long, though. He next held a teaching appointment at Meijo University, in Nagoya, until he left in 2022 to join the Egypt-Japan University.

A prominent volunteer

He joined IEEE in 1980 at the encouragement of one of his research advisors, Professor Fumio Harashima, now an IEEE Life Fellow. After attending conferences and reading the organization’s publications, Fukuda says, he looked forward to becoming more involved.

“I wanted to know how to organize a conference and how to edit a paper for one of its Transactions,” he says. “I wanted to know what was going on from inside the organization, not just the outside.”

In 1988 he was the founding chair and organizer of IROS, in Tokyo. The conference had 330 attendees that year, and was supported by Harashima. Today it is one of the largest and most prestigious conferences on the topic, attracting more than 9,000 people annually. Out of 120,000 conferences, it was the only conference in the Nature Index database for this year, Fukuda says.

In 1996 he and other members launched IEEE Transactions on Mechatronics.

He was the founding president of the IEEE Nanotechnology Council, which was established in 2002. He is considered a pioneer in nanotechnology research, particularly regarding how it relates to robotics.

Over the years, he has held numerous volunteer positions on IEEE editorial boards and committees.

He was the 1998–1999 president of the IEEE Robotics and Automation Society, becoming the first non-U.S. member to hold the title.

He was director of IEEE Division X (2001–2002 and 2017–2018), which covers intelligent systems, biological engineering, robotics, control systems, and photonic technologies. He served as the 2013–2014 director of IEEE Region 10 (Asia-Pacific).

As the 2020 IEEE president, Fukuda saw the organization through the early part of the COVID-19 pandemic. Because of travel restrictions, he realized IEEE should change how it offered its in-person services, specifically educational programs. He encouraged IEEE Educational Activities to develop an online learning platform. The IEEE Learning Network started with just three courses and now offers nearly 2,000 courses, webinars, and learning materials.

An award-winning member

The Emberson Award joins a slew of other recognitions Fukuda has received from IEEE. They include several from the IEEE Robotics and Automation Society: a 2004 Pioneer Award, a 2009 Saridis Leadership Award, and the 2011 Harashima Award for Innovative Technologies. He is also a recipient of the Board-level 2010 IEEE Robotics and Automation Technical Field Award.

He says he feels strongly that IEEE should be a diverse organization that is welcoming to all. As IEEE president, he led efforts to devise a diversity, equity, and inclusion program. Several policies, procedures, and bylaws were revised to give members a safe, inclusive place for discourse.

“It’s important for IEEE to make everyone feel comfortable,” he says. “DEI programs are important. All people should be equal. IEEE doesn’t care who you are, what you do, what country you are from, or whether you are male or female. IEEE accepts people who have energy and passion.

“It accepted me, from the Far East. That’s why I like it.”

You can learn more about Fukuda and his career from the oral history conducted by the IEEE History Center.

“In the future, the relationship between humans and robots will deepen, and the distinction between them will probably disappear.” This prediction, from one of the attendees at the recent Humanoids Summit in Tokyo, might have been unremarkable had it not come directly from an android that was first introduced to the world 20 years ago.

Geminoid HI-6 is the sixth-generation of a robot originally designed in 2006. The mechanical twin of Osaka University professor Hiroshi Ishiguro, Geminoid HI-6 is now equipped with a large language model trained on Ishiguro’s own writings and interviews. It has advanced conversational skills and can even have a chat with its creator, an eerie spectacle. But at the Humanoids Summit, Geminoid was one of the few humanoid robots from Japan, the country that pioneered the form factor.

While the event in Tokyo had only about 40 robots on display, Chinese systems outnumbered Japanese by roughly three to one. Some Japanese robotics firms were even using Chinese robots in their own technology demonstrations, something that would have been unthinkable in the recent past—one Japanese engineer described the situation as “sad.” The conference was a stark reminder of how Japan has ceded its early lead in humanoid robot development to overseas competitors, and the challenge it now faces to secure a place in an ecosystem increasingly dominated by general-purpose robots powered by AI.

Twenty-five years ago, Japan was turning out groundbreaking humanoids that were showstopping in their abilities, but they were not commercialized as practical machines in any meaningful way. Heavily influenced by science fiction and lacking practical applications, they were mostly expensive technology demonstrations that were eventually mothballed. What Japan retains, however, is robotics design and know-how, which it must leverage to be a key player in the rapidly evolving humanoid ecosystem.

Learning to Walk—Then Standing Still

To anyone who has seen recent videos of Chinese humanoids doing kung-fu and synchronized acrobatics, as well as half-marathon races, China’s remarkable progress in the field is nothing new. At the Humanoids Summit, Toyota showed a video of its latest basketball-playing robot, and Honda exhibited its latest robot hand, but the full-scale humanoids on the floor were mostly Chinese–the kid-size K1 machines from Booster Robotics of Beijing were dancing to Michael Jackson tunes. The full-scale G1 humanoid from Unitree Robotics of Hangzhou was also doing demos.

“You cannot sell these bipedal systems in Japan for safety and compliance reasons,” says Shuichi Nagao, a frequent visitor to China as CTO of Omakase Robotics, a division of Zeals, a Japanese humanoid robot developer. Omakase was exhibiting a G1 modified with an external PC controller, a dextrous hand, a suction-cup manipulator and a sensor “hat” with an extra speaker, mic, and camera.

“In China, the government is pushing humanoid development. They didn’t have an industry 20 years ago. The people pushing it are young, in their 20s and 30s. It’s a really different mentality out there,” says Nagao. “Big players in Japan are still looking for use cases for humanoids. In China, they’re already doing mass production and reducing the cost, so other countries can’t compete with them anymore.”

Another Japanese company showing off G1 bots was summit sponsor GMO AI & Robotics, a subsidiary of Japanese internet company GMO. It’s using the robots in partnership with Japan Airlines to load and unload cargo containers at Tokyo’s Haneda airport. The cargo project is a trial—like many other humanoid experiments—but the fact that Chinese machines have penetrated so far into Japan’s ecosystem upends a long history.

In 1973, scientists at Waseda University in Tokyo built WABOT-1, considered the first full-scale humanoid robot, which was capable of slow bipedal locomotion, grasping objects, and simple communication. It inspired Honda’s groundbreaking Asimo humanoid, but Asimo was never commercialized. It was eventually retired in 2022, the year ChatGPT was released. Two years later, Unitree’s G1 went on sale for US $16,000.

China’s High Torque Technology Co. showed off its Mini Pi biped, customized with an anime-inspired head, at Humanoids Summit in Tokyo. The regular version is priced at $3,500. Tim Hornyak

Supply and Demand

Japan’s development of humanoids happened before practical applications or widespread demand were in place, but bad timing is only part of the story—Japan also has a history of developing technologies that might appeal to domestic consumers but not necessarily those overseas. For example, decades after its highly engineered multifunction toilets first appeared, they have only recently found a following abroad.

Japan’s humanoid prowess was partly built on the back of its legendary industrial automation, yet even that stronghold has eroded. Ani Kelkar, a partner from McKinsey & Company in Boston who produces analytical reports about the robotics industry, told the summit audience that while Japan occupied the top spot in the world in manufacturing robot density (the number of multipurpose industrial robots in operation per 10,000 employees) from at least 1994 to 2009, it then slipped to second in 2014, third in 2019, and fifth in 2024. In that year, South Korea was at the top of the leaderboard with a robot density of 1,220 compared to Japan’s 446.

The International Federation of Robotics estimates China now has the most operational industrial robots in the world, with around 2 million total units, approximately 4.5 times more than Japan. “The annual installation numbers are impressive too: 54 percent of all robots installed worldwide in 2024 were deployed in China,” the IFR said in a release in April 2026.

“I think the loss of Japanese leadership is more to do with the rise of China as a manufacturing powerhouse including for sectors that Japan had high export levels,” Kelkar said in an email interview. “The recovery has not yet happened as Japan “missed” the rapid acceleration in AI for robotics and is now playing catch-up.”

How Japan Can Adapt

Kelkar believes Japan has a $100 billion opportunity in general-purpose robotics, which are machines that can perform a wide variety of tasks, and it cannot rely on the slower-growing industrial robot market, which is centered on factory machines that do one simple and predictable task like welding car parts. He points to a McKinsey white paper suggesting that while Japan has much of the hardware and technology experience needed to support general-purpose robot development, it must change its strategy to capture a larger share in AI, software, data collection, and robotics platforms.

Tetsuya Ogata is a professor of engineering and director of the Institute for AI and Robotics at Waseda University, the birthplace of humanoids in Japan. He briefed the summit on how a nonprofit he chairs, the AI Robot Association (AIRoA), is working with Toyota and other members to develop foundational technologies for collaborative use.

For instance, AIRoA has collected some 80,000 hours of data on remote operation of mobile manipulators, which Ogata believes is the largest dataset of its kind. Using the data, it built and verified vision-language-action (VLA) models, and it has also started data collection for dual-arm mobile manipulation. In an interview, Ogata acknowledged Japan’s struggle to find its place in the changing landscape.

“The world of AI is inherently a game of scale,” says Ogata. “Therefore, Japan’s absolute prerequisite is to secure a competitive baseline of scale—in data, computing resources, and talent. Beyond that, what I consider most critical is a mind-set shift: Rather than trying to hoard scale within a single nation or company, we must grow stronger by collaborating with a diverse ecosystem of domestic and international players.”

Specifically, this means creating a “collaborative domain” to address data—the single biggest bottleneck—through industry-wide cooperation rather than data siloing. By collectively nurturing a precompetitive, shared data infrastructure and foundation model, individual companies can then compete on top of it with their own applications. “By offering this open ‘data ecosystem’ to the world, we can engage global players and establish a ‘third pole’ alongside the U.S. and China,” says Ogata. “I believe this is how Japan can reclaim its global presence.”

In 1999, Japan introduced the world’s first mobile internet services platform. But being first didn’t turn Japan into a smartphone manufacturing or design center—it’s now merely a supplier of parts to other countries that are leading the smartphone industry. If Japan can avoid a repeat of that experience and successfully deregulate, diversity, and commercialize its original humanoid dreams, it stands a better chance of influencing the direction of the industry and reaping billions in value. As automobiles and electronics were pillars of Japan’s industrial strategy in the last century, Japan could make humanoid robots one of its key value generators in the 21st century, an approach that would not only deliver economic benefits but give Japan greater clout in how the industry will evolve. Just like Japanese cars, electronics, and even toilets, Japanese humanoids could stand for craftsmanship and reliability. It’s a legacy that Japan can’t afford to give up.

This article appears in the September 2026 print issue as “Japan Seeks a Humanoid Robot Comeback.”

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.

RSS 2026: 13–17 July 2026, SYDNEY

Summer School on Multi-Robot Systems: 29 July–4 August 2026, PRAGUE

Actuate 2026: 18–19 August 2026, SAN FRANCISCO

IROS 2026: 27 September–1 October 2026, PITTSBURGH

Enjoy today’s videos!

NASA is considering a mission concept for an advanced, nuclear-powered rover to be deployed to the Moon’s South Pole as part of the agency’s Moon Base plans. The PROMISE (Polar Rover for Observation, Mapping, and In-Situ Exploration) mission concept relies on the Curiosity Mars rover mission’s testbed rover. Some elements of the Perseverance Mars testbed rover shown in this video could be used as well. As exact duplicates of Curiosity and Perseverance, the testbed rovers are equipped with flight-proven engineering systems capable of carrying technology as well as science instruments that would advance Moon Base efforts.

A Mars rover for the Moon? That’s some OPTIMISM right there.

[ JPL ]

This is the absolute best thing since Festo’s AirPenguin.

The project explores soft, lightweight robots that can gently float around people in indoor environments and invite playful, affectionate, and everyday interactions. Unlike conventional drones, our robot is designed to be quiet, soft, touch-safe, and socially approachable. Through this work, we ask what future indoor companion robots might feel like if they were not rigid machines, but gentle floating beings that share space with us.

[ Paper ]

Thanks, Mingyang!

Today, we’re launching our home robot, Isaac 1. Deliveries will begin this fall.

US $500 per month, with some basic task autonomy, plus teleoperation.

[ Weave Robotics ]

A couple of things from this new Figure video: Thing one is that the cart-pulling is a good illustration of how clumsy humanoid robots still are at basic tasks relative to humans. Thing two is that there are absolutely no humans anywhere near these robots. You can see one guy at 0:19, which I can only assume is an accident, because these robots are not safe to be around from an industrial safety perspective.

[ Figure ]

Our very own Kohava Mendelsohn met some robots at ICRA in Vienna, and only one of them was murderous.

[ ICRA 2026 ]

Welcome to Robot Park, where we’re building the future with Apollo 2. Robot Park is where Apollo learns today, getting the experience needed to make a difference tomorrow. Today we’re announcing Robot Park, our nearly 90,000-square-foot facility where Apollo 2 is collecting real-world training data needed to advance autonomous humanoid robots.

[ Apptronik ]

UBTech Robotics, the world’s first publicly traded humanoid robot-maker, has launched a humanlike robot that features lifelike silicone skin and “emotional AI,” as Chinese tech firms increasingly transition robots from the factory floor to the family living room.

[ SCMP ]

Spherephones are redefining how we experience sound. Created at Georgia Tech, this wearable uses spatial audio to alert users to movement from every direction—including behind and below. Built for safer human-robot collaboration, the technology is expanding into gaming and accessibility applications. See how music is becoming a new language for awareness and interaction.

[ Georgia Tech ]

Humanoid robots are meant to carry out long-horizon autonomous missions in a world built for humans. This is hard. These missions consist of many steps, each of which requires them to perceive, navigate, and interact with the environment. This is exactly Flexion’s goal: building the general-purpose intelligence that turns any robot into a useful helper.

[ Flexion ]

We’re introducing KinetIQ Ascend—our reinforcement-learning approach designed to reach 99.9 percent manipulation reliability at human speed and beyond.

[ Humanoid ]

Dr. Sebastian “Basti” Scherer has worked in field robotics since the first DARPA Grand Challenge in 2004. He runs the AirLab at Carnegie Mellon’s Robotics Institute and is the director of safe embodied AI at FieldAI. While much of the industry is focused on local skills like tabletop manipulation, Dr. Scherer sees the greatest value in solving dirty, dull, and dangerous tasks that require operating in uncertain environments where the robot needs to “just work.” When robots “just work,” they become less like robots and more like tools. “That’s the big challenge that we have to overcome,” he says. “And that’s the challenge that FieldAI is really primed to solve.”

[ FieldAI ]

Look, I really appreciate how valuable robots like ElliQ can be, and robots that do good work and offer a financial benefit are incredibly important, especially in the context of family care. But in my opinion, you really shouldn’t suggest that a robot with FaceTime or whatever is an equal replacement for in-person human companionship, nor should you suggest that AI can replace a human wellness coach. If you can’t afford those things, then sure, ElliQ can offer some of those capabilities in a very limited way, but that’s all.

[ ElliQ ]

Very cool moves! Now get a job!

[ DEEP Robotics ]

Drawing inspiration from restaurant waiters in Morocco and Turkey, among other places, we equip a robot with a hanging tray to transport objects from one location to another without dropping them or spilling their contents. We incorporate this approach into an interactive robot waiter demonstration, which uses computer vision and visual servoing to steer toward a person with a raised hand to serve them.

[ Paper ]

If you’re going to make robots wear skirts or shorts or pants, you have to give them butts, or it’s just not going to work. That is all.

[ TechShare ] via [ Kazumichi Moriyama ]

It’s Los Alamos, so of course we have robots. Some work inside gloveboxes, while others probe unexploded ordnance in the field and aid with repetitive lifting, Doc Ock–style. Legend has it there’s a fro-yo robot in the cafeteria.

[ LANL ]

Here are a couple of talks from the recent Humanoids Summit in Japan, from Ali Agha of FieldAI as well as Hiroshi Ishiguro.

[ Humanoids Summit ]

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.

RSS 2026: 13–17 July 2026, SYDNEY

Summer School on Multi-Robot Systems: 29 July–4 August 2026, PRAGUE

Actuate 2026: 18–19 August 2026, SAN FRANCISCO

IROS 2026: 27 September–1 October 2026, PITTSBURGH

Enjoy today’s videos!

The best way of introducing a new robot hand is to have a disembodied one crawling across a table.

[ Tangent Robotics ]

MIT CSAIL’s Improbable AI Lab Director Pulkit Agrawal explains his “SoftMimic” approach to making robots safer around humans.

[ SoftMimic ]

I now have absolutely no interest in a humanoid robot for my home unless it can do this.

[ PNDbotics ]

The DARPA Lift Challenge is open to the public August 6-9, 2026, at the National Museum of the US Air Force.

[ DARPA ]

Getting Digit to step and shuffle around an obstacle on the floor is a real test of reactive footstep planning. Digit has to spot something small and moving, recalculate where to place each foot, and keep working—all without breaking stride or losing balance. That’s the same dynamic footwork Digit uses to navigate clutter and foot traffic on a real warehouse floor.

[ Agility Robotics ]

This is the most aggressive firefighting robot I’ve ever seen.

[ DEEP Robotics ]

Wait a sec, Dusty can print things on floors besides construction layouts? How is this not in every city, making sidewalks exciting and fun everywhere?!

[ Dusty ]

I am the first to admit that for US $4,900, the performance of the Unitree R1 is very impressive. But what is it going to do out in the world such that it will give you some sort of return on that investment?

[ Unitree R1 ]

Event cameras are extraordinarily powerful because they can see motion, but what if everything is moving because your camera is moving? Oh no!

[ University of Zurich Robotics & Perception Group ]

Can we understand whale behavior and language? Harvard SEAS Professor Stephanie Gil explains the possibility of understanding animal language and behavior using AI-driven robots and machine learning. With ongoing whale research and advancements in artificial intelligence, the potential for animal communication with whales could become a tangible reality.

[ Harvard SEAS ]

Rodney Brooks, founder and chief technology officer of Robust.AI, sits down with Forbes Assistant Managing Editor Kerry Dolan to discuss how he came up with the idea of the Roomba vacuum cleaner and the future of robotics.

[ LinkedIn ]

Here are a couple of interesting presentations from UIST 2025, including everyday objects that move around your home with a mind of their own and a project featuring teamwork between helium balloons and ground robots called Buoyancé.

[ UIST 2025 ]

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.

RSS 2026: 13–17 July 2026, SYDNEY

Summer School on Multi-Robot Systems: 29 July–4 August 2026, PRAGUE

Actuate 2026: 18–19 August 2026, SAN FRANCISCO

IROS 2026: 27 September–1 October 2026, PITTSBURGH

Enjoy today’s videos!

Eno is our first agentic robot: an AI agent and a general-purpose robot working as one system. It reasons, plans, and acts in the real world. Human in capability, not in form. Every detail with a purpose, reduced to what matters. Designed not to resemble us, but to extend us. Eno is built end to end at Genesis.

[ Genesis ]

Engineers from NASA’s Jet Propulsion Laboratory are field-testing advanced capabilities for potential future Moon and Mars rovers. In the Colorado Desert near Plaster City, California, teams used a prototype rover called ERNEST (Exploration Rover for Navigating Extreme Sloped Terrain) to test software for a potential future long-range lunar mission. The software enables the rover, developed at JPL, to operate autonomously and travel extreme distances with minimal intervention from human operators.

ERNEST is a lot more capable than it may look; here’s some recent research showing the kinds of terrain it can handle:

[ NASA's Jet Propulsion Lab ]

Table tennis can produce moments that are difficult even for experienced players to anticipate…like when the ball clips the net and suddenly changes direction. For the Ace research project at Sony AI, these events were a key test of the system’s ability to operate reliably in unpredictable real-world conditions. Ace addresses this uncertainty by simulating counterfactual ball trajectories in real time. In the video, the green overlays show these alternative paths the system considers while planning its response.

And check out some of these rallies that the robot has with Miyuu Khiara.

[ Sony AI ]

This video of an ANYmal deployment in a concrete plant is worth watching because it makes explicit how quadrupeds make money in inspection contexts: Among other things, “a cracked crusher foundation [was] caught before a week-long shutdown, avoiding roughly $630,000 in lost production.” That pays for a lot of robots.

[ ANYbotics ]

A lot of interesting footage here from GITAI’s prep for a robotic satellite servicing demo mission. The thruster test-firing isn’t a robot, exactly, but it may be the coolest part.

[ GITAI ]

Anyone who’s tried to take a half decent photo underwater knows that it’s basically impossible, so let’s try and teach robots to cope.

[ Bi-AQUA ]

Thanks, Masato!

Handling delicate, irregular or unpredictable objects is one of the hardest problems left in automation, and one of the most important. It’s what’s holding back the next wave of robots from doing more in the real world. That’s why we’re working with PSYONIC on a new approach. Their Ability Hand, worn by hundreds of people every day, captures real-world data on touch, pressure and grip. Our GoFa cobot brings the industrial-grade accuracy and repeatability to turn that human data into reliable robotic performance.

[ ABB Robotics ]

Sanctuary AI has achieved world-class performance on a complex wire-plugging production task with a global Tier 1 automotive supplier. In this demonstration, Sanctuary AI’s Physical AI successfully performs a high-speed wire-plug insertion task, achieving a validated task success rate of over 99.5% with a cycle time of just 2.54 seconds, meeting live production benchmarks established by the customer.

WHY IS THIS STRESSING ME OUT SO MUCH?

[ Sanctuary ]

This video is quite obviously fake, but I suppose maybe there’s a market for extra beefy quadrupeds? Maybe?

[ Kepler ]

I cannot overstate how much I do not want any robot to look at what I’m wearing and then attempt to sell me things based on what it thinks it can guess about my personality or interests.

[ MagicLab ]

I am here for fed-up robots learning how to move boxes by just kicking them.

[ ATARI Lab ]

Ah, yes, very useful and very important robots that make me very uncomfortable.

[ Paper ]

I built GrowBot ( a ~6”, two-servo bipedal robot) that runs entirely on a $15 Raspberry Pi Zero 2 W, ~$100 in parts. An LLM drives it directly: it reads the raw IMU stream with no translation layer and narrates its own motion (“rocked side to side like a baby”), riding on a 50-Hz reinforcement-learning walk policy trained in sim and transferred to the real body.

The idea here is to build an open course around this project, Brit says, “so everyone can experience physical AI right now in a low-risk way.”

[ GrowBot ]

Thanks, Brit!