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!

In 2018, Amazon brought me in as the lead UX Sound Designer for Astro, its first consumer home robot. Astro used cameras and other sensors to map and navigate your home and workplace, and could proactively patrol, check up on loved ones, and transport small items using its built-in cargo bin. While there was a well-defined feature set and form factor, initially there was no character direction. In fact, even before Astro had a name, there were two main questions—was it simply Alexa on wheels, or was it a robot with its own character?

The Astro team was divided. One option was to focus on Alexa, and treat the mobile robot simply as an added utility. Along with the majority of the UX team, I argued for Astro to not focus on Alexa. Our belief was that a thing that moves through your home and turns toward you with intent can never be just an appliance. People would ascribe character to it whether we wanted them to or not, and so the only question was whether we shaped that character or let it happen by accident.

Ultimately, Astro became Astro rather than Alexa, and user testing backed up our decision. People didn’t see the robot as Alexa. They saw it as its own character, and that’s what they wanted it to be. Alexa on the device felt somewhat strange and creepy, but building Astro its own voice was too slow and expensive in 2018. So, we settled on Alexa as a supporting character that handled any actual talking, while Astro was the main character, communicating as much as it could without words, through sound, motion, and facial expressions.

I had been brought on to the Astro team to define the robot’s sound design language and voice. But there was no one to flesh out the robot’s actual character. You cannot make a single real decision about a character without defining it first. Every choice about how Astro moved, sounded, paused, or reacted was a character choice, and those choices required all disciplines working together. As sound lead, I was weaving together sound, motion, and character, and how they played together inside each story moment. The animators, who programmed Astro’s motion and facial expressions, were extraordinary at what they did, but the emotional arc they were animating came from the sound (and therefore character) work first. So I stepped into that role, which is where my real work started. What I learned about building character for robots applies to nearly everything being built in embodied AI right now.

Character Is a Design System

Developing a character for Astro meant answering questions that had never been asked about a product at Amazon: What is the emotional range of this robot’s baseline state? How does this robot communicate uncertainty without eroding trust? Where is the line between being expressive and annoying? What are the vulnerabilities of this device’s character?

These are design questions. They have real answers, and every team working on the product has to build from them. For example, Astro’s emotional range was designed to be relatively small at first. We never wanted Astro to get too sad or too angry. It could play sad, but would snap out of it quickly and end the reaction on a high note to keep things positive.

Character leaks out of every seam and can create a disjointed experience if not defined correctly. Even if it’s just animation timing that’s slightly off, or a response that’s technically correct but contextually tone-deaf, users feel every one of these inconsistencies, even if they can’t name them. Watch what happens at the beginning and end of this Sing sequence:

Astro goes from nothing, into the emotional moment, and then lands back on nothing. No buildup, no cooldown, no sense that the feeling came from somewhere or had anywhere to go. I pushed hard for better character stitching, the transitions in and out of expressive moments that make a performance feel continuous rather than assembled, but it never got implemented. The moment itself works. But without the stitching, it reads as a clip playing on a robot rather than coming from within the robot character itself.

Story and Sound at the Beginning

We had decided that Astro would have no spoken dialogue, but it had something that functioned the same way: a vocabulary of sounds, tones, and rhythms that acted as its voice. This vocabulary became the leading output of the character’s personality. The robot’s motion and facial expressions were built around it.

Astro’s wake-up sequence is a great example. Waking wasn’t just a boot animation on the screen; it was an entire performance. Slow and humble at first, the robot oriented itself quietly, then stretched its screen, checked its wheels, and finally, with an upward gesture toward its telescoping mast, it popped it up slightly, and did a little dance of joy. Sound, motion, and eyes hit every beat together in full choreography.

The character’s output in that sequence was first written as a story. Astro is waking up in its new home for the first time. Its main aspiration is to be part of a family, so this is the moment it has been waiting for, this is its purpose. Being the responsible character that it is, it wants to make sure everything is good to go before it introduces itself and starts learning its new home.

This narrative came first because it drove every other decision that we made. After the story was written, sound gave that story a metaphorical voice: the excited tones, the pacing as it checked its wheels, and the bright melodic phrase as Astro looked up at its new family for the first time and introduced itself. Once the sound was laid down, the animation team did their thing with motion and facial expressions, taking cues from the emotional arc the sound had established. Motion didn’t lead—it followed the feeling of the story and the sounds, the same way an animator follows a recorded vocal take.

That wake-up sequence became one of the most-discussed moments in early user testing. People described it as “alive.” What they were responding to wasn’t any single element. It was all three channels (sound, motion, and facial expressions) expressing the same defined character in harmony.

Context Is Where Character Becomes Real

The most compelling characters are defined not by a fixed disposition but by how they respond to their environments and the people in them. They’re still recognizably themselves even as they adapt. This is what I call contextual character. A robot living in a home doesn’t occupy a single emotional state. It moves through rooms with different energy, encounters people in different moods, operates at different times of day, and responds to an endless range of social situations it was never explicitly designed for.

We got close to a contextual character output with Astro’s sound. When a specific piece of environmental context was fed in, the system adapted beautifully, and Astro felt completely alive. But every state like this was still a prediction we made by hand—a situation we had to imagine in advance and design a response for. A random home throws more situations at a robot than anyone can possibly predict, so there was always a longer tail of moments the system was never prepared for.

The difference between a product people describe as “smart” and one they describe as “aware” often comes down to this. Smartness is capability. Awareness is context. Presence is character. And character is always in reaction to the people around it, to its environment, to its own evolving state. That’s what makes it feel like something is emotionally present with you.

This is where AI changes the game for character design in ways that go well beyond what was possible with Astro. AI-driven adaptation doesn’t require the contextual predictions that we relied on. It learns the specific rhythms, preferences, and emotional context of the people it lives and works with. The character doesn’t just respond to context. It grows into it.

What Industry Is Missing

The character and soul of the impending wave of embodied AI products appears to almost always be an afterthought. And character defined late is character defined by default. It becomes the sum of a thousand small decisions made by different people thinking about anything but character. People project character onto devices whether you plan for it or not, especially if those devices move—a robot that moves is already a character. If nobody has designed this character, the result will be products that feel like nothing, or worse, feel confusing and not trustworthy. Technically impressive, but lifeless.

We did not get this fully right with Astro. So many things were moving in parallel that character was rarely treated as a utility, and it made sense why. When you are building a first-of-its-kind product, the things that are the loudest are the ones that break, the deadlines, the costs, the features a customer can point to on a box. Character is quieter than all of that. It’s easy to assume it can come later. On a team as large as the Amazon Astro team, it’s lucky to get any idea onto the road map when it is competing with a hundred others that all feel more urgent in the moment. None of this came from people not caring. It came from character being the kind of thing that is hard to prioritize until you see what its absence costs you.

My Asks to Product Leaders

If you are building a product that will share physical or conversational space with people, three things are worth considering:

Define character before you define interactions. You need a defensible character with enough emotional logic to answer hard questions consistently. Find answers to character questions early, and have every discipline build from the same foundation.

Build story and sound into the character pipeline, not the production pipeline. Story and sound developed alongside character definition has the chance to inform motion, expression, and interaction logic. This requires a different kind of collaboration, and a different kind of hire.

Design for adaptation, not just consistency. A consistent character is necessary, but the products that will matter most in people’s lives are the ones that deepen through use. The infrastructure to support that is more and more accessible, but the design thinking to take advantage of it is still rare.

An expanded version of this story is available on Medium.

On 19 April 2026, the Honor Lightning humanoid robot ran a half-marathon in 50 minutes and 26 seconds, beating the human world record by 7 minutes and the best robot time from 2025 by almost 2 hours.

How did Honor do it? Is there some magical technology or technique that unlocked this performance? How did the company beat the significantly better-known Unitree (which reportedly had to supply its robot with an ice backpack to try and complete the race without overheating)? My doctoral thesis involved building and controlling hopping and running robots, and since then I’ve tried to design and build efficient commercial legged robots, giving me a decent idea of the constraints involved. In this article, we take a look at the fundamental underlying constraints to try and answer these questions.

The Physics of Running

Running consists of alternating phases of a leg pushing against the ground (“stance phase”) and the body flying through the air (“aerial phase”). In the aerial phase, the body falls due to gravity, losing vertical momentum. The leg in stance phase pushes against the ground to redirect the vertical momentum upward, while the other leg swings forward to reposition for the next foothold.

Electric motors use energy to produce torque—the higher the torque, the more energy is lost as heat. Adding a gear train after the motor amplifies its torque and reduces its speed. A large reduction helps with torque production, but since the rotor of the motor itself has to spin faster, it becomes very sluggish at accelerating its output. This is obviously bad for the swing phase described above. These competing effects mean that for a particular motor, there is usually a sweet spot for the gear ratio:

The power consumed by a robot leg is minimized at an optimal gear ratio (30:1 in this example).Avik De/Datawrapper

How Honor Did It

While the Lightning’s motor specifications are not published, the hip and knee motors roughly have a 110-to-150-millimeter outer diameter. For an approximate set of motor parameters, I looked to the ILM115x25 motor due to its relevant size and detailed specifications.

We can use a simple physics model to estimate the power consumption for running at 7 meters per second (the Lightning’s average half-marathon speed) as gear ratio varies:

The light blue curve shows how to pick the optimal gearing (45:1). The dark blue curve shows how much heat will be produced in the knee motor, ~150 watts for the optimal gearing.

We see that the drivetrain is not magical: with a gear ratio chosen for this task (we’ll return to this below), the approximate robot power consumption would be a very reasonable 400 watts.

However, the dissipated knee power ( typically the main thermal limiting factor) is approximately 150 W. This is almost an unavoidable consequence—running at human speeds with a humanoid-size robot will inevitably generate this amount of heat! Over a prolonged period, keeping the motor from overheating would be a challenge, but the Lightning has a trick up its sleeve:

According to Honor, the liquid-cooling pipes penetrate deep into the motors like capillaries. The high-power liquid pump has a heat-exchange flow rate of more than 4 liters per minute. Each of the four drive motors in the lower limbs is equipped with an independent liquid-cooling circuit.

Liquid cooling is not new, but it’s definitely not a commodity. It has shown up in research periodically, and on the commercial side Apptronik tried it for a few of its prototypes but (to my knowledge) does not use it on its main Apollo platform. Basic air-convection-based cooling would not continuously be able to extract 150 W out of the knee motor, and so the cooling technology is a key enabler of this type of performance.

Why Others Couldn’t Compete

Why did Honor’s competitors, including more established and widely shipped humanoids such as from Unitree or Agibot, not compete as well?

We can use the same model to generate an equivalent energetics plot for walking at 1.5 m/s, a much more modest but potentially more common activity for a commercial humanoid robot:

The solid and dashed light blue lines show a running-optimized design, while green lines show a walking-optimized design. The optimal ratio for walking is much lower (30:1 vs. 45:1). However, the power dissipated in the knee motor while running [dark blue] is much higher at 30:1 vs. 45:1—the price to pay for running with a walking-optimized design.Avik De/Datawrapper

The plot adds a new green curve for the walking power, and the optimal gearing is significantly different!

Let’s say you design your robot to excel at the normal walking task and choose the green design with 30:1 gearing. The knee motor power to run a half marathon is over 300 W (red arrow), more than two times what we had with the running-optimized design. It wouldn’t be so surprising to need ice packs!

Conversely, visually following the green curve shows that the running-optimized robot wastes more power for walking. Using larger motors sized for running increases the weight of the robot and wastes power when it is standing or walking. The larger motors also pose practical issues like bumping into objects while operating in homes or factories.

Closing Thoughts

Honor’s half-marathon performance was an impressive engineering effort and result. It didn’t need any magical leaps in technology, but the deployment of the capillary motor cooling solution is a notable advance without which this running pace would have been unsustainable. The cooling, weight optimization, and robustness advances may well be useful for more practical purposes like carrying heavy payloads down the line.

The Honor Lighting robot [right] has much larger motors driving its legs than the Unitree H1 robot, making it a more efficient runner but a less efficient walker.Left: Wei Zhiyang/Zhejiang Daily Press Group/VCG/Getty Images; Right: VCG/Getty Images

However, the Lightning is not as well-suited to other tasks as a robot designed for greater versatility. Engineering is always characterized by trade-offs, and making the correct ones separates good products from great ones. With consistently improving AI language models, this very human skill is becoming the most valuable one an engineer can have.

The news coverage seemed to overly focus on the fact that the human half-marathon record had been broken by a robot. Machines and humans have very different capabilities and constraints, so why should we ever have expected the half-marathon time for a robot and human to be related? As in Deep Blue’s 1997 defeat of Garry Kasparov in chess, where it couldn’t physically move the pieces, the Honor robot’s capabilities are much narrower than a human running elbow to elbow with other runners while visually navigating the course without GPS. Comparing the robot runner to a human runner is just an apples-to-oranges comparison, which only risks diminishing Honor’s engineering achievement on one hand and human athletic achievement on the other.

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

As robots advance in terms of dexterity and other physical capabilities, it becomes more likely that humans may find themselves working alongside them. If that happens, how will robots’ emotional capabilities need to advance for them to successfully work with people?

In a recent study, researchers trained collaborative robots to read human emotions by not only accounting for facial expressions, but also contextual factors in the interactions as well. Through experiments with 40 volunteers, the researchers then evaluated how a robot’s ability to read human emotions and adjust its behavior in turn impacted a human’s perception of the robot and its capabilities as the two collaborated on tasks. The results—which show that the emotional capabilities of robots only go so far with humans—were published 18 May in IEEE Robotics and Automation Letters.

Seung Chan Hong led the study as part of his undergraduate thesis while studying at Monash University, in Melbourne, Australia. He notes that, while there has been a lot of hype in the advancing physical abilities of robots, this is only one piece of the puzzle. “We need to also innovate when it comes to them actually interacting with humans, not just their physical capabilities,” he says.

This prompted him to dig deeper into the emotional aspects of human-robot interactions. First, Hong and his co-authors decided to train a robot to read human emotions using a vision language model (VLM), which is similar to large language models (LLMs) such as ChatGPT, but which can also take visual inputs.

Training VLMs for Human Emotion Recognition

To evaluate their VLM, which used Gemini 2.5, the researchers had volunteers watch videos of robots handing over objects to humans—with varying degrees of success—and describe the emotions the humans were expressing. Importantly, the volunteers labeling these videos were able to take into account more context in these interactions, rather than reporting solely on the facial expressions of the humans in the video. For example, a person pausing to think with a furrowed brow may simply be concentrating on their task at hand and not necessarily be angry. Contextual factors such as drumming their fingers, pursing their lips, or other behaviors can point to the real cause of a person’s furrowed brow.

The researchers then compared their VLM to a conventional AI system that relies on standard facial analysis and object tracking that is used in human-robot interactions. They found that the VLM outperformed the traditional approach. On a scale from 0 (no similarity in meaning to the emotion identified by the human volunteers) to 1 (a perfect match in meaning), the conventional AI system achieved a score of 0.77. In comparison, the VLM achieved a score of 0.86.

Hong says, “I think [the VLM] was able to align with what human observers were seeing a lot better, because it wasn’t just looking at the person’s face for a brief amount of time, but seeing the whole scene—where the person was and what they were doing, and how they were interacting with the robot.”

In a second experiment, the research team asked 40 volunteers to interact with a robot using their VLM—but purposefully programmed the robot to make an error. The robot then had to offer either an emotionally adaptive apology that accounted for the human’s perceived response to the mistake or a pre-scripted spoken apology.

Participants overwhelmingly preferred the emotionally adaptive response, with 31 out of 40 people favoring this approach over a boilerplate apology.

However, their survey responses underscored how this emotional adaptivity was far less important than the robot’s functionality. After collaborating with a robot that failed in its task, many participants ranked their trust in the robot as lower, regardless of how it apologized for its mistake. “A personalized apology acts as a social lubricant, but it cannot repair the trust lost by the robot failing its physical task,” Hong says.

Interestingly, the VLM classified the emotions of its human partners similarly to human volunteers who observed an interaction from a third-party perspective. But when the VLM’s assessments were measured against humans’ self-reported emotions during the second experiment—the most accurate descriptions of their true emotions—its ability to accurately predict emotions dropped significantly.

“While the VLM is a good observer of outward social cues, it isn’t a mind reader,” Hong says. “It matched third-person human observers well, but it didn’t always align with the users‘ internal, self-reported feelings.”

Together, these results show that robots are not perfect at reading human emotions. So while people might appreciate their efforts, they still ultimately will want competent co-workers.

This story was updated on 15 June 2026 to correct where the research was conducted and clarify that the researchers evaluated the performance of a pre-trained model.

Yen-Ling Kuo always wanted to understand how things worked. When she was growing up in Taiwan, reading the story of Michael Faraday in elementary school piqued her curiosity about the natural world. During that time, she was introduced to Logo, a computer program with a turtle cursor to help children learn basic coding through hands-on experimentation.

It was Kuo’s introduction to programming logic.

Yen-Ling Kuo

Employer

University of Virginia in Charlottesville

Title

Assistant professor of computer science

Member grade

Member

Alma maters

National Taiwan University; MIT

In high school she learned the capacity computers held. She could write programs that completed tasks independently, she realized.

“Once I discovered how powerful computers could be,” she says, “I knew I wanted to focus on using them to solve real-world problems.”

Kuo, an IEEE member, never lost her interest in the “how” behind processes and tools. Her curiosity, combined with a stint working at a Silicon Valley company, led her to focus on innovations that live at the intersection of cognitive and computer sciences.

Kuo, now an assistant professor of computer science at the University of Virginia in Charlottesville, last year received the IEEE Robotics and Automation Society’s inaugural Outstanding Women in Robotics and Automation Early Career Contribution Award. The award is part of the IEEE-RAS Women in Engineering’s Outstanding Women in Robotics and Automation (WiRA) Paper Awards, which promote excellence and recognize the impact that female researchers have on robotics and automation fields at different stages in their academic careers.

Kuo’s winning paper, “Diff-DAgger: Uncertainty Estimation with Diffusion Policy for Robotic Manipulation,” demonstrates a novel method to help robots better identify and estimate uncertainty when faced with scenarios on which they’ve not been trained. The method reduces the amount of human supervision, improves a robot’s rate of successful task completion, and opens up a path to introduce more complex models with bigger data demands into interactive robot learning.

She says her research will help people working in the robotics and automation fields more efficiently collect the data needed for effective model training.

Silicon Valley’s impact

Kuo earned bachelor’s and master’s degrees in computer science at the National Taiwan University, in Taipei, in 2009 and 2012. As she was nearing completion of her master’s degree, she did what many computer science graduates do: She pursued a summer internship at a tech company.

She spent the summer of 2011 at Google’s campus in Kirkland, Wash., working on the company’s comparison ads project.

When her internship ended, she joined the MIT Media Lab as a visiting student, working on the Open Mind Common Sense project with Henry Lieberman.

As she was considering pursuing a Ph.D., a call from Google changed her plans. The company offered her a full-time role as a software engineer.

“I viewed the job offer as a positive development,” she says. “I believe it can never hurt your future research career to get some real-world experience under your belt.”

She was hired in 2012 and helped build techniques that incorporate computer vision and natural language processing to improve the customer shopping search experience. She led the company’s Shop the Look initiative, a predecessor to Google’s current AI-powered shopping experience. The project connected social media content with search results, something the company had struggled to do in the past.

Kuo and her team were tasked with building a connection between the natural language people use to describe an item and an image that matches the searcher’s intent. It was at a time when the neural network—using deep learning models to power Google products—was gaining momentum at the company. Integrating neural network tools into her work was a requirement—which raised questions for Kuo.

“I was applying the neural network tools,” she says. “But I didn’t have 100 percent certainty about how they actually worked.”

She considered how she could become more knowledgeable about deep learning models. It was a full-circle moment. She decided that after nearly four years at Google, it was time to earn a Ph.D. in computer science. She returned to MIT in 2016.

The question that changed everything

Boris Katz, one of Kuo’s Ph.D. advisors, is a principal research scientist and the head of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL)’s InfoLab. He also led the creation of the START Natural Language System, the world’s first Web-based question-answering system.

When the two met, Katz asked Kuo why she wanted to pursue a doctorate degree. She explained her interest in understanding how neural networks work and in using that knowledge to connect the physical world with human language.

He suggested she attend a summer course at MIT’s Center for Brains, Minds, and Machines, a research initiative that ran from 2013 through 2025. CBMM’s objective was to bring together computer scientists, cognitive scientists, and neuroscientists to understand how human intelligence works. The goal was to use the resulting insights to establish an engineering practice to build artificial intelligence systems.

For Kuo, it was a chance to better understand human intelligence and identify ways it could be replicated in machines.

“It was an opportunity for me to interact with other scientists and gain insight into how people learn, understand, and figure things out in the world,” she says. “I saw it as a very useful and inspiring way to incorporate those ideas into my own research work.”

During her Ph.D. studies, she was a research assistant at CSAIL. The experience helped shape her doctoral research, which focused on building AI systems that apply past learning to new situations. She developed machine learning models to support the efforts, including language understanding and social interactions.

She completed her Ph.D. in computer science in 2022 with a minor in cognitive science.

After graduation, she continued her work and collaboration at CSAIL, particularly on projects that involved the “theory of mind” concept.

Theory of mind spurs innovation

Theory of mind isn’t new, having originated with primatologists studying chimpanzees in the late 1970s. The theory recognizes that others have their own thoughts, beliefs, and perspectives. It’s a skill that allows humans to infer someone’s mental state and predict their behavior without verbal communication.

“It’s like when college roommates are moving into their dorm. They may not talk too much, but they work together naturally to coordinate their activities and accomplish goals,” Kuo says. “They can infer and mentally interpret each other’s behaviors and signals to make decisions and complete tasks without words.”

She brought her theory of mind research to the University of Virginia when she joined as an assistant professor in 2023.

Kuo conducts her research in UVA Engineering’s multidisciplinary cyberphysical Link Lab. Her broad focus is on developing computational models that help robots interpret both direct data and silent signals, from language and movements to a person’s gaze. If successful, it could give robots the same sort of physical and theory of mind reasoning capabilities that power physical and social interactions among humans.

“There are no computational frameworks yet available that will translate this kind of understanding into a robot efficiently,” she says.

She adds that the process to get there begins with improving how robots learn to perform tasks.

The evolution of robot learning

Historically, one way robots learned was to mimic humans. A researcher would manually guide a robot through a task, like cutting an apple, and it would repeat the movements. The robot was successful until the environment changed, such as when its hand was in a different position or the apple was at a different angle. The robot was then faced with a situation for which it hadn’t been trained. Without any data available to help it correct course, the robot would start making small errors that eventually led to a full system crash.

This diagram describes how the robotic gripper’s visual perception and tactile sensing prevents a potato chip from breaking.Xuhui Kang, Yen-Ling Kuo, et al.

To solve the problem, researchers developed the dataset aggregation (DAgger) method. As a robot performed a task, a researcher was on standby to provide real-time corrections during unexpected scenarios. The correction data was continuously added to the robot’s model, teaching it how to recover from mistakes.

To reduce the human monitoring effort, robot-gated DAgger was created to enable bots to query humans when the machines became uncertain.

The most popular approach to make the query decision is to train multiple models to consider when determining a course of action. If the models all agree, the robot proceeds. If they don’t agree, the robot is likely to get stuck and ask for help.

Although the multiple model approach was widely adopted, it has limitations. Practically speaking, as models become more complex, it is hard or impossible to train multiple copies. A more fundamental issue is that disagreement among models doesn’t always imply uncertainty; it could just mean there are different ways to accomplish a task.

The Diff-DAgger solution

That is the gap Kuo’s research team closed with the novel Diff-DAgger research. The approach builds on diffusion policy, a technique that helps robots account for different ways a task can be performed.

The new method repurposes diffusion loss, the signal a robot uses to improve its model during training, as a real-time confidence check. During task execution, the robot computes the signal and compares it against values from its training data using a statistical test. The signal spikes when the robot faces an unfamiliar situation and is uncertain how to proceed. The signal stays silent when the robot’s current action is close to what it learned before.

The spike represents the robot’s ability to self-diagnose and predict an imminent failure. Human intervention is triggered only when the signal spikes. No spike means the robot can be left to complete its decision-making process on its own.

Kuo’s team achieved significant results: Failure prediction rates were improved by 39 percent. Task completion rates were increased by 20 percent, and tasks were completed nearly eight times faster.

Her research at UVA gained attention from the National Science Foundation, which honored her last year with a Career Award, the foundation’s flagship grant for early-career researchers. The five-year US $665,000 grant supports her research that builds computational models for human-robot interactions through theory of mind reasoning.

She also received the Toyota Research Institute’s Young Faculty Researcher Award to teach cars to reason about interactions on the road and with the driver.

As service robots and self-driving vehicles become more available, such works are likely to make interactions between humans and robots more intuitive and useful.

Kuo ultimately wants to build more robust robots that are able to integrate into a social space with humans by engaging with us through grounded interactions, she says.

The impact of IEEE

Like many IEEE members, Kuo was introduced to the organization as a student. In 2018 she submitted her first paper, “Deep Sequential Models for Sampling-Based Planning,” to the IEEE/Robotics Society of Japan International Conference on Intelligent Robots and Systems while pursuing her Ph.D. at MIT. Her IEEE involvement grew alongside her professional career.

“It was a natural segue to transition from student to a full IEEE member,” she says. Today she is an active volunteer with the IEEE Robotics and Automation Society, a reviewer for submitted papers, and a presenter and panelist at conferences.

She says one of the best parts of attending conferences is having the opportunity to engage with students. She also enjoys participating as a panelist at luncheons, she says, because it gives her one-on-one time with student attendees. She can share her knowledge and offer insights as they prepare to embark on their career.

Her goal in the coming years, she says, is to broaden her involvement with IEEE initiatives and branch out to other technical committees. Sharing knowledge and learning from others is essential to anyone’s career growth, she says, and “IEEE offers a great opportunity for both.”

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

Enjoy today’s videos!

We present MotionDisco, a framework that discovers contact-rich, long-horizon humanoid loco-manipulation motions from scratch, without relying on teleoperation or motion retargeting from human demonstrations.

Some of the discovered behaviors are a little nutso:

[ MotionDisco ]

Not sure I’d say any of this is ‘effortless’ but those claws are pretty cute.

[ Deep Robotics ]

It turns out running a workout class is a decent way to stress-test whole-body range of motion. Coordinating fluid movement across every joint at once—timing, velocity, balance compensation—is one of the harder control problems in humanoid robotics.

[ Agility ]

Our very own Gwendolyn Rak made a robotic shoulder-friend at Computer Human Interaction in Barcelona.

Here’s a bit more about it:

[ MIT ]

At AIRoA, we’re bringing robots into real homes. Check out our exclusive first video to see how they work in our development hub and real-life household settings! The project aims to develop home robots that can assist people with everyday tasks and become long-term companions in daily life. In this video, we demonstrate Toyota’s Human Support Robot (HSR) deployed in real homes, where it assists residents with everyday tasks such as tidying rooms and fetching objects.

[ AI Robot Association ]

Thanks, Naoaki!

MIDAS Hand is a fully open-source, tactile-sensor-integrated dexterous robotic hand platform for manipulation, teleoperation, and robot learning research. MIDAS stands for Modular low-Impedance Direct-drive Anthropomorphic Sensing Hand.

[ MIDAS Hand ]

Thanks, Jun Kim!

This video presents a novel flight maneuver for a flying bipedal robot. During forward flight, the robot performs aerial braking by swinging its legs to adjust the orientation of foot-mounted thrusters.

[ Paper ]

Seems like a really good application for autonomy, to be honest.

[ Built Robotics ]

In this time-lapse, controllers on the ground are repositioning Dextre, our robotic handyman currently installed at the end of the Canadarm2. They used Dextre to unload equipment from the unpressurised Dragon trunk. Such a beautiful choreography to watch with Earth in the background!

[ European Space Agency ]

This video demonstrates how AI Sapiens learns and performs humanoid motions from video-based motion capture using only a smartphone camera, without professional motion capture equipment. ROBOTIS plans to release an open-source motion generation and learning pipeline for AI Sapiens, enabling users to generate humanoid motions from video and bring them to the real robot.

[ ROBOTIS ]

NAO LIVES!

[ Maxtronics ]

Tumblenauts are a swarm of minimalist, bacteria-inspired robots designed for collaborative inspection of pressurized microgravity habitats such as the International Space Station. Unlike current intra-vehicular robots that rely on complex actuator-dense mechanisms for precise motion, the Tumblenauts use a stochastic run-and-tumble locomotion and collective cooperation inspired by bacterial colonies.

[ Self-Organizing Swarms and Robotics Lab ]

LUMOS Robotics Founder and CEO Yu Chao officially introduces Project EDGE—inviting global builders, universities, robotics labs, and creative technologists to explore the future of humanoid robotics together. To supercharge the global developer community, we are providing 100 complimentary LUMOS NIX robots to selected global partners.

[ Lumos Robotics ]

How do you progress from early childhood computational thinking to advanced high school robotics? Sphero’s product offerings are intentionally scaffolded to scale for students by building critical skills and concepts at every grade level.

[ Sphero ]

An examination of how socially assistive wellness robots could support the seven dimensions of senior wellness, and how a framework can measure their autonomy.

What Attendees will Learn

1. Why the senior care crisis exceeds incremental automation. Demographic pressure, workforce shortages, and a daily wellness-programming gap all strain traditional care models.
2. What defines a wellness robot as a category. The seven ICAA wellness dimensions and eight properties separate these robots from companion and medical devices.
3. How autonomy can be measured with CRAS. This six-level scale, modeled on the SAEJ3016 driving standard, evaluates four care dimensions.
4. What maps the road to full autonomy. The paper examines technical capabilities, clinical evidence, and a three-phase roadmap toward the early 2030s.

This article is brought to you by AGILINK.

Throughout the exhibition hall at the 2026 IEEE International Conference on Robotics (ICRA), in Vienna, one demonstration seemed to attract a disproportionate amount of attention.

Two robotic hands were making a balloon dog. Slowly and deliberately, the robot twisted a long balloon into loops, bends, and joints without popping it. Visitors stopped, watched, and often returned with colleagues to watch again.

AGILINK’s balloon dog demonstration draws a crowd at ICRA 2026.AGILINK

At first glance, the demonstration appeared almost playful. Among roboticists, however, balloon twisting is widely recognized as an unusually difficult manipulation task.

A balloon is lightweight, highly deformable, slippery, and extremely sensitive to force. Every twist changes its geometry and internal pressure, turning a seemingly simple activity into a continuously changing physical interaction problem.

Humans navigate those changes almost intuitively. While making a balloon animal, people rarely think consciously about force regulation, slip prevention, or contact stability. They simply adjust.

For robots, those adjustments remain remarkably difficult. The challenge is not merely moving fingers to the right positions. The harder part is maintaining stable interaction while the object itself is changing.

Highlights from AGILINK’s ICRA 2026 demonstrations, including visuotactile sensing, in-hand manipulation, balloon-animal shaping, and other contact-rich tasks enabled by the company’s latest OmniHand platform.AGILINK

That distinction helps explain why the balloon dog drew so much attention in Vienna. What appeared to be a dexterity demonstration was, in many ways, a demonstration about contact itself.

As robotic manipulation continues to advance, a growing number of researchers are arriving at a similar conclusion: many of the hardest problems in robotics begin only after contact occurs.

Motion and Contact Intelligence for Robot Manipulation

Balloon twisting combines two challenges that robotics has traditionally struggled to solve simultaneously: long-horizon task execution and contact-rich manipulation.

The first concerns motion.

A balloon dog is not created through a single grasp or twist. It emerges through a carefully ordered sequence of manipulations, each setting the conditions for what follows. A small rotational error introduced early may appear insignificant at first, yet several steps later it can prevent the final structure from forming altogether.

In that sense, balloon twisting is a long-horizon task. Success depends not only on performing individual actions correctly, but also on preserving the future feasibility of the entire manipulation process.

To address this challenge, AGILINK began by collecting demonstrations from professional balloon artists. Human actions were mapped onto robotic hands to establish an initial manipulation policy. But successful demonstrations alone were insufficient.

In practice, some of the most valuable learning occurred when execution began to drift toward failure. Whenever instability emerged, human operators intervened and corrected the manipulation in real time. Those interventions were recorded and incorporated into reinforcement-learning cycles, allowing the system to learn not only how successful demonstrations unfold, but also how experienced operators recover when things start to go wrong.

Through this process, the robot gradually acquired the capabilities required for long-horizon task execution—a collection of abilities that AGILINK groups under the term motion intelligence: the ability to generate actions, coordinate bimanual behaviors, and execute extended manipulation sequences under real-world uncertainty.

OmniHand 3 Ultra-M on display at ICRA 2026.AGILINK

Yet motion alone does not explain why balloon twisting remains difficult. The second challenge is contact.

The robot must continuously regulate force, adjust contact locations, and respond to subtle changes in the object’s state. These decisions are difficult to encode through explicit rules. Even skilled human operators often rely on tactile intuition developed through experience rather than consciously articulated strategies.

Analysis of those interventions revealed that many failures did not originate from incorrect action sequences, but from the breakdown of contact itself.

To better capture those interaction dynamics, AGILINK collected contact-centric intervention data and incorporated those interactions into reinforcement-learning training. Rather than learning only which motions to perform, the system also learned how humans maintain stability when contact conditions begin to deteriorate.

AGILINK describes this capability as contact intelligence: the ability to establish, maintain, and adapt physical interaction as force distribution, friction, deformation, and contact geometry continuously evolve.

The distinction between the two capabilities is subtle but important. Motion intelligence determines what the robot intends to do. Contact intelligence determines whether it can continue doing it. For balloon twisting, both are necessary. One provides the sequence of actions. The other keeps those actions physically viable.

YouTuber KhanFlicks follows OmniHand’s motions while learning to fold a balloon dog at the AGILINK booth.AGILINK

Between a balloon slipping away and a balloon bursting lies a narrow region of stability. Successful manipulation depends on finding that region—and remaining within it throughout the task.

Introducing the OmniHand 3 Ultra-M Dexterous Hand

The balloon dog demonstration showcased a manipulation capability. It also revealed a broader question. How much contact intelligence can be achieved through learning alone? A robot can only regulate what it can perceive. It can only respond as quickly as its hardware allows.

As manipulation tasks become increasingly complex, researchers are finding that progress depends not only on better policies, but also on richer sensing and faster physical response.

That realization formed the backdrop for AGILINK’s second major announcement at ICRA 2026. Alongside the balloon dog demonstration, the company introduced the OmniHand 3 Ultra-M.

OmniHand 3 Ultra-M closely matches the size of an adult human hand.AGILINK

The two exhibits represented different stages of the same technological trajectory. If the balloon dog demonstrated what contact intelligence can already accomplish today, Ultra-M was designed to explore what contact intelligence may require next.

Building Hardware for Contact Intelligence

Roughly the size of an adult human hand, the OmniHand 3 Ultra-M integrates 20 active degrees of freedom within a human-scale form factor.

Its most distinctive feature is a fully direct-drive architecture. By adopting direct-drive actuation throughout the system, the hand is designed to enable faster and more transparent force regulation and higher force-control bandwidth, enabling faster response as contact conditions change. For contact-rich manipulation, responsiveness can be as important as sensing itself.

By adopting direct-drive actuation throughout the system, the OmniHand 3 Ultra-M is designed to enable faster and more transparent force regulation and higher force-control bandwidth, enabling faster response as contact conditions change.

The platform also incorporates tactile sensing across nearly the entire hand. Each fingertip contains a miniature vision-based tactile sensor, while more than 300 three-dimensional tactile sensing points are distributed throughout the palm. Together, they provide information not only about where contact occurs, but how contact is evolving.

The system is designed to estimate pressure distribution, shear forces, local deformation, slip tendencies, and other interaction dynamics that often remain invisible to conventional position-based control systems.

According to AGILINK’s tests, individual sensors achieve force resolution of approximately 0.005 N—roughly equivalent to detecting the weight of a sheet of paper resting on a fingertip. Spatial resolution reaches approximately 0.04 mm, while sensing density approaches 50,000 sensing points per square centimeter.

OmniHand 3 Ultra-M recognizes feather texture through vision-based tactile sensing.AGILINK

For dexterous robots, contact has traditionally been a largely hidden process. Ultra-M is designed to make that process more observable.

Rather than simply detecting that contact has occurred, the system attempts to resolve where interaction is happening, how forces are distributed, whether instability is beginning to emerge, and how manipulation strategies should adapt in response.

The balloon dog offered a glimpse of what contact intelligence can already accomplish. Ultra-M explores a different question: what capabilities may be required to push contact intelligence further?

The Physical World Remains the Hardest Benchmark

The significance of contact intelligence extends far beyond balloon animals. Many tasks that continue to resist automation involve unstable or deformable interaction: cable insertion, garment handling, flexible packaging, delicate assembly, connector mating, tool use, and household manipulation.

These tasks are difficult not because robots cannot reach the correct location, but because maintaining stable interaction after contact begins remains extraordinarily hard.

For decades, robotics achieved many of its successes by reducing uncertainty. Factories were engineered to make robotic motion predictable, repeatable, and highly structured. The physical world behaves differently.

A growing share of robotics research is shifting toward interaction itself—understanding how robots can establish, maintain, and adapt physical contact within environments that remain fundamentally unpredictable.

Objects shift. Materials deform. Friction changes. Contact evolves. Real environments rarely follow scripts. Seen through that lens, the balloon dog was never really about the balloon dog. What attracted attention at ICRA was not simply a visually impressive demonstration, but what it revealed: intelligence in the physical world is ultimately measured through interaction.

As motion generation continues to mature, a growing share of robotics research is shifting toward interaction itself—understanding how robots can establish, maintain, and adapt physical contact within environments that remain fundamentally unpredictable.

For robots moving beyond structured environments and into less predictable real-world settings, managing contact may become as important as motion itself.

Thirteen years ago last August, I was camped out in NASA’s Jet Propulsion Laboratory press room in Pasadena, Calif., waiting to see whether the Curiosity rover would survive its descent and skycrane-assisted landing on the surface of Mars. It did, and it was awesome.

Since then, Curiosity (also known as Mars Science Laboratory) has traveled nearly 37 kilometers, drilled into and sampled 42 different rocks, and as of publication has snapped nearly 763,000 photos. The fact that this robot is still hard at work, getting real science done at the age of 13, is absolutely incredible—not only is Mars an actively hostile environment for robots, but the only kind of maintenance that JPL engineers can do is to send very, very careful software updates.

Nevertheless, the clever folks at JPL have managed to keep Curiosity safe, warm, mobile, and sciencing, despite well-worn wheels and less and less power every day. One of those folks is Alexandra Holloway, the assistant team chief for engineering operations for Curiosity, who spoke to IEEE Spectrum about keeping Curiosity roving, what its future looks like, and how JPL has used that experience to make rovers like Perseverance even more capable.

How astonished should we be that after 13 years on Mars, Curiosity is not only still doing science, but actually getting more capable?

Alexandra Holloway is the assistant team chief for engineering operations on the Curiosity Mars rover at the Jet Propulsion Laboratory.Alexandra Holloway

Alexandra Holloway: I’m astonished! The longevity comes from a lot of ongoing work. It’s not just that Curiosity was built robustly; it’s also because we’re continuously putting in effort to ensure it can continue to have that lifespan. I think about all the different kinds of embedded systems there are, from cars to refrigerators, and none of them have the kind of longevity that we have with the rover. It’s mind-boggling, and it’s inspiring.

Is the Perseverance rover, which is nine years younger than Curiosity, significantly different in terms of its hardware and software?

Holloway: In terms of hardware, the rovers are actually very similar. Both use a RAD 750 processor and have the same amount of memory. However, Perseverance has an extra processor specifically for visual odometry, which allows it to drive autonomously. This difference reflects their primary mission designs: Perseverance was designed for driving long distances, while Curiosity is a mission focused on sampling as it goes. So Perseverance’s onboard scheduling capabilities are there to optimize its driving. In fact, just last year, Perseverance surpassed Curiosity’s driving distance after only about three years on Mars.

Curiosity Rover Memory and Software Fixes

Do you have some examples of significant tweaks the team has made to keep Curiosity roving?

Holloway: One of my favorite examples comes from a processor anomaly that happened on Sol 2172 [Ed. note: “Sol” is the term for a Martian day—about 24 hours and 40 minutes]. Curiosity has two computers, A and B. We landed on A, swapped to B due to a NAND memory anomaly early on (Sol 200). For years, we were chugging along on B, until one day there was a problem—B booted up, but it couldn’t mount its drive partition. We’d never seen this before. To preserve B’s data, we swapped back to A, which we hadn’t trusted in two thousand Sols. A also had a degraded memory, with only two gigabytes of usable storage space instead of four. We painstakingly transferred data from B over to A and then down to Earth, and eventually we ran out of stuff we wanted to transfer, which was really good, because A then started acting funny in the same way it did on Sol 200. It was acting like its memory was coming unsoldered. That’s bad.

We quickly swapped back to B, formatted it, and got it working again. The problem then became that we couldn’t trust A’s memory at all, but we needed a second computer as a “lifeboat” for diagnostics and transfers if B failed again. We realized we had one other place of memory: where we keep our flight software. We have four copies of the flight software (two current versions and two older versions) in different banks of very small amounts of memory, just 32 megabytes each. What if we just jettisoned the old flight software copies and used that 64-megabyte NOR memory as our file system for computer A?

So that’s what we did. It was so elegant! Computer A is operating with less than 1 percent of its original memory, but we can run a mission on it. A small mission, but we haven’t had to jettison any core capabilities. We can still drive, we can manage data, we can even theoretically do science. Everything works fine, just much slower and much smaller. That flight software release was even called “R-Hope“ because we hoped it would work.

What are the constraints on Curiosity’s lifespan?

Holloway: Our biggest hardware challenge is wheel wear. It looks like we’re driving on this sandy terrain with some rocks in it, and our intuition said that we could just drive over these rocks and they’d get pushed down into the sand and it would be no big deal. But what we ended up seeing was that those little rocks are actually the tips of giant boulders buried in the sand, and they’re razor sharp. Our wheels were getting ripped apart driving over them, especially our front wheels, so we started driving backwards.

We also monitor consumables. We consider the number of times we move our actuators. That’s a consumable. Curiosity hasn’t taken a selfie in a while, and one of the reasons is that it’s really hard on the joint actuators. Our onboard memory is a consumable, but surprisingly we’re not anywhere near our life cycle for memory. Our biggest consumable is power; we have an RTG, a nuclear power source, which decreases its output as it ages.

Newer missions are flying Snapdragon [processors], but Curiosity’s RAD 750 is a power hog. One of the things that we’ve rolled out that’s going really well is a way of reducing the amount of time we spend with the computer powered on, by harvesting time when we finish activities early and going to sleep, which lets us turn off the computers and some of the heating. Another thing we’re looking at is doing stuff in parallel when we’re on, like being able to drive or use the arm while communicating with an orbiter.

So power is decreasing, and that’s causing us to do all this parallelism work and become more efficient and nuanced in the way we operate. But we are not having any degraded science output at this time. Our wheels are still going, our arm is still okay for now, knock on wood. I would say maybe the bottleneck is budget.

Curiosity Rover’s Impact on Future Mars Exploration

What have you learned from Curiosity that will improve future missions?

Holloway: As an embedded flight software person, I think about how we can change, add, or modify software capabilities during the mission. There’s definitely a sweet spot for loading and patching flight software—some of these concepts were pioneered on Spirit and Opportunity and then inherited by Curiosity and Perseverance, making it easier to understand and change the software.

Some of the things that I wish we had now on [the Mars Science Laboratory] include a better understanding of where our power is going. I want to see how much power each component is drawing every minute, so that we could architect a software system that could balance loads better. We have some of this information that was built in by the engineers who designed the rover, but as an operator, I want something slightly different. So if I were building a mission, I would have those discussions earlier and get operators into the room to say, “what do you want your data products to look like?”

The key takeaway for designing future missions is to talk to all your users early in the design process. It needs to happen upfront.

What does Curiosity’s long-term future look like?

Holloway: That’s a conversation that happens, and it’s a really delicate one. We have a lot of science instruments, and a lot of them have to do with contact science and sampling and rely on the arm. If we lose the arm, what science can we still do? Well, we have a lot of remote sensors too, like cameras, environmental sensors, and radiation sensors. All of these things are important for the future of space exploration and humans on Mars.

From a power perspective, our RTG is projected to start degrading science output in the sixth extended mission, but we’re going to be fine through 2035 and potentially even beyond that. So we have a long and exciting future ahead of us. We need to figure out the best way of operating within our constraints, but we’re still kicking.

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

Enjoy today’s videos!

It’s been a while since a humanoid robot video actually impressed me, but the beginning of this does.

Hard to know how much of that recovery was luck, though.

[ Deep Robotics ]

When you’re very confident in your MPC-based balance controller...

I feel you, buddy. And thanks for posting this. We’ve all been there, in one way or another.

[ DARoS Lab ]

GENE01 designed from scratch and sent to batch production. Two scalable lower bodies. Physical AI deployed for motor control and world-action modeling. All in three months. Generative Bionics is running.

[ Generative Bionics ]

Alex, the newest humanoid robot built entirely by ‪IHMCRobotics‬, takes its first steps outdoors! This was a significant milestone for our team, especially because Alex is the first humanoid robot developed entirely by IHMC Robotics to venture outside the lab. These outdoor trials were conducted in preparation for a demonstration in Maryland, where Alex later successfully walked completely untethered.

[ IHMC ]

Built on the Enlight platform, Flexiv Mico is a compact dual-arm system engineered for safe, seamless collaboration in any workspace.

[ Flexiv Robotics ]

Is it weird that I’m jealous that robots can have feet that are swappable?

[ Boston Dynamics ]

Midweek at ICRA 2026. Cable-climbing robots that work as a squad: CCRobot-S is a team of robots with reconfigurable cable-driven manipulation that collaboratively inspect and maintain long-span bridge stay cables. Parallel operation for speed, morphological reconfiguration for reach.

[ IEEE Transactions on Robotics ]

I would love to know the story behind this odd choice of hat.

[ Robotis ]

How did Atlas learn football—and why? Go behind the scenes of School of Football and discover a glimpse of the Next of robotics.

What I really want to know is, what kinds of things is it possible to do in football (soccer) when your robot’s joints are not constrained by biology?

[ Boston Dynamics ]

In 1987, Richard Greenhill, a British photographer who was fascinated by (but had no actual training in) robotics, decided he wanted to build a life-size humanoid that could do useful things, like carrying luggage. He was working at a startup called Intergalactic Robots, but he couldn’t convince anyone there to build such a machine, so he set about building one himself, in his attic.

To help with his project, he organized a weekly get-together of a dozen or so like-minded folks. Every Wednesday night, his wife, Sally, would make a big pot of spaghetti, and the group would tinker with components scavenged from old printers and picked up from junkyards. They called themselves the Shadow Group. They eventually constructed several different robots, but their main project was the two-legged Shadow Walker.

In 1987, photographer Richard Greenhill organized a weekly gathering of DIY enthusiasts to work on projects in his attic, including the Shadow Walker. Richard Greenhill and David Buckley

Greenhill’s friend David Buckley, a robotics and animatronics expert he’d met at Intergalactic, sketched out a rough design based on medical textbooks of human bone structure and muscle movement. The robot’s skeleton, made of maple, was greatly simplified—only one bone in the lower leg and a single wide toe on each foot. The ankle’s double-axis design allowed for two degrees of movement. The knee had no complicating kneecap.

Greenhill didn’t want the robot to use motors, so its movement was controlled using compressed air to extend and contract 28 “air-muscles”—his version of a McKibben muscle, invented in the 1950s to mimic musculature with pneumatics. The muscles were connected to the bones across eight joints (hips, knees, ankles, toes), which provided 12 degrees of freedom.

RELATED: The Short, Strange Life of the First Friendly Robot

The robot’s headless torso held the control valves, electronics, and computer interfaces. It stood 168 centimeters tall and 46 cm wide and weighed about 38 kilograms. The group managed to get the robot to stand up reliably and balance itself; it could even regain its center if pushed a little. But walking turned out to be more of a challenge.

Rich Walker joined the group as a teenager and began writing software to get the robot to stand. He was particularly interested in using neural networks to solve balancing problems, although he ran into a number of hardware obstacles, including the unreliability of the sensors and the valves, and the robot’s overall fragility. Over time, Walker and the team developed a standard library of routines to control the robot. Walker wrote a detailed description of the Shadow Walker in 1999, which is available on David Buckley’s website.

The 1st International Robot Olympics

By the time the Shadow Group began developing Shadow Walker, engineers in academia and industry had been working on robotics for several decades. The world’s first industrial robot, the Unimate, debuted in 1961, and in 1967 Donald Michie and others began building a series of Freddy robots to investigate machine intelligence. The IEEE created its first dedicated robotics organization in 1984 when it established the IEEE Robotics and Automation Council, which became the IEEE Robotics and Automation Society in 1987. Also in 1987, the nonprofit International Federation of Robotics was established to promote research, development, use, and cooperation in the field of robotics.

As Shadow Walker pushed the limits for a DIY humanoid robot, industrial humanoids were also gaining ground. In 1986, Honda began working on its experimental (E-series) and later the prototype (P-series) humanoid robots, finally unveiling the P2 in 1996. The P2 stood 183 cm tall and weighed 210 kg. It was the first humanoid capable of stable, autonomous walking. This work eventually led to the development of the groundbreaking ASIMO.

Greenhill’s friend, roboticist David Buckley, consulted medical textbooks to create Shadow Walker’s humanoid design.Richard Greenhill and David Buckley

In the late 1980s, the public was both fascinated and horrified by the potential of robots. Businesses saw robots as a way to increase productivity, while workers worried they would take their jobs. Children viewed them as wondrous toys, while people with disabilities embraced them as tools of liberation. Military experts hoped robots would fight wars without endangering human soldiers, while politicians pondered if robots might eventually get to vote. Philosophers thought robots could challenge our notions of intelligence (and stupidity), while the religious struggled with concerns about the human race in a robot-dominated future.

Shadow Walker’s simplified anatomy included only one bone in the lower leg and a single wide toe on each foot.Science Museum Group

Mowforth himself thought all of the competing visions of robots were overblown. Steeped in machine learning research and robotics development, he knew firsthand the limitations of the state of the art: Robots rarely worked as intended, easily broke down, and glitched over seemingly trivial problems. He envisioned the Robot Olympics as a testbed to assess what the latest generation of robots could and could not do.

At the 1990 Robot Olympics, held in Glasgow, Shadow Walker wore pants to conceal its pneumatic “air-muscles” from competitors.Adam Hart-Davis/Science Source

The call for participation was wide open. Instead of having predetermined categories of competition, the organizers opted to see who applied to compete and then group them based on their claimed capabilities. In addition to picking the winners of individual events, the judges would select an overall Olympic champion based on the quality of the hardware, the sophistication of behavior, and novelty. Other prizes were given for young competitors, technologies that showed commercial potential, and design. In the end, more than 50 robots were entered, from a mix of universities, industry, and hobbyist groups from Canada, France, India, Japan, Mexico, the Soviet Union, the United States, the United Kingdom, and Yugoslavia.

There were plenty of disappointments. Trolleyman, a golf-cart-like wheeled robot, suffered a power failure while carrying the opening Olympic torch through the streets of Glasgow. The pile rug in the arena tripped up many robots that had been trained only on flat, smooth floors. David Buckley later concluded that the events were too difficult, and that the Olympics didn’t push development forward.

Of course, there were winners. In a surprise triumph for vintage technology, the fully mechanical 19th-century Japanese Archer from the Museum of Automata in York, England, won gold in javelin, beating out competitors more than 100 years its junior. The overall Olympic Champion was Yamabico, Shoji Suzuki’s entry from the University of Tsukuba, in Japan, which won bronze in obstacle avoidance and gold in wall following, but was disqualified in the talking category for not speaking English.

The Shadow Group had high hopes for Shadow Walker. Unfortunately, though, it failed to take a step, and the biped race was won by the Cardiff University Biped. Shadow Walker now resides in the collections of the Science Museum in London.

The Legacy of Shadow Walker

In 1997, a paying customer in search of a robotic leg compelled the Shadow Group to get serious and become a registered company. Shadow Robot is now Britain’s oldest robotics company. Rich Walker, who had left the Shadow Group to earn a B.A. in mathematics and a diploma in computer science at the University of Cambridge, joined Shadow Robot in 1999 as technical director. Today he’s the director of the company.

Shadow Robot specializes in durable robot hands rather than walking robots. But the focus on hands is also a legacy of the Shadow Group. Walker remembers that the Shadow Group’s first humanoid hand in the late 1990s was impressive simply for being able to pick up a pint of beer (a smooth-sided, thin-walled glass). Today, Shadow Robot’s hands are testbeds for dexterity. Gone are the pneumatic muscles, replaced by actuators that move each finger with precision. The classic model contains 20 motors, allowing for abductive and adductive movement with 24 degrees of freedom.

Shadow Walker’s operator wore a data suit that captured his movements and allowed the robot to copy them.Richard Greenhill

In a recent blog post, Sejal Parsotomo, senior marketing executive at Shadow Robot, wrote that while humanoid robots are great for public relations, specialized dexterity is key for success: A robot that can walk into your factory may be impressive, but a robot that can reliably manipulate objects is transformative.

In its struggles to take more than a few steps, the Shadow Walker showed the inherent difficulty that robots had in mastering even low-level skills. In August 2025, Beijing hosted the World Humanoid Robot Games. Competing in sports such as gymnastics, soccer, and track events, as well as more “useful” tasks like hotel cleaning and sorting medicine, these robots could literally have run circles around the competitors in the first Robot Olympics 35 years earlier. And yet, there is still so much work needed in order for robots to navigate the human-built environment. Despite the astonishing progress, we’re still not all that close to actually useful humanoid robots.

Part of a continuing series looking at historical artifacts that embrace the boundless potential of technology.

An abridged version of this article appears in the June 2026 print issue as “Learning to Walk.”

References

Richard Greenhill gives an overview of his life and the founding of the Shadow Group in a post on Shadow Robot’s corporate website.

David Buckley has a compilation of resources on the Shadow Biped Walker, including specifications from the 1999 iteration and a brochure from the 1st International Robot Olympics.

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.

ICRA 2026: 1–5 June 2026, VIENNA

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

Enjoy today’s videos!

What is the right number of legs for a robot? Two? Four? No, the answer is obviously all of them. All of the legs.

[ Argus ]

Sigh, yet another skill that I as a soccer-playing human should have but a robot has instead: the rabona.

[ Boston Dynamics ]

Robots are rapidly becoming part of our everyday lives, from drones and industrial machines to home assistants and humanoid robots. As their presence continues to grow, an important question arises: How can we choose the right robot—not only in terms of performance and cost but also in terms of sustainability? This video introduces the Eco‑Score for Robots, a new approach to evaluating the environmental impact of robotic systems. Just as eco-labels help consumers make informed choices in other industries, the Robotics Eco‑Label provides a clear and transparent way to assess how sustainable a robot truly is.

[ Robotics EcoLabel ]

Thanks, Bram!

Uh oh, five-fingered hands.

[ Agility ]

Robotic manipulation has come a long way since the 1990s. We’ve gone from the two-ball paddle juggling robot to AthenaZero, who can juggle barehanded using onboard vision feedback. By moving away from task-specific passive end-effectors such as cups or paddles and using multifingered hands, it can transition between a wide range of patterns including cascade, half-shower, tennis, shower, and box.

There needs to be a robot circus show already.

[ Robotics and AI Institute ]

Zero legs. One hat. $13K.

[ Astribot ]

From its elegant design to the advanced technology powering every step, Luna is more than a machine—it’s a leap into the future.

[ LimX Dynamics ]

Thanks, Jinyan!

You got a quadrotor in my quadruped! No, you got a quadruped in my quadrotor!

[ MARS Laboratory ]

A human hand, a robot’s arm—together tracing circles of trust and precision. No missteps. No hesitation. Just pure, algorithmic grace.

[ UBTECH ]

Low-gravity planetary exploration with a quadruped just looks like fun.

[ Autonomous Robots Lab ]

Here it is, that robot Kool-Aid that everyone seems to be drinking. Including me!

[ Generalist ]

Don’t shoot Mini Pupper!

[ MangDang ]

We show here the ARISTO (Anthropomorphic, Robotic, Integrated-Sensing, Tendon-Operated) Hand. Developed in collaboration with Sony Group Corporation, this research platform is engineered to address the complex requirements of manipulating small, thin, and fragile objects.

[ University of Texas Human Centered Robotics Lab ]

Okay, but did you really have to call it the T800?

[ EngineAI ]

Moby shows what useful mobile manipulation looks like in the real world: picking up, carrying, and placing adaptable payloads. The video shows payload handling across increasing crate loads, including a 50.3-pound load, while maintaining balance, control, and mobility. This is the kind of capability that matters outside the lab—moving real objects, in real spaces, with practical reliability.

[ Noble Machines ]

What does it take to make a robot look human? Harvard SEAS students Hailey Block, Henry Tavistock, and Evan Crowley created “Hollow Minds,” a pair of animatronic heads capable of speaking, blinking, tracking movement, and displaying lifelike facial expressions.

[ Harvard University ]

The longevity here is impressive, but the obvious question here is why the heck you’d ever do this task with a bipedal humanoid robot. It also doesn’t seem to have any error recovery, which is obviously fixable, but highlights the fact that real humans are versatile and humanoid robots are not.

[ Figure ]

Kacper Nowicki, CEO and cofounder of Nomagic, recently sat down for a deep dive into the “humanoid vs. purpose-built” debate during a panel discussion at the Web Summit in Vancouver 2026.

[ Nomagic ]

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.

ICRA 2026: 1–5 June 2026, VIENNA

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

Enjoy today’s videos!

Just months after its debut, Atlas is proving why it is the world’s most capable and dynamic humanoid robot, ready for real work. Lifting a mini-fridge is a feat of strength, but the true breakthrough is in the underlying reinforcement learning and controls systems. The robot is learning to navigate real world adaptability: handling heavy objects by bracing and accounting for the mass and inertia; using whole-body control, not just hands to maneuver; and demonstrating superhuman range of motion and balance. This marks a critical shift in robotics where humanoids move beyond the lab and into dynamic industrial settings.

Watching Atlas move a fridge may be less impressive than whatever the heck it does at 4:10.

[ Boston Dynamics ]

SpikerBot is a robot you teach by wiring neurons, not writing code. Drag spiking neurons in the app, connect them to sensors and motors, then press play. It moves, reacts, and changes behavior based on the brain you built.

Already funded on Kickstarter with a robot kit starting at US $219.

[ Kickstarter ] via [ Backyard Brains ]

Thanks, Greg!

Wheeled-legged robots, which have wheels at their feet and achieve high mobility by coordinating wheel drive and leg drive, have been developed. In this paper, we address the problem of how to draw out the potential task-execution capability of the legs by freeing them from the roles of locomotion through external body support.

[ WiXus ] from [ JSK Robotics Laboratory ] via [ ICRA 2026 ]

A very clever idea for electronics-free, multi-dimensional touch sensing.

[ Nature Communications ]

Using external voice commands, G1 is directly controlled to generate a wide range of actions in real time. This video was recorded in a single take, with on‑site audio recording.

[ Unitree ]

Hummingbirds are impressive flyers, and advancements in high-speed photography, instrumentation, and measurement techniques have revealed much about their aerodynamics, flight behaviors, and wing and body kinematics. However, comparatively less is known about their natural flight dynamics, which is the relationship among a bird’s flight velocities, the control actions of its wings, and the acceleration of the bird in flight. To investigate this, at the Advanced Vertical Flight Laboratory we have designed, built, and flight tested a biomimetic robotic hummingbird on which is implemented the same techniques for flight control as observed in hummingbirds.

[ Advanced Vertical Flight Laboratory ]

I guess if you’re going to make a robot dog, it’s only fair to give it the ability to frolic in the water.

[ MagicLab ]

The original automated layout robot—the one that showed up when the construction industry was pretty sure robots were lame and then proved otherwise. It has printed millions of square feet of layout across thousands of projects. It built an entire category of construction technology. The category of: Stuff That Actually Does Helpful Work on Real Jobsites. But FieldPrinter 2 is here. It’s faster, tougher, smaller, and smarter. So for FieldPrinter 1, it’s time. Time for a quiet retirement. A mug. Maybe a plaque... But nay, good knight! Thou shalt expire in a blaze of thunderous glory!!

[ Dusty Robotics ]

Here’s an interesting idea for an inflatable monocopter drone.

[ AIRLAB ]

Meet the Lynx S10—a compact all-terrain robot built to deliver industry-grade performance in a lightweight form factor under 20kg.

[ DE Robotics ]

Noble Machines builds general-purpose robots for heavy industry, supporting people with the most hazardous and physically demanding tasks. Attendees at NVIDIA GTC 2026 witnessed the power of autonomous industrial work with Noble Machines Moby.

[ Noble Machines ]

I’m sorry, but Lego bricks should be for humans only.

[ LimX Dynamics ]

Need a robot that can go places? Huskies were around way before legged humanoids, and I bet they’ll be around way after, too.

[ Clearpath Robotics ]

I know this little dude is just a research platform at Disney, but I still want one to be my friend.

[ Paper ]

In March 1982, General Motors announced a rapid and aggressive conversion to robotics. By 1990, GM wanted 14,000 robots in their factories doing everything from painting to welding to assembly. Nowadays, we dream of robots in the factories, doing everything end to end. In the dark. Lights out. Guess what? GM dreamed the same 40 years ago, and they spent an estimated US $60 billion to try to make it reality. In today’s video, we look at General Motors and their dreams of the automated, all-robot factory.

[ Asianometry ]

When a group of academics started making open-source robotics hardware, a generation of roboticists got years of their lives back. Now, the bigger challenge is getting robots to think—and that’s starting to be open sourced too.

The shift is still early, but companies including Hugging Face, Nvidia, and Alibaba have all made significant bets on open-source robotics in the last two years, releasing tools and models aimed at the higher-level work of getting robots to reason, decide, and act.

The open source movement that accelerated other AI applications is now being applied to the problem of making robots smarter. If these attempts to bring AI to robotics with open-source platforms succeed, the barrier to building a capable robot could fall as fast as the barrier to building an AI application did.

The world ROS built

Open-source robotics software has been around since the mid-1990s, with early projects like Carnegie Mellon University’s Inter-Process Communication package and the Player Project in the early 2000s laying the groundwork. But these were often tied to specific research groups, and the field remained fragmented.

The Robot Operating System, ROS, changed that when it made its debut in 2007. By bundling tools and attracting more users, it became the de facto standard. The story of open-source robotics, in many ways, starts there.

Despite its name, ROS is not actually an operating system. Rather, it is a software framework that sits on top of Linux and handles robotic fundamentals like moving data between components, talking to hardware, building maps, planning paths, and supporting developer tools, such as data logging and visualization. Before ROS, every robotics team wrote that infrastructure themselves. It often took a year or two before a lab could get to the research it actually cared about.

Brian Gerkey, who helped build ROS in the mid-2000s, says he was drawn to the project because of how much open source had already changed the world, pointing out that nearly the entire internet is built on it.

“I’m a tool builder, and I like to share everything as openly as I possibly can, because I think that’s where we get the most impact out of what we build,” says Gerkey, board chair of Open Robotics and now CTO at Intrinsic, a robotics and AI unit of Google.

As it was developing, the AI community largely took the same approach, sharing research, models, and data openly, and the field accelerated faster than almost anyone predicted. Now some of those same advancements are arriving in robotics.

Open-source AI for robotics

Computer vision, once a hard problem, has advanced dramatically in just a few years, says Spencer Huang, Nvidia’s director of product for robotics. What once required significant expertise can now be done in a few lines of code. Simulation tools have become accurate enough to be useful for training, and access to the tooling that once required a specialized lab is now widely available, much of it open source.

“To get into robotics, you no longer need a Ph.D.,” he says. The result is a much larger pool of people who can contribute, and the field is starting to look less like a specialized discipline and more like a platform that anyone can build on.

Nvidia has built out an open-source robotics stack that covers the full development pipeline. Its Cosmos world models generate synthetic training data and simulate physical environments. Its GR00T models give robots the ability to reason through and execute complex tasks. And its Isaac frameworks handle the orchestration that ties training, simulation, and deployment together. Not everyone needs to train the robots from scratch, Huang says, and most people probably shouldn’t.

“If you gate pre-training, the field just never grows,” he says. “We should be able to provide a high-quality, state-of-the-art pre-trained model that anyone can go and take and fine tune for their own purposes.”

All of Nvidia’s open-source models live on Hugging Face, the open-source AI platform that has become the default place to share models and datasets. Hugging Face launched LeRobot, a community platform for robotics AI, in May 2024. Since its launch, the number of robotics datasets on the platform grew from 1,145 at the end of 2024 to more than 58,000 today, making it the single largest dataset category on the hub.

Hugging Face has also moved into hardware, acquiring robotics company Pollen Robotics. The acquisition came from a realization that software alone was not enough, according to Clement Delangue, Hugging Face’s CEO. The goal, as with the software, was to bring more people in.

The contributors to LeRobot include the biggest names in the industry, academic labs, and hobbyists building robots in their spare time. For instance, earlier this year, Alibaba released RynnBrain, an open-source foundation model for physical AI that the company claims outperforms comparable offerings from Google and Nvidia on benchmarks. That diversity of projects, Delangue says, is important.

“It is not just one model or one dataset or one hardware,” he says. “It is a lot of small contributions that everyone can be part of.”

Commercial incentives muddle the field

The stakes, Delangue says, go beyond convenience. A world where only a few proprietary systems control the robots in people’s homes is a concerning one. “Having robots at home that you don’t really understand, that you don’t really control, that a few people in Silicon Valley control is a scary thought,” he says. “Open source gives an alternative path.”

But getting there is not straightforward. The open sourcing happening now looks different from what produced ROS, which emerged largely from academics pooling their work with no commercial stake in the outcome. The biggest contributors today are companies with clear business reasons to want more people building on their platforms. That’s not necessarily a bad thing, says Bill Smart, a professor at Oregon State University, in Corvallis, who was part of the early open-source robotics community. But the incentives are worth being aware of.

He also worries that the lowered barrier to entry has a downside. Researchers coming from AI without a robotics background are sometimes solving problems the field already solved. A newcomer might spend a week training a neural network to move a robot’s hand from one point to another, unaware that the same task can be accomplished with a few lines of code using decades-old techniques. The incentives are not always pointing in the same direction as the progress.

Smart is not without hope though. Whatever the motives behind the open sourcing, he says, the effect is real. More people are in the field than ever before, the tools are genuinely easier to use, and the community is bigger and more diverse than anything that existed when ROS was getting started.

“Anyone can make a robot move now,” he says. “As an old tech guy, that makes me happy and sad, because I’m no longer special.”

This sponsored article is brought to you by Wetour Robotics.

A field technician on a wind turbine, harness clipped, both hands on a wrench, needs to send a command to the diagnostic device hanging at her belt. A logistics worker on a loading dock, gloves on, eyes on the pallet, needs to redirect a connected lift. A person using an assistive mobility device on a crowded street wants to nudge it forward without taking out a phone or speaking aloud. None of these moments call for a smarter robot. They call for a smarter way to be heard by the machines that already exist.

The industry has been building from one side

The past three years of Physical AI have been a story of remarkable progress on the robot side of the loop. Companies like Boston Dynamics, Figure, and Unitree have advanced actuators, locomotion, and dexterity to a level that would have seemed implausible a decade ago. Google DeepMind’s Gemini Robotics has redefined what vision-language-action models can do in unstructured settings. The trajectory of the hardware and the foundation models is real, and it is accelerating.

But there is another side to this loop, and it has been treated as a solved problem for too long. The interface between humans and machines has defaulted, for 40 years, to three input modalities: screens, buttons, and voice. Each of those assumes the user can stop, look down, and translate intent into structured commands. That assumption breaks the moment the work moves into a real environment. On a turbine. On a dock. On a sidewalk. In any setting where hands are occupied, eyes are committed, or speaking is impractical, the conventional interface stack quietly fails.

Spatial Intent Fusion is the simultaneous processing of three streams of human-centered information, namely spatial position, visual context, and gestural intent: Your body is the interface.

The bottleneck on the human side of the loop is becoming as important as the one on the machine side. And solving it requires a different question. Not how do we make the robot more capable, but how do we let the human participate in the computing system as naturally as the robot already does.

Wetour Robotics’ bet: put the human back into the computing loop

Wetour Robotics is betting that the next architectural leap in Physical AI is not about making the robot more capable. It is about making the human a first-class node in the computing network, with the same kind of low-latency, high-fidelity participation that connected devices already enjoy.

Wetour Robotics’ engineers frame the problem this way: a wristband that recognizes a gesture is not enough. A camera that recognizes a scene is not enough. The information a human carries about what they are about to do is distributed across multiple channels, including where their body is in space, what their eyes are attending to, and what their muscles are preparing to do, and any single channel observed in isolation is ambiguous. Reconstructing intent reliably means fusing those channels at the operating system level, with latency low enough that the loop feels closed rather than mediated.

This approach has a name. Wetour Robotics calls it Spatial Intent Fusion: the simultaneous processing of three streams of human-centered information, namely spatial position, visual context, and gestural intent, fused into a single real-time command for any connected physical device. It is the technical implementation behind a simpler positioning statement the company uses externally: your body is the interface.

Orchestra is a portable intelligent hub running the operating system that handles sensor fusion, intent inference, command translation, and safety arbitration. The reference compute platform is NVIDIA Jetson Orin Nano Super, which provides enough on-device inference capacity to keep the entire control loop at the edge, with no cloud dependency on the critical path. Wetour Robotics

The architecture: three layers, four engines, one loop

Orchestra is not a single device but a layered platform, designed from the start to be sensor-flexible and actuator-agnostic. The architecture decomposes into three perception layers and four coordination engines.

Orchestra itself is the local compute and orchestration core: a portable intelligent hub running the operating system that handles sensor fusion, intent inference, command translation, and safety arbitration. The reference compute platform is NVIDIA Jetson Orin Nano Super, which provides enough on-device inference capacity to keep the entire control loop at the edge, with no cloud dependency on the critical path. Edge inference is non-negotiable for this application. Full-chain latency from biosignal acquisition to actuator command is held under 100 milliseconds, the envelope inside which closed-loop control feels natural rather than laggy.

VisionLink handles visual and spatial perception. Cameras feed into vision models that identify objects, estimate distances, and track environmental context. VisionLink is designed not as a passive recognition layer but as a real-time command generator: its outputs feed directly into Orchestra OS to be fused with biosignal data.

Conductor is the biosignal pipeline. It ingests raw surface electromyographic (sEMG) data from a wrist-worn device, classifies temporal patterns into discrete gestures or continuous control signals, and outputs actuator commands. The technically interesting property of sEMG for this use case is that the signal precedes visible motion. Motor unit action potentials appear at the skin surface roughly 50 to 80 milliseconds before a finger completes the corresponding gesture. Wetour Robotics calls this property pre-motion intent sensing, and it is what allows Orchestra to anticipate user intent rather than react to it.

On top of the three perception layers, Orchestra OS runs four coordination engines. The Perception Engine ingests and normalizes raw sensor streams. The Intent Engine performs Spatial Intent Fusion across modalities, resolving what the user is trying to do given where they are, what they are looking at, and what their hand is signaling. The Orchestration Engine translates intent into device-specific command sequences for any connected actuator. The Safety Engine arbitrates conflicting commands, enforces operational envelopes, and gates execution against runtime safety conditions.

The trade-offs we’re honest about

No system that bridges the human body and the digital world is finished. Three engineering challenges remain open, and the company addresses each with a deliberate trade-off rather than a claim of having fully solved it.

Baseline stability of sEMG under motion. In a stationary user, continuous gesture recognition from sEMG is reliable. Once the user is walking, climbing, or otherwise moving, motion artifacts and electrode drift degrade the signal in ways that are difficult to fully compensate for. Rather than overpromise on continuous control in dynamic settings, Orchestra defaults to a smaller set of robust discrete gestures in complex operating environments, and reserves continuous control modes for contexts where the signal-to-noise ratio supports them.

Miniaturization of edge AI compute. Running the Orchestra control loop entirely at the edge requires real on-device inference, which has historically meant trading off between compute capacity, battery life, and form factor. Wetour Robotics’ approach has been a compact carrier board paired with a thermal design and a battery module sized for all-day wearability. The result is a hub that travels with the user rather than tethering them to a desk, and that performs the full perception-to-actuation loop without offloading to the cloud.

Heterogeneity of third-party device protocols. The actuator side of the loop is a fragmented landscape. Different manufacturers expose different command interfaces, different communication stacks, and different safety conventions, and a Physical AI operating system has to integrate with all of them. Wetour Robotics uses an AI-agent layer to negotiate connection and protocol translation adaptively, so that Orchestra OS can ingest data from a wide range of devices, run them through neural network models that infer human intent, and emit the right command on the right protocol for the device on the other end.

Why this matters, and why it helps the rest of the field

The history of computing is a history of interface revolutions. Command lines gave way to graphical user interfaces, which gave way to touch, which gave way to voice. Each transition expanded who could participate in the system and what they could do with it. The next transition is not about a new screen or a new microphone. It is about treating the human body itself as a participant in the computing network, capable of contributing intent at the same speed and fidelity that any other connected node can.

The history of computing is a history of interface revolutions. The next transition is not about a new screen or a new microphone — it is about treating the human body itself as a participant in the computing network.

This path is not a competitor to the work being done on humanoid robots, foundation models for embodied AI, and dexterous manipulation. It is the missing complement to that work. The hardest open problem for humanoid systems is the data: every natural interaction between a human and the physical world is a potential training signal, and most of those interactions are currently invisible to any computing system. As more humans become first-class nodes in the loop, those interactions become observable, structured, and ultimately useful for training the next generation of embodied AI, including the humanoid robots being developed today.

In other words: putting the human back into the computing loop is not just about better interfaces for individual users. It is about generating the kind of grounded, in-the-wild human-machine interaction data that the broader Physical AI ecosystem will need to keep advancing. The robot side and the human side of the loop are not two competing futures. They are two halves of the same one.

That is what Wetour Robotics means when it says: Your body is the interface.

Learn more at wetourrobotics.com.

Over the next few decades, billions of autonomous, AI-powered robots will work alongside people in factories, perform tedious tasks in warehouses, care for the elderly, assist in unsafe disaster areas, deliver packages and food to our doorsteps, and eventually help out in our homes. Some will look like us, and many won’t. What is certain is that regardless of form factor, robots will all rely heavily on AI in order to deliver real-world value.

In 2025, total investments in robotics companies reached a record US $40.7 billion, accounting for 9 percent of all venture funding. The multibillion dollar question therefore is this: What will it take for AI-powered robots to begin to have a serious economic impact? Many of today’s robotics and AI companies are making bold claims, such as that humanoid robots will soon be coming into our homes, but there’s still a big gap between promise and reality.

The promise of robots that live and work alongside us has been the stuff of science fiction for a very long time. And while many programmers have tried to make that promise a reality, the physical world is just too complicated for traditional computer programs to handle the endless complexity it presents. Thanks to AI, robots are no longer being programmed—instead, they learn to operate in the real world. With enough practice, they can learn to perceive and understand the world around them, reason about that world, and use that reason and understanding to perform tasks that are useful, reliable, and safe.

The two of us have worked at the forefront of AI and robotics for the last decade, as a Professor in Robotics at Oregon State University and Co-Founder of Agility Robotics, and as former CEO of the Everyday Robots moonshot at Google X. Our experience deploying AI-powered robots in real-world settings has given us a perspective on where AI can be used to great benefit in complex robotic systems in the near term and where we are still on the frontier of science fiction. We believe AI will enable an inflection point in robotics advances, but that it will be through the well-engineered application of coordinated systems of different AI tools rather than a single ChatGPT-style breakthrough.

As the excitement around AI is matched only by the uncertainty of what will be possible, here are five hard truths that will define AI in robotics.

1. The YouTube-to-Reality Gap Is Real

For years, we have been seeing videos on YouTube with humanoid robots performing amazing moves on everything from a dance floor to an obstacle course. The inside knowledge in robotics is to “never trust a YouTube robot video.” The gap between real robots that can perform real work in unstructured human environments and carefully scripted and edited robot performances remains significant. The latest performance to get a lot of attention was a martial arts show featuring Unitree humanoid robots performing with children at the Chinese 2026 Spring Festival Gala. While impressive, this falls into a long lineage of tightly scripted robotic performances, where everything has been carefully choreographed and planned in advance. The low-level controls, synchronization, and choreography were stunning, yet the Spring Gala robot performance showed a level of autonomy and intelligence much closer to industrial robots building cars in a factory than something that will show up in your living room any time soon.

Seeing these kinds of demos nevertheless raises questions about where robotics really is. If robots can perform kung fu moves and do backflips and dance, why aren’t they also showing up on factory floors yet? And why can’t they do the dishes in my home after dinner? The simple answer is this: Making AI-powered robots capable of performing general tasks in varied human environments is still really hard. While impressive technological feats like those at the Spring Festival may make it look like we could be very close, the use of AI in these demos is only for low-level motor control (to keep the robots from falling over) and therefore is only a small part of the solution for robots to be general purpose in the real, unstructured spaces where we humans live and work.

2. Data Is An Unsolved Challenge

Large Language Models (LLMs) like OpenAI’s ChatGPT and Anthropic’s Claude were initially trained on an internet-scale database of text. The world woke up one day in late 2022 to ChatGPT demonstrating that AI computers could suddenly “speak” to us in prose or verse and about seemingly any topic. LLMs have turned out to generalize well and are now able to take multimodal input (text, images, video) and produce multimodal output. Importantly, the corpus of training data was both enormous and human-generated, which are characteristics that form the gold standard for AI training.

The fastest path to robots as part of everyday life may emerge through a range of robot forms performing increasingly sophisticated applications and employing a range of AI tools.Agility Robotics

Giving AI a body (in the form of a robot), so that it can engage with people in the physical world, continues to be a very difficult and broadly unsolved problem. AI models for general-purpose robotics must simultaneously satisfy multiple, often conflicting, physical, geometric, and temporal limitations while operating in unstructured, dynamic environments. In order to generalize, robot models need to be trained on data gathered in a high-dimensional configuration space, where “dimensions” represent text, lighting conditions, degrees of freedom, joint limits, velocities, force, and safety boundaries, just to mention a few. Importantly, this must be good data—it must contain many examples from what amounts to an infinite number of possible configurations in the physical world.

Since there are very few existing sources of data like this, approaches like teleoperation, video analysis, motion capture of humans, and self-exploration in simulation and in the real world are all seen as important ways to collect data. It’s a herculean task. For example, at Everyday Robots at Google X, we ran 240 million robot instances in our simulator over the course of 2022 to collect training data, mostly to train a trash-sorting model. Similar amounts of data will be needed for every skill to get to a similar level of capability, which is not yet human level.

3. There Will Be No Single Robot AI

We are far away from a moment where a single AI model might allow general-purpose robots to live and work alongside us.

General-purpose robots can have wheels or legs. They can have one, two, three, or more arms. Some have propellers and can fly, while others may be designed to operate under water. Some will drive on busy roads. The physical world is infinitely varied and complex. And then there are all the people and other animals that will be surrounding the robots. How do you train a model to operate a robot safely and reliably in all of these settings? The simple answer is: You don’t. At least not for quite some time.

We believe the winning AI architecture leading to the next big breakthroughs in general-purpose robotics will be “agentic AI” for robots, which are high-level coordinating models that can reason, plan, use tools, and learn from outcomes to execute complex tasks with limited supervision. Agentic, high-level models running on robots will invoke a system of specialized ones for different types of tasks. We will likely soon see multiple robots collaborating and coordinating with each other through their onboard agentic AI models.

AI tools are unlocking new and powerful capabilities in robotics, which in turn will enable new solutions and new markets. It’s encouraging to see these new models being made broadly available, some even as open-source solutions. This availability is akin to what happened with the internet: Real progress occurred when it became ubiquitous. We anticipate an inevitable democratization of complex behaviors in robotics with wide access to these AI tools and technologies.

4. Hardware Is Still Very Hard

Robots are complex systems with many parts that all need to work together with great precision. For a robot to be useful and safe, every part of it must be coordinated, from its perception systems to the computer controlling it, all the way down to its individual actuators.

Actuators—that is, the motors and gears—are a good example of an important part of the robot where what got us here won’t get us there. The actuators used at scale by most industrial robots will not work for robots that will operate in human environments. If these robots accidentally collide with an obstacle, the resulting impacts are harsh, forces are high, and things break. Humans don’t move in this way. We are far more compliant in how we interact with the world, and we’re constantly making contact with our environment and using that contact to help us accomplish things.

Consider the challenge of inserting a key in a lock: Humans typically don’t do this by aligning the key perfectly with the keyhole. Instead, we just feel for the edge of the keyhole and jiggle the key in. Robots need to be able to operate in novel ways to achieve comparable capabilities by using a new class of actuators that are sensitive to force and able to have a compliant interaction with the environment. While these kinds of actuators do exist, they are not yet generally available at scale for robot systems designed to operate around people.

5. Real Value Comes From “Easy” Tasks

There’s a big difference between tasks that look impressive and real-world tasks that provide value. Robotics is a perfect example of Moravec’s paradox, which states that tasks that are hard for humans are easy for computers (like multiplying two big numbers), and tasks easy for humans (like a toddler’s movements) are extremely difficult for computers and robots.

Serving customers is an unforgiving reality check, because customers only care about solving the real problems they have. If we are to deploy AI-based robot solutions, they must outperform the way things are currently done while demonstrating reliable performance metrics and safety. Agility Robotics’ early work to deploy our humanoid robot Digit in customer locations led to the realization that our first obstacle was safety: Robots that balance and manipulate objects in human spaces bring new types of risk to the workplace. In the first humanoid deployments, physical barriers were necessary, and Agility kicked off a multi-year engineering effort to solve the safety challenge, touching nearly every aspect of robot design and relying heavily on new AI-based approaches to human detection and behavior control.

Everyday Robots at Google deployed robots in 2019 that worked autonomously in office buildings doing chores like cleaning cafe tables and sorting trash. We quickly learned how “messy” and difficult the real world is for a robot. This experience informed the architecture and deployment of our AI systems while also gathering real-world data that could be combined with simulation data for training and improving models.

This focus on creating a product to meet specific customer needs and deploying robots in real-world settings is the only way to inform the structure of the AI tools and infrastructure for near-term utility on a path towards long-term broader capability and generality. There will be no “aha” moment, no silver bullet algorithm, and no volume of data sufficient to produce a general-purpose robot without extensive real-world experience.

AI Robots Are Coming, One Step at a Time

Electronic waste is moving up on regulatory agendas in 2026. New European waste-shipment rules, expanded recycling fees on products with non-removable batteries in California, and an e-waste import ban in Malaysia, for example, are all increasing pressure to recover more value before electronics are shredded or exported.

The world is projected to generate 82 million tonnes of e-waste annually by 2030, according to the United Nations’ most recent Global E-Waste Monitor report in 2024. The report estimated that current e-waste management captures less than a third of the recoverable metal value contained in discarded electronics.

For recyclers, much of that lost value is a consequence of what happens before a circuit board ever reaches a smelter or shredder. Boards contain a mixture of components such as memory chips, processors, magnets, and capacitors, as well as valuable raw materials such as copper, aluminum, tantalum, and precious metals. Conventional recycling often mixes everything into bulk streams and destroys components that might otherwise be reused.

Tuurny, a startup based in San Francisco, is developing an automated system to remove and separate reusable chips from circuit boards before the remaining material is shredded. In April, the company announced it had designed a robotic system, called Nantul, to identify and extract RAM integrated circuits, claiming each machine can recover 300 intact RAM ICs per hour.

Sina Ghashghaei, Tuurny’s founder, says the company is preparing its first field deployment with dozens of machines through a six-figure deal with Areera, a television recycler in the United Kingdom, which processes 1,500 tonnes of televisions per month. The deployment is planned for early 2027.

Tuurny’s first target is recovering RAM ICs and other chips used in legacy systems where replacement components can be difficult to source. Ghashghaei says the company is talking with a few legacy chip suppliers and pursuing potential agreements to supply aluminum and copper recovered from circuit boards to smelters and refiners. He declined to identify the companies involved.

Robots for Automated RAM Recovery

Traditional electronics recycling often begins by shredding boards and sorting the mixed output afterward. Tuurny aims to do the opposite: Identify and remove components first, sort them by model or material, then reroute the recovered items to testing labs for potential reuse as new chips or to refiners and smelters for further processing.

Nantul comprises three robotic systems in one. The first is an arm to continuously feed the component-removal robots, paired with two tabletop machines similar to 3D printers or computer numerical control (CNC) machines. A neural network identifies and catalogs components, then searches the internet for manufacturers’ thermal-profile specifications. Nantul uses those specifications to employ a combination of suction, controlled heat, computer vision, and robotic controls to remove chips while minimizing damage. Recovered items are then sorted by model number in material-specific groups.

“We’re creating a new supply chain from old feedstock that didn’t exist before,” Ghashghaei says, adding that manual recovery is expensive and difficult to scale.

Tuurny’s recovery system includes a computer vision system that identifies specific RAM components to assess them for recovery.Tuurny

Minghui Zheng, an associate professor of mechanical engineering at Texas A&M University, in College Station, who studies robotic disassembly and electronics recycling systems, says Tuurny’s approach appears technically feasible, especially when focused on the narrow, valuable target of recovering RAM from more controlled e-waste streams.

“RAM is a good starting point because it has relatively high reuse value and is more standardized than many other electronic parts,” Zheng says. The harder challenge, however, is removing chips “without heat, mechanical, or electrical damage, and making sure it still works reliably afterward.”

Used circuit boards can vary by layout, markings, age, contamination, solder condition, or prior damage. A robot has to identify the correct component, choose a removal strategy, apply heat locally, lift the part cleanly, and preserve enough information about the part for downstream testing and resale.

E-Waste Recycling Strategies

Ghashghaei says Tuurny is building small modular machines using off-the-shelf parts, custom controls, and Nvidia Jetson Nano hardware. The company is trying to keep costs down by reducing hardware complexity to arrive at a price point far below centralized industrial equipment used at large facilities. He says the biggest challenge from an engineering perspective has been developing the autonomous computer vision and robotic control.

Last year, the four-person startup received a NASA-funded grant to support an AI-powered repair assistant for printed circuit boards that used computer vision and a custom large language model (LLM) to guide technicians.

Ghashghaei says Tuurny pivoted from board repair to e-waste processing after concluding that discarded electronics represented a larger market amid growing interest in the U.S. around on-shoring capacity for critical minerals and rare earths. The pivot also positions Tuurny to potentially address supply chain concerns around legacy chips for systems in telecom, aerospace, defense, and other industries where equipment remains in service long after chips leave mainstream production.

In practice, Zheng says the main challenge in making robotic disassembly of electronics commercially viable is ensuring it’s adaptable enough to handle the large variability in e-waste while keeping costs reasonable.

“Every electronic product is different, and used boards may be damaged, dirty, or arranged differently. The robot must be able to find the right parts, remove them carefully, and avoid damaging them in real time, which creates major challenges for robotic perception, decision-making, planning, and manipulation,” Zheng says. “Economically, the recovered parts should be valuable enough to justify the costs of the robot, sensing, testing, maintenance, labor, and scaling up the process.”

For smelters and refiners, the question may be whether Tuurny can supply predictable material streams at commercial volumes. Ghashghaei acknowledged that Tuurny’s scaling efforts could run into its own supply chain constraints in trying to acquire enough components to build more robots.

Zheng called Tuurny’s approach promising but still early. “For now, it is more realistic as a targeted recovery strategy for valuable components like RAM,” Zheng says. “The key question is whether the robotic disassembly technology can work reliably, affordably, and at scale.”

The International Organization for Standardization (ISO) is updating its 12-year-old safety requirements for personal care robots. A lot has happened since the last revision, both on the technology side and with researchers’ understanding of safety for humans collaborating with domestic robots. The proposed ISO update addresses hazard identification, risk assessment, and different use scenarios. It does not, however, set limits, propose testing methods, or have enforcement mechanisms that might address the complexities of human-robot collaboration. And that is a problem, argues technology policy researcher Jae-Seong Lee of the Electronics and Telecommunications Research Institute in Daejeon, South Korea.

Why is the next revision of ISO 13482 a big deal?
Jae-Seong Lee: The standard is moving into final approval at a moment when domestic humanoid robot makers are shifting from lab prototypes to products aimed at real homes, real caregivers, and real families. That matters because the standard does more than specify geometry and impact limits. It helps define what counts as acceptable robot behavior in the messy world of everyday life.

What is the core engineering problem?
Lee: It is not simply whether a robot can avoid collisions or detect people in its path. The harder problem is that human-robot interaction is bidirectional. The robot changes what the human does, and the human changes what the robot perceives and does next. In other words, safety is not a fixed property of the machine alone; it emerges from the relationship.

Isn’t that already covered by current safety standards?
Lee: Only partially. ISO 13482 addresses personal care robots through hazard identification, risk assessment, and intended use scenarios, and related guidance acknowledges noncontact hazards such as unpredictability and incorrect autonomous decisions. But it stops short of binding compliance criteria, test methods, or enforcement mechanisms for the hazards produced by the human-robot relationship.

The technical community understands bidirectional coupling, and the standards framework acknowledges relevant hazards, but no current standard fully converts that knowledge into enforceable rules for domestic autonomy.—Jae-Seong Lee

Why can’t engineers just better define a robot’s operating envelope?
Lee: Because the value proposition of a domestic humanoid depends on operating in uncontrolled environments. A robot that is safe only in standardized rooms, with healthy adults, under well-defined conditions is not really a domestic humanoid at all.

In industrial robotics, designers can usually bound the task, the workspace, and the population. In a home, the robot must adapt to elderly residents, children, visitors, pets, clutter, tight spaces, and fluctuating human behavior. Those aren’t edge cases. Those are the baseline. Tightening the domain to be more like that of factory robots would make the home robots less useful.

The proposal mentions training data. Why does that matter?
Lee: Because the data already reflect the diversity of domestic life. Companies building humanoid training datasets are reportedly sending paying contract workers around the world to record their chores in ordinary settings. That means the robots will be trained on real-world variability, not sanitized demonstrations. The safety problem is therefore in the composition of the entire human-robot system, not in any one component.

What is the standards gap?
Lee: The gap is governance. The technical community understands bidirectional coupling, and the standards framework acknowledges relevant hazards, but no current standard fully converts that knowledge into enforceable rules for domestic autonomy. What is missing is a way to specify safe behavior across the full range of human conditions the robot will actually encounter.

What’s also missing is a decision about who gets to decide whose behavior counts as normal. Whose gait sets the baseline? Whose is an acceptable risk threshold? Whose definition of safe judgment gets written into the requirement language? Those are value judgments, not purely engineering ones. A standards committee cannot avoid choosing a normative reference point; it can only decide whether that choice is explicit and inclusive.

Who could help answer those questions?
Lee: The proposal argues that the people most affected by domestic humanoids are not systematically represented in the working groups shaping the standard. It points especially to older adults, who are often the primary intended users of domestic care robots, yet whose movement patterns and cognitive states are not directly embedded in the standards process.

In other words, this revision acknowledges the hardest problems but pushes unresolved issues into advisory language, nonbinding guidance, or future revision scopes. That can be useful, but it also delays the real question: What counts as safe relational behavior in the home?

What are the stakes?
Lee: The risk is not only injury, though that is the obvious concern. The deeper risk is that safety assumptions get baked into products and standards before the market, regulators, and users have a chance to question them. Once deployment patterns harden, it becomes much harder to revise the baseline.

What should the engineers on the standards bodies do about it?
Lee: The engineers on the standards body should ask not just, “What are the robot’s outputs, and do they stay within safe thresholds?” but “What states does this robot engage with, and does that engagement remain safe across the full range of those states?” That shift sounds subtle, but it changes the design brief. It moves safety from machine-centric measurement toward system-level relational assurance.

Domestic humanoid safety cannot be solved by machine engineering alone. It requires a framework that treats the human not as background noise, but as part of the system, part of the definition of the safety envelope.

For years, the field of robotics has used the terms “dull, dirty, and dangerous” (DDD) to describe the types of tasks or jobs where robots might be useful—by doing work that’s undesirable for people. A classic example of a DDD job is one of “repetitive physical labor on a steaming hot factory floor involving heavy machinery that threatens life and limb.”

But determining which human activities fit into these categories is not as straightforward as it seems. What exactly is a “dull” task, and who makes that assumption? Is “dirty” work just about needing to wash your hands afterwards, or is there also an aspect of social stigma? What data can we rely on to classify jobs as “dangerous?” Our recent work (which was not dull at all) tackles these questions and proposes a framework to help roboticists understand the job context for our technology.

First, we did an empirical analysis of robotics publications between 1980 and 2024 that mention DDD and found that only 2.7 percent define DDD and only 8.7 percent provide examples of tasks or jobs. The definitions vary, and many of the examples aren’t particularly specific (for example, “industrial manufacturing,” “home care”). Next, we reviewed the social science literature in anthropology, economics, political science, psychology, and sociology to develop better definitions for “dull,” “dirty,” and “dangerous” work. Again, while it might seem intuitive which tasks to put into these buckets, it turns out that there are some underlying social, economic, and cultural factors that matter.

Dangerous Work: Occupations or tasks that result in injury or risk of harm

It’s possible to measure the danger of a task or job by using reported information. There are administrative records and surveys that provide numbers on occupational injury rates and hazardous risk factors. While that seems straightforward, it’s important to understand how this data was collected, reported, and verified.

First, occupational injuries tend to be underreported, with some studies estimating up to 70 percent of cases missing in administrative databases. Second, injuries and risk factors are rarely disaggregated by characteristics like gender, migration status, formal/informal employment, and work activities. For example, because most personal protective equipment—such as masks, vests, and gloves—are sized for men, women in dangerous work environments face increased safety risks.

These caveats are an opportunity for robotics to be helpful. If we went out and looked for it, we could probably find some less obviously dangerous work where robotics might be an important intervention, not to mention some groups that are disproportionately affected and would benefit from more workplace safety.

Dirty Work: Occupations or tasks that are physically, socially, or morally tainted

Colloquially, most people might think of dirty work as involving physical dirtiness, such as trash removal, cleaning, or dealing with hazardous substances. But social science literature makes clear that dirty work is also about stigma. Socially tainted jobs are often servile or involve interacting with stigmatized groups (for example, correctional officers), and morally tainted jobs include tasks that people commonly perceive as sinful, deceptive, or otherwise defying norms of civility (like a stripper or a collection agent).

“Dirty work” is a social construct that can vary across time (like tattoo industry stigma in the United States) and culture (such as nursing in the U.S. versus in Bangladesh). One way to measure whether work is “dirty” is by using the closely related concept of occupational prestige, captured through quantitative surveys where people rank jobs. Another way to measure it is through qualitative data, like ethnographies and interviews. Similar to “dangerous,” we see some hidden opportunities for robotics in “dirty” work. But one of our more interesting takeaways from the data is that a lower-ranked job can be something that the workers themselves enjoy or find immense pride and meaning in. If we care about what tasks are truly undesirable, understanding this worker perspective is important.

Dull Work: Occupations or tasks that are repetitive and lacking in autonomy

When it comes to defining dull work, what matters most is workers’ own experiences. Outsiders can make a lot of false assumptions about what tasks have value and meaning. Sometimes things that seem boring or routine create the right conditions for developing skills and competence, such as the concentration needed for woodworking, or for socializing and support, when tasks are done alongside others. Instead of assuming that repetitive work is negative, it’s important to examine qualitative data on how people experience the work and what purpose it serves for them.

DDD: An actionable framework

In our paper, we propose a framework to help the robotics community explore how automation impacts individual jobs. For each term—dull, dirty, and dangerous—the framework gathers key pieces of information to reflect on what physical or social aspects of the task are, in fact, DDD. Worker perspective is an important part of all three considerations. The framework also emphasizes awareness of context—meaning the physical and social environment of an occupation and industry that can influence the DDD nature of a task. Our corresponding worksheet suggests existing data sources to draw on and encourages us to seek out multiple perspectives and consider potential sources of bias in the information.

What makes tasks dull, dirty, or dangerous depends on the perspective of the humans doing those tasks.RAI

Let’s take, for example, the waste and recycling industry. The world generates over 2 billion tonnes of waste annually, and this figure is expected to rise to nearly 4 billion tonnes by 2050. Intuitively, trash collection seems like a job that hits all the Ds. Going through our worksheet, we confirm that globally, workers in this industry face significant health hazards (dangerous), and waste collection is ranked as a low-status job (dirty), although interestingly, many workers take pride in providing this essential service.

The job is also repetitive, but there are aspects that make it not dull. Specifically, workers cite the day-to-day interaction with their coworkers (which includes extensive insider vocabulary, work hacks, and mutual aid groups) and task variety as two of the most enjoyable aspects of the job. Task variety includes inspecting their vehicle and equipment, driving their truck, coordinating with crew members, lifting bins and bags, detecting incorrect sorting of waste, and unloading at the end destination.

This finding matters because some types of robotic solutions will eliminate the parts of the job that workers most appreciate. For instance, the National Institute for Occupational Safety and Health (NIOSH) recommends the adoption of automated side loader trucks and collision avoidance systems. This innovation increases safety, which is great, but it also results in a sole worker operating a joystick in a cab, surrounded by sensor and camera surveillance.

Instead, we should challenge ourselves to think of solutions that make jobs safer without making them terrible in a different way. To do this, we need to understand all aspects of what makes a job dull, dirty, or dangerous (or not). Our framework aims to facilitate this understanding.

Finally, it’s important to note that DDD is only one of many possible approaches to classify what work might be better served by robots. There are lots of ways we could think about which types of tasks or jobs to automate (for example, economic impact or environmental sustainability). Given the popularity of DDD in robotics, we chose this common phrase as a starting point. We would love to see more work in this space, whether it’s data collection on DDD itself or the creation of other frameworks.

At RAI, we believe that the fusion of robotics and social sciences opens a whole new world of information, perspectives, opportunities, and value. It fosters a culture of curiosity and mutual learning, and allows us to create actionable tools for anyone in robotics who cares about societal impact.

This presentation highlights recent efforts at the Johns Hopkins Applied Physics Laboratory to advance agentic AI for collaborative robotic teams. It begins by framing the core challenges of enabling autonomy, coordination, and adaptability across heterogeneous systems, then introduces a scalable architecture designed to support agentic behaviors in multi-robot environments. The talk concludes with key challenges encountered and practical lessons learned from ongoing research and development.

Key learnings

• Provides an introduction to LLM-based AI Agents
• Describes an approach to applying LLM-based AI Agents to robotic teams
• Provides demonstrations of the approach running in hardware with a heterogeneous team of robots
• Presents lessons learned and future work in this area

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.

ICRA 2026: 1–5 June 2026, VIENNA

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

Enjoy today’s videos!

Bulk material handling is a critical, labor-intensive operation across various industries, traditionally performed by human operators using heavy hydraulic manipulators equipped with free-swinging, underactuated grippers. This work presents the first complete autonomous material-handling solution deployed on a real-world 40-ton material handler.

[ ETH Zurich ]

I don’t want to minimize this bedroom tidying by Figure (although I suppose I’m going to), but in the context of doing a task like this in place of a human, it really illustrates what these robots are comfortable with, and what they’re not.

[ Figure ]

Give me this over videos of robots doing backflips any day.

[ Hello Robot ]

Okay, but can it get them out of the can?

[ Generalist ]

The world’s first production-ready manned mecha. It can transform. It’s a civilian vehicle. It weighs ~500 kilograms with you inside.

[ Unitree ]

Curious about what happens when street dance meets embodied AI? From smooth choreography to dynamic flips, NIX is exploring movement, rhythm, and real-world interaction through embodied AI. We’ll make NIX available—FOR FREE!—to selected partners from global universities, robotics labs, and creative technologists.

[ Lumos ]

Thanks, Ni Tao!

We introduce and open-source the Unified Autonomy Stack, a novel solution for resilient autonomy across aerial and ground robot morphologies. The architecture combines multimodal perception, multibehavior planning, and multilayered safe navigation to deliver mission-level autonomy across diverse robot morphologies. It fuses lidar, radar, vision, and inertial sensing to enable robust localization and mapping, vision-language-based scene reasoning, multibehavior planning, and layered safety through map-based avoidance, deep learned policies, and control barrier functions. The system supports Global Navigation Satellite System–denied navigation in perceptually degraded environments, exploration, object discovery, and inspection, and has been validated on multirotor and legged robots in challenging settings, demonstrating resilient performance.

[ NTNU ]

Thanks, Kostas!

Cassie WAS the best robot!

The next video better be a Digit Centaur.

[ Agility ]

Any robot doing anything consistently over a long period of time is impressive. Having said that, you want to be very careful about claiming that any robot operates at “human performance levels,” especially in a somewhat complex manipulation task, because humans are very, very good at stuff like this.

[ Figure ]

Robust.AI cofounder and CTO Rodney Brooks, ranked #44 on the Forbes 250 America’s Greatest Innovators list, sits down for a Q&A ahead of his panel discussion at the Forbes America Innovates event in San Francisco. We asked him two questions: What makes innovation in robotics such a challenge? What does the current surge in AI mean for robotics today?

[ Robust AI ]

This is one of the best robotic research videos I’ve ever seen—and don’t worry, according to the credits it’s not AI. And make sure to watch after the credits!

[ Nature ]

EFGCL is a guided-reinforcement learning method that efficiently enables highly dynamic motions through the use of assistive forces. In this work, we successfully achieved several dynamic motions, including jumping, backflips, and lateral flips.

[ EFGCL ]

Thanks, Keita!

Legged robots: helping farmers one vegetable at a time.

[ University of Southern California ]

Humanoid robots promise general-purpose assistance, yet real-world humanoid loco-manipulation remains challenging because it requires whole-body stability, dexterous hands, and contact-aware perception under frequent contact changes. In this work, we study dexterous, contact-rich humanoid loco-manipulation.

[ Touch Dreaming ]

More than just technology, KATA Friends is a lifelike AI companion designed to see your world, feel your touch, and understand your heart. With expressive movements, evolving emotions, and natural conversations, Noa and Niko both grow alongside you to become a presence uniquely yours. From curious head tilts and playful reactions to ever-changing eye expressions and a soft, innocent voice, every interaction feels warm, personal, and alive.

[ SwitchBot ]

I really hate to say this, but despite how cute it is, Aibo may be showing its age.

[ Aibo ]

One of the biggest challenges in robotics right now isn’t the hardware. It’s data. While many data-collection methods are effective, handheld data collection can create a diverse dataset of environments, conditions, and strategies for completing manipulation tasks. The Koala platform codesigned the handheld grippers and robot grippers around the same linkage mechanism, the same degrees of freedom, and the same force transmission. The human feels through the linkages what the robot will feel through its actuators.

[ Robotics and AI Institute ]

Many roboticists (and at least one robotics journalist) have been seduced by the dream of a robot butler. And the rampant popularity of videos showing humanoid robots doing household tasks in improbably clean kitchens and unrealistically tidy bedrooms suggests that we’re not the only ones interested in a robot that can do our chores. But for all kinds of reasons, legged humanoids are not yet ready for industrial or commercial applications at scale, and home applications (if people even want them), I would argue, are even farther away. Even so, ludicrously well-funded humanoid robotics companies are now ramping production while explicitly promising that their robots will be doing ‘housework.’

So what about that robot butler dream, then? It still exists! All you have to do is forget about legs, arms, hands, faces, and focus on what really matters: mobility and manipulation. This is what Hello Robot’s Stretch robot is unapologetically all about, and the newest version being announced today, Stretch 4, is closer than ever to a robot that could safely do practical work in the home at an accessible cost.

Hello Robot says Stretch 4 is “built for the real world.”Hello Robot

“With Stretch 4, we wanted to make the transition from a research platform to something that is truly deployable,” explains Aaron Edsinger, Hello Robot co-founder and CEO. This version, while ready for research and enterprise customers now, is designed for pilot deployments to help Hello Robot understand how to scale in the home. “This has been our most difficult design process,” adds co-founder and CTO Charlie Kemp. “We had a lot of fear of ‘second-system syndrome,’ where you add all the features you didn’t get to initially and end up with a monstrosity. But since we founded the company on making simple, minimalist robots, every time we added complexity it was an emotional challenge. Navigating that fear resulted in a nice compromise that sits in a great spot, rather than being a maximalist humanoid.”

Stretch 4 Upgrades

The biggest change from the previous version of Stretch is the addition of an omnidirectional base, meaning that the robot can translate in any direction without having to turn first. This makes it much easier to control (especially for novice users), but omnidirectional bases are significantly more complicated to design and build. What ultimately made it possible for Stretch were new types of omnidirectional wheels developed for powered wheelchairs, along with a solid six months of focused development by Hello Robot.

A redesigned sensorized head gives Stretch more options for teleoperation and autonomy.Hello Robot

Stretch 4 also ditches the cute little pan-tilt head for a more complex sensor suite with a much wider field of view. “We started out wanting to use lots of cheap cameras to keep costs low, like Tesla does,” Edsinger tells us. “But we ended up with an approach closer to Waymo’s: the richer and more reliable your data, the safer and more intelligent the robot can be.” There are a pair of hemispherical lidars, Luxonis cameras for vision and navigation, and a wrist-mounted depth camera for manipulation. The robot’s primary system runs on an Intel NUC 15, plus an Nvidia Jetson Orin NX for researchers to play with for visual processing or AI.

Philosophy on Autonomy

Hello Robot’s general philosophy on autonomy is to have a human in the loop, but that can take many different forms ranging from direct control to purely supervisory control. The robot will ship with a baseline of autonomous capabilities that include mapping, navigation, and self-charging, along with demo-ready features like autonomous grasping. But unlike most other robotics companies, Hello Robot isn’t looking to use their hardware to collect a stupendous amount of data in the concerningly vague hope that commercially viable autonomy will follow.

“Stretch has huge advantages in safety, cost, and capability,” Kemp says. “I’d much rather be the platform that foundation model developers target.” Edsinger agrees: “We do want to partner with foundation model companies to explore things like dexterous in-home manipulation, but we aren’t the ones to build those foundation models.”

In-Home Pilots

While earlier versions of Stretch were primarily for research, Kemp tells us that Stretch 4 has been explicitly designed to be piloted in the homes of people with severe mobility impairments. Hello Robot will be happy to sell you one (or lots, I’m guessing) for commercial or industrial applications, but the broader goal with Stretch 4 is to use remote testing and in-home evaluations to work towards a robot that’s useful and reliable enough that it can provide consistent daily value for disabled users.

A holonomic base and an extendable arm make for a capable robot without the complexity.Hello Robot

Part of why I’m optimistic about Stretch finding near-term success in this role is precisely because it’s not a humanoid. One of the primary arguments for humanoids is that they’re worth pursuing because they can better operate in environments designed for humans, where legs and five-fingered hands are tangible advantages. But those very same environments often exclude an entire subset of humanity—a subset of humanity that we will all likely join at some point, because the best that any of us can ever say is that we are not disabled yet.

Why Not Humanoids?

A key partner for Hello Robot throughout the Stretch development process has been Henry Evans. Evans is paralyzed and cannot speak, although he can use a computer (for controlling robots, among other things) and type at about 15 words per minute. I spoke with Evans about his thoughts on the idea of a humanoid assistive robot, compared to a robot like Stretch. “The question is: What benefit does a bipedal robot offer to a person who can’t walk?” Evans asks. “Their entire environment has been modified to accommodate wheeled conveyances. Automobiles don’t have legs, and neither should home robots. Wheels are cheap, stable, precise, require very few controls, and don’t have to be invented.”

Henry Evans has been testing a Stretch 4 as a home assistive robot.Hello Robot

Evans also points out that humanoids can require the simultaneous control of dozens of degrees of freedom. “A paralyzed person who can’t talk (like yours truly) can control maybe one or two joints at a time with today’s control mechanisms, if they are lucky.” Evans believes that AI, along with Brain Computer Interfaces (BCIs), show promise for dramatically increasing what he can do when it comes to motion. “Remember, though, a paralyzed person has no movements to mimic, so until a perfectly tuned BCI gets here and facilitates a true humanoid body surrogate, I don’t think it will work. And even then, I don’t see the advantage of legs for assistive care robots. I am willing to be proven wrong, though, and will test-drive almost anything once, so bring it on!”

Kemp and Edsinger, who have many decades of humanoid experience between them, feel similarly. “There are applications where the human form is fundamental,” Kemp says. “But for many applications, the value of the human form is unclear or even problematic. Jumping to the conclusion that robots must be humanoid means missing opportunities to take advantage of the structured indoor environments that we’ve already created.”

Georgena Moran and her sisters tested Stretch 4 at the California Academy of Sciences Museum, allowing her to interact with the exhibits from home.Hello Robot

And of course there’s the question of safety, which Evans brings up. “My caregivers and I have been testing robots in my home to assist us for about 15 years, and the very first concerns are: Where is the emergency stop, and how do you activate it? It gets used surprisingly often. The thing is, when a wheeled robot gets emergency stopped, it freezes in place. When a bipedal robot gets run-stopped, it collapses on anything under it, including the patient.” Kemp agrees. “The safety aspect of humanoids in a home freaks me out. I don’t know how someone can confidently think about safety with a humanoid in a home.”

Robots for Sale

However you feel about humanoids, here’s one more reason why Stretch feels like a much more realistic solution for in-home assistive robots right now: You can actually buy one, and at US $29,950, it’s very affordable, as mobile manipulators go. Edsinger and Kemp are planning to leverage in-home Stretch 4 pilot deployments to make the next version of Stretch the one that can be commercially sold for home assistance. At the rate that Hello Robot has been releasing new hardware, that could easily be within the next year or so—and my guess is that Stretch 5 is very likely to be the first practical, affordable assistive robot for home use. It may not look like Rosie, but it promises to be safe, and it works.

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.

ICRA 2026: 1–5 June 2026, VIENNA

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

Enjoy today’s videos!

Introducing GENE-26.5—the first AI brain to give robots human-level physical manipulation capabilities. Cooking a full meal. Cracking an egg one-handed. Conducting lab experiments. Wire harnessing. Even playing the piano. Tasks that were impossible for robots. Until now.

[ Genesis AI ] via [ TechCrunch ]

This is Labububot—one of the rarest monsters on Earth. Twelve Labubu heads are reconstituted into a single spherical form: a Frankenstein’s Monster of pop culture iconography. Labububot is a playful critique of social robots, and a question made physical—what do the monsters we make reveal about the monsters we are?

[ MIT Media Lab ]

Watch Spot crouch, jump, climb boxes, and leap across gaps, controlled by a neural network trained with reinforcement learning (RL) and multi-expert distillation.

[ Robotics and AI Institute ]

Good, now there is a robot that can take over exercise for me.

[ Kepler ]

Additive manufacturing has become an enabling technology, but existing techniques are not capable of directly 3D printing high-current electromagnetic actuators due to material and design limitations. In this work, a novel 3D-printable, multilayer, wave-winding topology is created for high-efficiency electric motors.

[ Sensing Technologies Laboratory ]

NASA is pushing the limits of flight on Mars—by spinning helicopter rotor blades so fast, they’re breaking the sound barrier. During recent tests at NASA’s Jet Propulsion Laboratory, engineers accelerated the tips of next-generation rotor blades beyond Mach 1 inside a special chamber that simulates the atmospheric conditions of the Red Planet.

[ NASA Jet Propulsion Laboratory ]

Balancing commercial goals and robotics research can be tricky, but with Atlas, we’re making it work.

[ Boston Dynamics ]

Open Duck Mini is an open-source version of Disney’s BDX droids, and you can play with it in your browser.

[ Open Duck Mini Viewer ]

Thanks, Masato!

Automated inspection of steel structures using magnetic climbing robots can reduce costs and improve safety, but many such structures feature interior corners that are challenging for wheeled or tracked robots to traverse. We present the first magnetic-wheeled robot to use X-ray fluorescence for steel structure inspection, Sally, capable of overcoming all interior corner transition types, traversing small obstacles, and maneuvering in tight spaces.

[ Robomechanics Lab ]

I don’t know what this is, but it’s coming soon from SwitchBot.

[ SwitchBot ]

You probably know the answers to these questions already, but this ELI5 from Aaron Ames is still fun.

[ Wired ]

Jim Fan, who leads the embodied autonomous research group at Nvidia, returns to AI Ascent to argue that robotics is entering its endgame—and that the playbook is already written.

[ Sequoia ]

Two years ago, Colin Angle stepped down as CEO of iRobot, the company that he cofounded and the most successful home robot company the world has ever seen. Angle almost immediately founded a stealthy new “physical AI” company called Familiar Machines & Magic (FM&M), which in short order managed to attract a combination of exceptionally talented robotics folks, including Morgan Pope from Disney Research, which got us very curious.

Today, Familiar Machines & Magic is announcing its first robot, a “physically embodied AI system designed to perceive, adapt, and interact with people in ways that feel natural and consistent,” the press release says. This robot is not a toy, and it’s not specifically for kids. Rather, it’s for adults to purchase for themselves and their families. It will get to know you, seek you out for attention, and actively help you positively pursue an idealized routine in your life.

Intended for adults, Familiar is pet-like in that it will seek you out for attention.Familiar Machines & Magic

Here are the (limited) technical details from the press release:

The first Familiar is a quadruped, specifically designed for human-robot interaction, with 23 degrees of freedom enabling both lifelike movement and expressive behaviors. The Familiar is covered with a custom touch-sensitive coat, a vision system, and a microphone array and audio system, to support rich interactions. Its onboard edge AI stack is powered by a custom small multimodal model optimized for social reasoning, combining vision, audio, language, and memory to create socially responsive behaviors in real time.

FM&M CEO and cofounder Colin Angle tells us that this first prototype Familiar is designed to look like a sort of highly abstracted bear. It’s very deliberately nothing like a dog or a cat, following the successful strategy of other social robots like Paro and Pleo—if you can’t connect the form factor to an animal that you have direct experience with, you won’t bring expectations to your interactions with the robot.

What Does it Do?

“Our goal is to position this as a robot familiar that lives with you and helps reinforce healthy routines,” Angle says. He explains that thinking of a Familiar like a pet is a strong analogy, but pet-like also undersells what the robot can do. The Familiar behaves a little more like a service animal, in the narrow sense of being able to recognize activities and intervene to motivate you to do more or less of them, as the case may be. One easy example is screen time—the Familiar can note how much time you spend on your phone, and if it’s too much, it can actively try to engage you in other activities, including taking it for a walk outside. “The idea,” says Angle, “is that you can have a bit of technology in your home which is hyperloyal to you, gets to know you, helps you figure out an idealized routine, and then plays a positive role.”

Spending too much time on your phone? Familiar can help with that.Familiar Machines & Magic

Cramming this amount of intelligence into a robot that you can take for a walk outside (at regular human walking pace) is extremely ambitious. I asked FM&M’s creative director Morgan Pope what made him feel that a robot like a Familiar was possible, with enough confidence that he was willing to leave Disney Research to join the startup. “Two recent advancements made it feel tractable,” Pope says. “First, seeing Disney’s bipedal robots walk flexibly over various terrain using reinforcement learning proved you can execute dynamic motion without needing perfect, zero-backlash actuators or crazy expensive hardware. And second, while I am often skeptical of generative AI hype, it is a perfect fit here because it excels at creating the plausible assumption of intelligence, which helps the character feel coherent and lifelike.”

The Challenge of Social Home Robots

As a social home robot, the Familiar will have quite a lot of work to do to single-pawedly reestablish a category that burned itself out between 2012 and 2019. A series of high-profile and very-well-funded startups including Anki, Mayfield, and Jibo were not able to sustain social home robots as a business, primarily because of a struggle with longer-term engagement. It’s not enough for a robot to be cute and charming in the short term; it has to continue enthralling its users or at least providing value after the initial novelty has worn off. In other words, a flashy demo is arguably counterproductive, which is a real problem, since robots excel at flashy demos.

Part of the value of Familiar is that it will help you establish healthy routines.Familiar Machines & Magic

“It’s about creating the right expectation and delivering on that expectation,” says Angle. “Familiars live in your world and play by your rules, and if you don’t find yourself hanging out with it, petting it, and engaging with it, then we haven’t succeeded.”

In what is very much not a coincidence, the term familiar really is the best way of thinking about this robot—a sort of vaguely magical nonhuman entity that has some amount of independence but whose existence and motivation are fundamentally tied to its human. “This isn’t trying to be a replacement for a real friend,” Angle explains. “It’s artificial life that lives in your world, has its own personality and goals, and has a special link to its guardian where it wants attention and wants its guardian to be active.”

Creating Long-Term Value

This philosophy is a key differentiator for FM&M. A Familiar is more than a companion; it has long-term objectives that it’s trying to fulfill to improve your life in a targeted way, says Angle. It’ll attempt to connect with you socially to encourage you to spend time with it in service of those goals, but the goals are the end, er, goals, rather than just the social connection itself, which was the primary draw of the previous generation of social robots. “Within a few days of bringing your Familiar home,” Angle tells us, “it’s figured out what its role in your life is. It’s trying to reinforce a healthy routine, whether that be summoning people to dinner or cuddling up while you watch TV, or greeting you when you get home. And then the way you sustain that relationship is by having it evolve, with both characters playing an active role—you’re also helping it with the things required to keep a robot operating.”

Human-Familiar Interaction

The temptation to leverage recent advances in AI to make a robot like a Familiar talk, especially in the context of regularly interacting with humans in pursuit of specific goals, must have been overwhelming. But to its credit, FM&M managed to resist. “I don’t believe that the technology exists today for AI to talk to humans in a safe, responsible fashion,” Angle explains. Consequently, a Familiar does not currently speak, although it does make sounds, and has plenty of other ways of communicating. “Through careful design, you’d be amazed what you can powerfully convey using a tail, wiggly ears, blinking eyes, and a brow that can be happy, sad, angry, or annoyed,” Angle says. This will likely resonate strongly with dog owners, somewhat less strongly with cat owners, and only very slightly with reptile owners like me.

Familiar is capable enough to keep up with you on walks outdoors.Familiar Machines & Magic

Going the other direction is more complicated. Those same recent advances in AI mean that a Familiar can very likely understand everything you say and obey you perfectly, if it chose to. But doing so would break the illusion that the robot has its own desires and goals and personality, so FM&M had to be careful. “The way we’ve trained it from an AI perspective is really cool,” Angle explains. “We’re using a tableau of speech and vision inputs presented to a small multimodal model trained on stories, and for a given tableau of inputs, it goes through a generative process to decide at a high level what it is going to do. That decision is handed to a behavior engine which builds out those behavior trees into goals and drives a reinforcement learning unified motion model. There is nothing fully deterministic about your Familiar’s behavior; it truly tries to live its life with a variety of personality-driven emotions.”

Safety at Home

A Familiar is not big, as robots go, but it’s not exactly small, either. And as something with legs, there’s always a concern about what happens if it falls over. “Its low center of gravity helps immensely,” says Pope. “If we pull power, it collapses downward safely rather than tipping over. Furthermore, it is wrapped in soft rubber, fur, and padding, so even if a leg impacts you, it won’t have a lot of force behind it.” Interestingly, FM&M is also leveraging the “character experience” to mitigate risks to both robot and user. “We can use emotions to communicate hazards effectively,” explains Pope. “For example, if someone carries it somewhere high or puts it near an open flame, the Familiar can act visibly scared to directly communicate that it doesn’t like the situation.”

While not a toy or specifically intended for children, Familiar can provide gentle, warm attention to your family.Familiar Machines & Magic

Besides physical safety, social robots must also consider emotional safety. The better job you do emotionally connecting with people, the more responsibility you have to make sure that those connections are positive. “We take this very seriously,” Pope tells us. “We must follow a ‘do no harm’ philosophy, ensuring we don’t trigger unhealthy dependency or monopolize people’s attention the way a phone does. We are designing carefully to ensure the overall impact remains positive and never crosses the line into harm.” Additionally, the Familiar’s AI runs onboard the robot, and the robot does not stream private data to the cloud. It will, in fact, run just fine if you disconnect it from the internet entirely, although you’ll lose access to any new features that come out.

Managing Expectations

Alongside the many engineering and human-robot interaction (HRI) challenges that FM&M is having to manage is one other challenge that, in the near term, sounds rather dull but may be the most challenging: marketing. The company obviously has to promote this robot, but there’s a real danger (which has had dire consequences for many robotics companies in the past) of selling an idea of what the robot could be rather than the reality of what the robot actually is.

From my conversations with Pope, FM&M seems to understand that robots have always been the most successful when the experience or task is incidental to the robot itself—in other words, what’s most compelling is what the robot will do, rather than the fact that it’s a robot. “The best way to understand a Familiar is that we are not building a robot; we are building a relationship,” Pope explains.

Whether in the context of locomotion or relationships, we can be absolutely certain that a robot of this level of sophistication is not going to do what it’s supposed to every single time. Fortunately, the folks at FM&M have been building robots for long enough that they’re prepared for this. “We’ve explicitly tried to design it to motivate forgiveness,” Angle tells us. “This is not a precise robotic entity in its motion or dexterity. It’s supposed to be imperfect, but it’s going to get some of it right. By actively working to manage expectations to a place we can achieve, we want consumers to appreciate what it can do.”

What customers expect, what they appreciate, and how much forgiveness they’re willing to bestow is for better or worse highly dependent on how much a Familiar will cost. “For the cost of ownership of something like a pet, you’re getting something that can help you live a healthier life, feel attended to, and provide social benefit,” Angle says. This could mean many things, depending on the pet, but one source puts the low end of the monthly cost for a cat at around $65 per month, with a dog somewhat more expensive at closer to $100 per month. FM&M’s press release stresses that today’s announcement ‘is not a commercial product launch,’ and specific pricing and a timeline will come later.

A Future Platform

While it’s much too early for us to be speculating about what the future might hold for FM&M’s robots, Angle is of course already thinking about other places where Familiars might be at home. “This first robot is meant to be a platform with general appeal and an opportunity to specialize into things like elder care and parental support,” Angle says. “From the ground up we are designing machines focused on human connection, and the underlying technology can further generalize into other form factors.”

This will require the Familiar to find success, and it’s important to reiterate how much of a challenge this will be. A legged robot, designed for human interaction, in the home—everything about what FM&M is doing is hard. Because of his experience launching and leading iRobot, Angle is one of the very few people with the experience to really understand this, but his excitement and optimism about the Familiar is undiminished. “Do we know exactly how it’s going to land? I don’t,” says Angle. “But do I think it’s going to work? Absolutely. We’re going to find out, with a mission and goals that are noble at heart.”

This article is brought to you by DAIMON Robotics.

This April, Hong Kong-based DAIMON Robotics has released Daimon-Infinity, which it describes as the largest omni-modal robotic dataset for physical AI, featuring high resolution tactile sensing and spanning a wide range of tasks from folding laundry at home to manufacturing on factory assembly lines. The project is supported by collaborative efforts of partners across China and the globe, including Google DeepMind, Northwestern University, and the National University of Singapore.

The move signals a key strategic initiative for DAIMON, a two-and-a-half-year-old company known for its advanced tactile sensor hardware, most notably a monochromatic, vision-based tactile sensor that packs over 110,000 effective sensing units into a fingertip-sized module. Drawing on its high-resolution tactile sensing technology and a distributed out-of-lab collection network capable of generating millions of hours of data annually, DAIMON is building large-scale robot manipulation datasets that include vast amounts of tactile sensing data. To accelerate the real-world deployment of embodied AI, the company has also open-sourced 10,000 hours of its data.

Prof. Michael Yu Wang, co-founder and chief scientist at DAIMON Robotics, has pioneered Vision-Tactile-Language-Action (VTLA) architecture, elevating the tactile to a modality on par with vision.DAIMON Robotics

Behind the strategy is Prof. Michael Yu Wang, DAIMON’s co-founder and chief scientist. Prof. Wang earned his PhD at Carnegie Mellon — studying manipulation under Matt Mason — and went on to found the Robotics Institute at the Hong Kong University of Science and Technology. An IEEE Fellow and former Editor-in-Chief of IEEE Transactions on Automation Science and Engineering, he has spent roughly four decades in the field. His objective is to address the missing “insensitivity” of robot manipulation, which practically relies on the dominant Vision-Language-Action (VLA) model. He and his team have pioneered Vision-Tactile-Language-Action (VTLA) architecture, elevating the tactile to a modality on par with vision.

We spoke with Prof. Wang about how tactile feedback aims to change dexterous manipulation, how the dataset initiative is foreseen to improve our understanding of robotic hands in natural environments, and where — from hotels to convenience stores in China — he sees touch-enabled robots making their first real-world inroads.

Daimon-Infinity is the world’s largest omni-modal dataset for Physical AI, featuring million-hour scale multimodal data, ultra-high-res tactile feedback, data from 80+ real scenarios and 2,000+ human skills, and more.DAIMON Robotics

The Dataset Initiative

This month, DAIMON Robotics released the largest and most comprehensive robotic manipulation dataset with multiple leading academic institutions and enterprises. Why releasing the dataset now, rather than continuing to focus on product development? What impact will this have on the embodied intelligence industry?

DAIMON Robotics has been around for almost two and a half years. We have been committed to developing high-resolution, multimodal tactile sensing devices to perceive the interaction between a robot’s hand (particularly its fingertips) and objects. Our devices have become quite robust. They are now accepted and used by a large segment of users, including academic and research institutes as well as leading humanoid robotics companies.

As embodied AI continues to advance, the critical role of data has been clearer. Data scarcity remains a primary bottleneck in robot learning, particularly the lack of physical interaction data, which is essential for robots to operate effectively in the real world. Consequently, data quality, reliability, and cost have become major concerns in both research and commercial development.

This is exactly where DAIMON excels. Our vision-based tactile technology captures high-quality, multimodal tactile data. Beyond basic contact forces, it records deformation, slip and friction, material properties and surface textures — enabling a comprehensive reconstruction of physical interactions. Building on our expertise in multimodal fusion, we have developed a robust data processing pipeline that seamlessly integrates tactile feedback with vision, motion trajectories, and natural language, transforming raw inputs into training-ready dataset for machine learning models.

Recognizing the industry-wide data gap, we view large-scale data collection not only as our unique competitive advantage, but as a responsibility to the broader community.

By building and open-sourcing the dataset, we aim to provide the high-quality “fuel” needed to power embodied AI, ultimately accelerating the real-world deployment of general-purpose robotic foundation models.

The robotics industry is highly competitive, and many teams have chosen to focus on data. DAIMON is releasing a large and highly comprehensive cross-embodiment, vision-based tactile multimodal robotic manipulation dataset. How were you able to achieve this?

We have a dedicated in-house team focused on expanding our capabilities, including building hardware devices and developing our own large-scale model. Although we are a relatively small company, our core tactile sensing technology and innovative data collection paradigm enable us to build large-scale dataset.

Our approach is to broaden our offering. We have built the world’s largest distributed out-of-lab data collection network. Rather than relying on centralized data factories, this lightweight and scalable system allows data to be gathered across diverse real-world environments, enabling us to generate millions of hours of data per year.

“To drive the advancement of the entire embodied AI field, we have open-sourced 10,000 hours of the dataset for the broader community.” —Prof. Michael Yu Wang, DAIMON Robotics

This dataset is being jointly developed with several institutions worldwide. What roles did they play in its development, and how will the dataset benefit their research and products?

Besides China based teams, our partners include leading research groups from universities, such as Northwestern University and the National University of Singapore, as well as top global enterprises like Google DeepMind and China Mobile. Their decision to partner with DAIMON is a strong testament to the value of our tactile-rich dataset.

Among the companies involved there are some that have already built their own models but are now incorporating tactile information. By deploying our data collection devices across research, manufacturing and other real-world scenarios, they help us to gather highly practical, application-driven data. In turn, our partners leverage the data to train models tailored to their specific use cases. Furthermore, to drive the advancement of the entire embodied AI field, we have open-sourced 10,000 hours of the dataset for the broader community.

Equipped with Daimon’s visuotactile sensor, the gripper delicately senses contact and precisely controls force to pick up a fragile eggshell.Daimon Robotics

From VLA to VTLA: Why Tactile Sensing Changes the Equation

The mainstream paradigm in robotics is currently the Vision-Language-Action (VLA) model, but your team has proposed a Vision-Tactile-Language-Action (VTLA) model. Why is it necessary to incorporate tactile sensing? What does it enable robots to achieve, and which tasks are likely to fail without tactile feedback?

Over these years of working to make generalist robots capable of performing manipulation tasks, especially dexterous manipulation — not just power grasping or holding an object, but manipulating objects and using tools to impart forces and motion onto parts — we see these robots being used in household as well as industrial assembly settings.

It is well established that tactile information is essential for providing feedback about contact states so that robots can guide their hands and fingers to perform reliable manipulation. Without tactile sensing, robots are severely limited. They struggle to locate objects in dark environments, and without slip detection, they can easily drop fragile items like glass. Furthermore, the inability to precisely control force often leads to failed manipulation tasks or, in severe cases, physical damage. Naturally, the VLA approach needs to be enhanced to incorporate tactile information. We expanded the VLA framework to incorporate tactile data, creating the VTLA model.

An additional benefit of our tactile sensor is that it is vision-based: We capture visual images of the deformation on the fingertip surface. We capture multiple images in a time sequence that encodes contact information, from which we can infer forces and other contact states. This aligns well with the visual framework that VLA is based upon. Having tactile information in a visual image format makes it naturally suitable for integration into the VLA framework, transforming it into a VTLA system. That is the key advantage: Vision-based tactile sensors provide very high resolution at the pixel level, and this data can be incorporated into the framework, whether it is an end-to-end model or another type of architecture.

DAIMON has been known for its vision-based tactile sensors that can pack over 110,000 effective sensing units.DAIMON Robotics

The Technology: Monochromatic Vision-based Tactile Sensing

You and your team have spent many years deeply engaged in vision-based tactile sensing and have developed the world’s first monochromatic vision-based tactile sensing technology. Why did you choose this technical path?

Once we started investigating tactile sensors, we understood our needs. We wanted sensors that closely mimic what we have under our fingertip skin. Physiological studies have well documented the capabilities humans have at their fingertips — knowing what we touch, what kind of material it is, how forces are distributed, and whether it is moving into the right position as our brain controls our hands. We knew that replicating these capabilities on a robot hand’s fingertips would help considerably.

When we surveyed existing technologies, we found many types, including vision-based tactile sensors with tri-color optics and other simpler designs. We decided to integrate the best of these into an engineering-robust solution that works well without being overly complicated, keeping cost, reliability, and sensitivity within a satisfactory range, thus ultimately developing a monochromatic vision-based tactile sensing technique. This is fundamentally an engineering approach rather than a purely scientific one, since a great deal of foundational research already existed. With the growing realization of the necessity of tactile data, all of this will advance hand in hand.

DAIMON vision-based tactile sensor captures high-quality, multimodal tactile data.DAIMON Robotics

Last year, DAIMON launched a multi-dimensional, high-resolution, high-frequency vision-based tactile sensor. Compared with traditional tactile sensors, where does its core advantage lie? Which industries could it potentially transform?

The key features of our sensors are the density of distributed force measurement and the deformation we can capture over the area of a fingertip. I believe we have the highest density in terms of sensing units. That is one very important metric. The other is dynamics: the frequency and bandwidth — how quickly we can detect force changes, transmit signals, and process them in real time. Other important aspects are largely engineering-related, such as reliability, drift, durability of the soft surface, and resistance to interference from magnetic, optical, or environmental factors.

A growing number of researchers and companies are recognizing the importance of tactile sensing and adopting our technology. I believe the advances in tactile sensing will elevate the entire community and industry to a higher level. One of our potential customers is deploying humanoid robots in a small convenience store, with densely packed shelves where shelf space is at a premium. The robot needs to reach into very tight spaces — tighter than books on a shelf — to pick out an object. Current two-jaw parallel grippers cannot fit into most of these spaces. Observing how humans pick up objects, you clearly need at least three slim fingers to touch and roll the object toward you and secure it. Thus, we are starting to see very specific needs where tactile sensing capabilities are essential.

From Academia to Startup

After 40 years in academia — founding the HKUST Robotics Institute, earning prestigious honors including IEEE Fellow, and serving as Editor-in-Chief of IEEE TASE — what motivated you to found DAIMON Robotics?

I have come a long way. I started learning robotics during my PhD at Carnegie Mellon, where there were truly remarkable groups working on locomotion under Marc Raibert, who founded Boston Dynamics, and on manipulation under my advisor, Matt Mason, a leader in the field. We have been working on dexterous manipulation, not only at Carnegie Mellon, but globally for many years.

However, progress has been limited for a long time, especially in building dexterous hands and making them work. Only recently have locomotion robots truly taken off, and only in the last few years have we begun to see major advancements in robot hands. There is clearly room for advancing manipulation capabilities, which would enable robots to do work like humans. While at Hong Kong University of Science and Technology, I saw increasingly greater people entering this area in the form of students and postdoctoral researchers. We wanted to jumpstart our effort by leveraging the available capital and talent resources.

Fortunately, one of my postdocs, Dr. Duan Jianghua, has a strong sense for commercial opportunities. Recognizing the rapid growth of robotics market and the unique value that our vision-based tactile sensing technology could bring, together we started DAIMON Robotics, and it has progressed well. The community has grown tremendously in China, Japan, Korea, the U.S., and Europe.

Robots equipped with DAIMON technology have been deployed in factory settings. The company aims to enable robots to achieve “embodied intelligence” and close the gap between what they can see and what they can feel.DAIMON Robotics

Business Model and Commercial Strategy

What is DAIMON’s current business model and strategic focus? What role does the dataset release play in your commercial strategy?

We started as a device company focused on making highly capable tactile sensors, especially for robot hands. But as technology and business developed, everyone realized it is not just about one component, rather the entire technology chain: devices, data of adequate quality and quantity, and finally the right framework to build, train, and deploy models on robots in real application environments.

Our business strategy is best described as “3D”: Devices, Data, and Deployment. We build devices for data collection, our own ecosystem, and for deploying them in our partners’ potential application domains. This enables the collection of real-world tactile-rich data and complete closed-loop validation. This will become an integral part of the 3D business model. Most startups in this space are following a similar path until eventually some may become more specialized or more tightly integrated with other companies. For now, it is mostly vertical integration.

Embodied Skills and the Convergence Moment

You’ve introduced the concept of “embodied skills” as essential for humanoid robots to move beyond having just an advanced AI “brain.” What prompted this insight? What new capabilities could embodied skills enable? After the rapid evolution of models and hardware over the past two years, has your definition or roadmap for embodied skills evolved?

We have come a long way now see a convergence point where electrical, electronic, and mechatronic hardware technologies have advanced tremendously in last two decades. Robots are now fully electric, do not require hydraulics, because hardware has evolved rapidly. Modern electronics provide tremendous bandwidth with high torques. If we can build intelligence into these systems, we can create truly humanoid robots with the ability to operate in unstructured environments, make decisions, and take actions autonomously.

“Our vision is for robots to achieve robust manipulation capabilities and evolve into reliable partners for humans.” —Prof. Michael Yu Wang, DAIMON Robotics

AI has arrived at exactly the right time. Enormous resources have been invested in AI development, especially large language models, which are now being generalized into world models that enable physical AI capabilities. We would like to see these manifested in real-world systems.

While both AI and core hardware technologies continue to evolve, the focus is much clearer now. For example, human-sized robots are preferred in a home environment. This is an exciting domain with a promise of great societal benefit if we can eventually achieve safe, reliable, and cost-effective robots.

The Road to Real-World Deployment

Today, many robots can deliver impressive demos, yet there remains a gap before they truly enter real-world applications. What could be a potential trigger for real-world deployment? Which scenarios are most likely to achieve large-scale deployment first?

I think the road toward large-scale deployment of generalist robots is still long, but we are starting to see signs of feasibility within specific domains. It is very similar to autonomous vehicles, where we are yet to see full deployment of robo-taxis, while we have already started to find mobile robots and smaller vehicles widely deployed in the hospitality industry. Virtually every major hotel in China now has a delivery robot — no arms, just a vehicle that picks up items from the hotel lobby (e.g., food deliveries). The delivery person just loads the food and selects the room number. It is up to the robot thereafter to navigate and reach the guest’s room, which includes using the elevator, to deliver the food. This is already nearly 100 percent deployed in major Chinese hotels.

Hotel and restaurant robots are viewed as a model for deploying humanoid robots in specific domains like overnight drugstores and convenience stores. I expect complete deployment in such settings within a short timeframe, followed by other applications. Overall, we can expect autonomous robots, including humanoids, to progressively penetrate specific sectors, delivering value in each and expanding into others.

Ultimately, our vision is for robots to achieve robust manipulation capabilities and evolve into reliable partners for humans. By seamlessly integrating into our homes and daily lives, they will genuinely benefit and serve humanity.

This interview has been edited for length and clarity.

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.

ICRA 2026: 1–5 June 2026, VIENNA

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

Enjoy today’s videos!

Figure is now able to produce 55 robots per week, which will be “allocated to internal research and development groups, data collection, efforts for robots to perform end-to-end housework, and commercial use-case development.” Er, that seems like a lot of robots to be making when commercial use cases are still “in development,” doesn’t it?

[ Figure ]

The opening of the NEO Factory in Hayward, Calif., marks a fundamental shift in humanoid robotics: The United States’ most vertically integrated robot factory has now begun full-scale production, bringing end-to-end manufacturing of NEO under one roof. Spanning 58,000 square feet and employing over 200 team members, 1X designs and builds every critical component in-house—motors, batteries, transmissions, sensors, structures, and final assembly—enabling faster iteration, superior safety, and true American scale. With the first robots already coming off the line and consumer shipments planned for 2026, this is the critical milestone that turns the vision of abundant, general-purpose home robots into reality.

Scale will fix everything...?

[ 1X ]

Unlike statically stable robots, a dynamically balanced robot can shift its center of mass to accommodate loads without tipping over, so we like to see just how far we can push our software. Getting Digit to stand on one leg pushes the limits of our sim-to-real pipeline training methodologies—even the slightest model mismatches can lead to instability.

[ Agility ]

In this work, we develop a tactile-enabled whole-body humanoid manipulation system for stable, dexterous, contact-rich real-world manipulation. Our system combines VR-based whole-body teleoperation, a lower-body controller based on reinforced learning, dexterous hand retargeting, distributed tactile sensing, and a multimodal policy called Humanoid Transformer with Touch Dreaming (HTD).

[ Humanoid Touch Dream ]

Thanks, Yaru!

Originally posted two years ago, “Can I Have a Pet T. Rex?” is a short interdisciplinary portrait documentary. It features paleontologist and Kod*lab postdoc Aja Mia Carter and the Kod*lab robotics researchers Wei-Hsi Chen (also a postdoc) and J. Diego Caporale, a Ph.D. student.

It’s been two years! Where is her pet T. rex!?

[ Kod*Lab ]

I am not entirely sure why CMU and HEBI had robots at the 2026 NFL Draft, but I’m entirely sure that it made it more interesting to watch.

[ HEBI Robotics ]

Thanks, Trevor!

Ethan Lauer, a software engineer, answers your questions about robot perception, world modeling, and what spooks our Stretch robot.

[ Boston Dynamics ]

Yet another thing that a robot is consistently better at than I am.

[ Generalist ]

If you’re wondering where all those reported humanoid robot sales are coming from, it’s because every big company needs one or two for this sort of thing.

[ Impress ]

Full-color laser yo-yo zapper, a phrase never before written in the history of the universe.

[ Ishikawa Group Laboratory ]

The future of the L’Oréal Pro 2026 Le Hair Show is...a bald robot?

[ LimX Dynamics ]

Meet MagicHand H01, our all-new dexterous hand.

[ MagicLab ]

This is briefly one of the flattest quadrupeds I have ever seen.

[ DEEP Robotics ]

I appreciate that Engineered Arts did not try to cover up the sound in this video.

[ Engineered Arts ]

This is very impressive considering that magnets are basically indistinguishable from magic.

[ Sung Lab ]

NASA has two rovers on Mars, but they’re exploring entirely different eras of the planet’s past. Separated by 2,300 miles, the two rovers are uncovering clues from very different moments in Martian history. Perseverance is on the rim of Jezero Crater, where it’s studying some of the oldest Martian terrain ever explored while searching for signs of ancient microbial life. Meanwhile, Curiosity is climbing Mount Sharp inside Gale Crater, where layers of rock reveal how Mars’s climate changed as water dried up from its surface.

[ NASA ]

We’ve built a surgical robot to automate key steps in the process of receiving a Neuralink implant to promote safety, reliability, and scalability.

[ Neuralink ]

The Chinese-made Unitree G1 humanoid robots are making their way into the United States. And they aren’t just in viral videos but in major tech companies like OpenAI and Nvidia, and top academic institutions. Most arrive through Robostore, a robotics reseller based on Long Island. I went there to watch them come off the pallet, then brought one to my home to see what it could actually do. Are these the future of home robots? A security risk? A Chinese surveillance system on legs? I got answers—and a broken toe.

[ New Things ]

How do autonomous robots make decisions when the world is unpredictable? From self-driving cars to drone swarms, autonomous systems must operate under uncertainty—making real-time decisions with incomplete or unreliable data. In this video, Harvard SEAS Prof. Stephanie Gil explains how AI-powered robots coordinate, adapt, and stay safe in complex, real-world environments.

[ Harvard University ]

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.

ICRA 2026: 1–5 June 2026, VIENNA

RSS 2026: 13–17 July 2026, SYDNEY

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

Enjoy today’s videos!

Sony AI’s latest research, published on the cover of Nature, addresses a long-standing challenge in physical AI: Can a high-speed autonomous system master the complex perception and dynamic control required to compete against professional athletes?

[ Sony AI ]

In this video, we present Ringbot Quad, a novel monocycle robot with four legs that combines wheeled and legged locomotion on a single platform. Ringbot Quad is designed as a unique monocycle mechanism that replaces the traditional drivetrain with four individually actuated driving modules, each integrated with an articulated leg.
Ringbot Quad aims to provide versatile and efficient mobility through two distinct locomotion modes. In driving mode, the four legs assist with balance and steering, while in walking mode, they fully support the body for quadruped locomotion. By switching between these modes, Ringbot Quad can navigate diverse terrains and overcome obstacles that are difficult for either wheeled or legged systems alone.

[ Kinetic Intelligent Machine Lab ]

Humanoid robots have beaten human runners in a Beijing half-marathon, marking a breakthrough in China’s rapidly advancing robotics industry. More than 100 robots competed alongside 12,000 people in the 21-kilometer race, with three crossing the finish line ahead of any human.

[ Al Jazeera ]

Watch AthenaZero juggle barehanded using on-board sensory feedback only. No motion capture. No funnels. No help adding the third ball. The robot learns to adapt to the uncertainties from contact and the appropriate hand-eye coordination.

[ Robotics and AI Institute ]

From the look of this, it’s based on data capture from humans. What I want to know is, what this will look like when it’s not based on data capture from humans.

[ Unitree ]

Looks like Sphero would like to fill that sad gap in educational robotics left by LEGO Mindstorms.

[ Sphero ]

I am pretty sure that this is not how the shell game is played.

[ Generalist ]

At this point, real value from robots in warehouses much more commonly comes from systems like these, not humanoids.

[ Berkshire Grey ]

Scientists at the Max Planck Institute for Intelligent Systems propose a method to measure the efficiency of soft electrostatic actuators, enabling systematic evaluation of electrical-to-mechanical energy conversion. Using Peano-HASEL actuators, they demonstrate efficiencies up to 63.6%, over three times higher than previously reported, and validate the approach across other actuator types, paving the way for more energy-efficient soft electrostatic robotic systems.

[ Max Planck Institute ]

Already deployed in North America, quadruped robots provide continuous patrol, real-time monitoring, and faster incident detection across residential communities—day and night.

Um, thanks, but no thanks.

[ DEEP Robotics ]

Catching drones with what looks like a UR20 robot arm is a neat trick.

[ Skydio ]

Overactuated drones performing aerial maneuvers will always look just a little bit wrong to me.

[ Paper ] from [ ETH Zurich ]

Need a rugged and reliable mobile manipulator? Please consider a not-humanoid.

[ Clearpath ]

This CMU Robotics Institute talk is from CMU’s Raj Reddy, on “The Future of AI : Doomers vs. Abundance.”

The last decade has seen extraordinary advances in AI. The potential arrival of Artificial General Intelligence (AGI) has profound implications for future of our society. We anticipate a world where AI assistants and humanoid robots will perform most of the tasks requiring human expertise and skill at 10% of current costs. In this paradigm, essential services—including food, housing, energy, education, healthcare, and transportation—will be provided via Universal Basic Services, signaling a historic shift from a society of scarcity to one of abundance. This transformation raises a critical concern: widespread displacement of traditional labor. What is the human role when AI can do everything? This talk presents an alternative scenario: a “Human-in-the-Loop” evolution. In this model, humans transition into high-level supervisory roles, collaborating with AGI to train robots in novel skills and adapt them to unforeseen tasks.
We explore this as the “Maharaja Model” where technology serves humanity so comprehensively that work will be optional for humans. Finally, we will discuss how institutions like the Robotics Institute must lead this transition, developing the hybrid technologies and ethical frameworks necessary to bridge the gap between our current economy and a robot-assisted future.

[ Carnegie Mellon University Robotics Institute ]

Tom Burick has always considered himself a builder. Over the years he’s designed robots, constructed a vintage teardrop trailer, and most recently, led a group of students in building a full-scale replica of a pivotal 1940s computer.

Burick is a technology instructor at PS Academy in Gilbert, Ariz., a middle and high school for students with autism and other specialized learning needs. At the start of the 2025–26 school year, he began a project with his students to build a full-scale replica of the Electronic Numerical Integrator and Computer, or ENIAC, for the 80th anniversary of the historic computer’s construction. ENIAC was one of the world’s first programmable electronic computers. When it was built, it was about one thousand times as fast as other machines.

Before becoming a teacher, Burick owned a robotics company for a decade in the 2000s. But when a financial downturn forced him to close the business, he turned to teaching. “I had so many amazing people help me when I was young [who] really gave me their time and resources, and really changed the trajectory of my life,” Burick says. “I thought I need to pay that forward.”

Becoming a Roboticist

As a young child in Latrobe, Pa., Burick watched the television show Lost in Space, which includes a robot character who protects the family. “He was the young boy’s best friend, and I was so captivated by that. I remember thinking to myself, I want that in my life. And that started that lifelong love affair with robotics and technology.”

He started building toy robots out of anything he could find, and in junior high school, he began adding electronics. “By early high school, I was building full-fledged autonomous, microprocessor-controlled machines,” he says. At age 15, he built a 150-pound steel firefighting robot, for which he won awards from IEEE and other organizations.

Burick kept building robots and reached out for help from local colleges and universities. He first got in touch with a student at Carnegie Mellon University, who invited him to visit campus. “My parents drove me down the next weekend, and he gave me a tour of the robotics lab. I was mesmerized. He sent me home with college textbooks and piles of metal and gears and wires,” Burick says. He would read the textbook a page at a time, reading it again and again until he felt he had an understanding of it. Then, to help fill gaps in his understanding, he got in touch with a robotics instructor at Saint Vincent College, in his hometown of Latrobe, who let him sit in on classes. Each of these adults, he says, “helped change the trajectory of my life.”

Toward the end of high school, Burick realized that college wouldn’t be the right environment for him. “I was drawn to real-world problem-solving rather than structured coursework and I chose to continue along that path,” he says. Additionally, Burick has dyscalculia, which makes traditional mathematics more challenging for him. “It pushed me to develop alternative methods of engineering.”

The ENIAC replica Burick’s students built precisely matches what the original computer would have looked like before it was disassembled in the 1950s. Robert Gamboa

When he graduated, he worked in several tech jobs before starting his own company. In 2000, he opened a computer retail store and adjacent robotics business, White Box Robotics. The idea for the company came when Burick was building a “white box” PC from standard, off-the-shelf components, and realized there was no comparable product for robotics.

So, he started developing a modular, general-purpose platform that applied white box PC standards to mobile robots. “The robot’s chassis was like a box of Legos,” he says. You could click together two torsos to double its payload, switch out the drive system, or swap its head for a different set of sensors. He filed utility and design patents for the platform, called the 914 PC-Bot, and after merging with a Canadian defense robotics company called Frontline Robotics, started production. They sold about 200 robots in 17 countries, Burick says.

Then the 2008 financial crisis hit. White Box Robotics held on for a couple of years, shuttering in late 2010. “I got to live my life’s dream for 10 years,” he says. After closing White Box, “there was some soul searching” about what to do next. He recalled the impact his own mentors had, and decided to pay it forward by teaching.

Neurodiversity as a Superpower

In 2013, Burick started working in a vocational training program for young adults living with autism. The program didn’t have a technical arm, so he started one and ran it until 2019, when he was hired to be a technology instructor at PS Academy Arizona.

Burick and one of his students assemble the base for one of ENIAC’s three portable function tables, which contained banks of switches that stored numerical constants. Bri Mason

Burick feels he can connect with his students, because he is also neurodivergent. Throughout his childhood, he was told what he wasn’t able to do because of his dyscalculia diagnosis. “People tell you what it takes, but they never tell you what it gives,” Burick says.

In adulthood, he realized that some of his strengths are linked to dyscalculia, too, like strong 3D spatial reasoning. “I have this CAD program that runs in my head 24 hours a day,” he says. “I think the reason I was successful in robotics, truly, was because of the dyscalculia…. To me, [it] has always been a superpower.”

Whenever his students say something disparaging about living with autism, he shares his own experience. “You need to have maybe just a bit more tenacity than others, because there are parts of it you do have to fight through, but you come through with gifts and strengths,” he tells them.

And Burick’s classes aim to play to those strengths. “I didn’t want my technology program to feel like craft hour,” he says. Instead, through projects like the ENIAC replica, students can leverage traits many of them share, like the abilities to hyperfocus and to precisely repeat tasks.

Recreating ENIAC

Burick has taught his students about ENIAC for several years. While reading about it, he learned that the massive, 27-tonne computer was dismantled and partially destroyed after being decommissioned in 1955. Although a few of ENIAC’s 40 original panels are on display at museums, “there was no hope of ever seeing it together again. We wanted to give the world that experience,” Burick says.

He and his students started by learning about ENIAC, and even Burick was surprised by how complex the 80-year-old computer was. They built a one-twelfth scale model to help the students better understand what it looked like. Seeing the students light up, Burick became confident in their ability to move onto the full-scale model, and he started ordering supplies.

ENIAC was composed of 40 large metal panels arranged in a U-shape that housed its many vacuum tubes, resistors, capacitors, and switches. Twenty of the panels were accumulators with the same design, so the students started with these, then worked through smaller groupings of panels. The repeating panels brought symmetry to ENIAC, Burick says, but it was also one of the main challenges of recreating it. If one part was slightly out of place, the next one would be too and the mistake would compound.

The students installed 500 simulated vacuum tubes in each of the panels here, for a total of 18,000 vacuum tubes.Robert Gamboa

Once they constructed the panels, they added ENIAC’s three function tables, which stored numerical constants in banks of switches, then two punch-card machines. Finally, they installed 18,000 simulated vacuum tubes. In total, the project used nearly 300 square meters of thick-ream cardboard, 1,600 hot-glue-gun sticks, and 7 gallons of black paint.

The scale of the machine—and his students’ work—left Burick in awe. “By the time we were done, I felt like I was in a room full of scientists,” he says.

Previously, Burick’s students built an 8-foot-long drivable Tesla Cybertruck (“complete with a 400-watt stereo system and a subwoofer”) and he plans to keep the momentum with another recreation—maybe from the Apollo moon missions.

“I go to work every day, and I feel passionate about robotics [and] technology. I get to share that passion with the students,” Burick says. “I get to feel what it’s like to be in the position of the people that helped me. It closes that loop, and I find that really rewarding.”

The American Security Robotics Act, a bipartisan bill introduced in March by Senators Tom Cotton (R-Ark.) and Chuck Schumer (D-N.Y.) and Representative Elise Stefanik (R-N.Y.), proposes to limit U.S. government use of Chinese ground robots including humanoids, dogs, and crawlers. The proposal came just a few days after the Federal Communications Commission (FCC) tightened its rules for new foreign-made routers. The two changes are part of a much broader decoupling of sensitive U.S. tech from China, which include semiconductors, port cranes, logistics data, telecom cellular base stations and network hardware, security cameras, passenger vehicles, and, in December 2025, uncrewed aircraft systems (UAS) including those sold by DJI.

“I see the robots and the routers as being the latest in a long line of growing tech security concerns in the U.S. vis-à-vis Chinese technology,” says sociologist Kyle Chan of the Brookings Institute in Washington, D.C, who testified on 16 April 2026 before the Congressional Select Committee on the Strategic Competition Between the United States and the Chinese Communist Party.

Certain U.S. firms, such as Ghost Robotics, may benefit, because they are among the few companies that can handle demand for ground robots from U.S. government buyers. Ground robots are finished products at the top of the chain of added value, unlike semiconductors, which are “lower” down the value chain since they are always components of other products. If the proposed ground robot ban were to move lower down the value chain, preventing American robot makers from buying Chinese-made components, those companies might have a harder time fulfilling U.S. demand. The U.S. robotics industry is in a pickle: Companies would benefit from eliminating Chinese competitors at their level of the value chain, so long as they can retain their Chinese suppliers.

The U.S. does not have a serious, overarching strategy to guiding its approach to the U.S.-China techno-economic competition.” —Stephen Ezell, Information Technology & Innovation Foundation

It’s still early for the ground robotics industry in the U.S. Adoption is not yet that high, nor are the supply chains mature yet. South Korea and Japan make many crucial robot components, for example, so if they or other countries the U.S. considers friendly can replace Chinese components the U.S. government declares unsafe, the U.S. robotics industry may be able to adapt and build its competitiveness.

For other technologies, it’s Chinese tech all the way down the chain. The UAS market, for example, is dominated by Chinese producers. The U.S. Department of Commerce has sought to ban them for more than a year, and in December, the FCC added UAS’s to its import ban list, called the Covered List.

“That was a problem with the drone ban,” Chan says. “Rather than thinking about how you would ramp up domestic production and then have this tapering off of dependence on Chinese drones, it was a sharp and fast switch, which left industry in the lurch.”

Many Supply Chains Already Extend Beyond China

The FCC’s March ban on new foreign-made routers was a surprise to that industry. In 2025, the U.S. imported nearly US $ 31 billion of routers, according to the Global Electronics Association. Yet China produced only 1.1 percent of that, by value, down from around 20.5 percent of the U.S. market share in 2019. In 2025, the top three sources of routers in the U.S. by value were Vietnam, Mexico, and Thailand, together accounting for 68.4 percent of the market.

“A lot of this is more nuanced than the regulatory approaches suggest. The real vulnerabilities are outdated software, patches that haven’t been installed, unchanged default passwords,” says Global Electronics Association economist Shawn DuBravac, one of the authors of the association’s report.

On 14 April, the FCC issued conditional approvals for U.S. distribution of certain Netgear and Adtran routers, along with Sees.ai UAS’s. U.S.-headquartered Netgear manufactures routers in Vietnam and Taiwan, according to Consumer Reports. DuBravac says the fact that the FCC took only about three weeks to exempt those imports is positive, but that since the exemptions last only 18 months, manufacturers must still contend with a lot of uncertainty.

“If you’re a company you’re going to have to have clear visibility into your suppliers and into your suppliers’ suppliers,” DuBravac says. “There’s much, much more scrutiny.”

The last several U.S. administrations have restricted a growing list of Chinese tech, across both political parties. “I see this as bipartisan,” Chan says, “and I would expect continued scrutiny.”

Companies building technology subject to security controls should also prepare for speed. A White House interagency task force determined that foreign routers were a security risk, leading to the FCC’s Public Safety and Homeland Security Bureau announcing first the UAS ban and later the router ban. Because UAS’s use radio to communicate, they are subject to FCC oversight. Both security-related determinations, unlike conventional FCC rule making, did not require public notice or a commenting period.

“There hasn’t been much of a back and forth process into [the UAS] rule,” Chan says.

The electronics industry is also accustomed to more dialogue with trade-related changes, DuBravac says. “When you see a problem, you open an investigation and stakeholders can submit input into that investigation so it feels a little more like a two-way conversation, so you’re actually hearing from industry on this.” So far, that has not happened.

Instead, even analysts that welcome U.S. security scrutiny of Chinese technology are finding the fits and starts of the associated policymaking jarring, says Stephen Ezell of the Information Technology and Innovation Foundation, a think tank in Washington, D.C.: “The U.S. does not have a serious, overarching strategy guiding its approach to the U.S.-China techno-economic competition.”

When the robotics engineering field that Maja Matarić wanted to work in didn’t exist, she helped create it. In 2005 she helped define the new area of socially assistive robotics.

As an associate professor of computer science, neuroscience, and pediatrics at the University of Southern California, in Los Angeles, she developed robots to provide personalized therapy and care through social interactions.

Maja Matarić

Employer

University of Southern California, Los Angeles

Job Title

Professor of computer science, neuroscience, and pediatrics

Member grade

Fellow

Alma maters

University of Kansas and MIT

The robots could have conversations, play games, and respond to emotions.

Today the IEEE Fellow is a professor at USC. She studies how robots can help students with anxiety and depression undergo cognitive behavioral therapy. CBT focuses on changing a person’s negative thought patterns, behaviors, and emotional responses.

For her work, she received a 2025 Robotics Medal from MassRobotics, which recognizes female researchers advancing robotics. The Boston-based nonprofit provides robotics startups with a workspace, prototyping facilities, mentorship, and networking opportunities.

When receiving the award at the ceremony in Boston, Matarić was overcome with joy, she says.

“I’ve been very fortunate to be honored with several awards, which I am grateful for. But there was something very special about getting the MassRobotics medal, because I knew at least half the people in the room,” she says. “Everyone was just smiling, and there was a great sense of love.”

Seeing herself as an engineer

Matarić grew up in Belgrade, Serbia. Her father was an engineer, and her mother was a writer. After her father died when she was 16, Matarić and her mother moved to the United States.

She credits her father for igniting her interest in engineering, and her uncle who worked as an aerospace engineer for introducing her to computer science.

Matarić says she didn’t consider herself an engineer until she joined USC’s faculty, since she always had worked in computer science.

“In retrospect, I’ve always been an engineer,” Matarić says. “But I didn’t set out specifically thinking of myself as one—which is just one of the many things I like to convey to young people: You don’t always have to know exactly everything in advance.”

Maja Matarić and her lab are exploring how socially assistive robots can help improve the communication skills of children with autism spectrum disorder. National Science Foundation News

While pursuing her bachelor’s degree in computer science at the University of Kansas in Lawrence, she was introduced to industrial robotics through a textbook. After earning her degree in 1987, she had an opportunity to continue her education as a graduate student at MIT’s AI Lab (now the Computer Science and Artificial Intelligence Lab). During her first year, she explored the different research projects being conducted by faculty members, she said in a 2010 oral history conducted by the IEEE History Center. She met IEEE Life Fellow Rodney Brooks, who was working on novel reactive and behavior-based robotic systems. His work so excited her that she joined his lab and conducted her master’s thesis under his tutelage.

Inspired by the way animals use landmarks to navigate, Matarić developed Toto, the first navigating behavior-based robot. Toto used distributed models to map the AI Lab building where Matarić worked and plan its path to different rooms. Toto used sonar to detect walls, doors, and furniture, according to Matarić’s paper, “The Robotics Primer.”

After earning her master’s degree in AI and robotics in 1990, she continued to work under Brooks as a doctoral student, pioneering distributed algorithms that allowed a team of up to 20 robots to execute complex tasks in tandem, including searching for objects and exploring their environment.

Matarić earned her Ph.D. in AI and robotics in 1994 and joined Brandeis University, in Waltham, Mass., as an assistant professor of computer science. There she founded the Interaction Lab, where she developed autonomous robots that work together to accomplish tasks.

Three years later, she relocated to California and joined USC’s Viterbi School of Engineering as an assistant professor in computer science and neuroscience.

In 2002 she helped to found the Center for Robotics and Embedded Systems (now the Robotics and Autonomous Systems Center). The RASC focuses on research into human-centric and scalable robotic systems and promotes interdisciplinary partnerships across USC.

Matarić’s shift in her research came after she gave birth to her first child in 1998. When her daughter was a bit older and asked Matarić why she worked with robots, she wanted to be able to “say something better than ‘I publish a lot of research papers,’ or ‘it’s well-recognized,’” she says.

“In academia, you can be in a leadership role and still do research. It’s a wonderful and important opportunity that lets academics be on top of our field and also train the next generation of students and help the next generation of faculty colleagues.”

“Kids don’t consider those good answers, and they’re probably right,” she says. “This made me realize I was in a position to do something different. And I really wanted the answer to my daughter’s future question to be, ‘Mommy’s robots help people.’”

Matarić and her doctoral student David Feil-Seifer presented a paper defining socially assistive robotics at the 2005 International Conference on Rehabilitation Robotics. It was the only paper that talked about helping people complete tasks and learn skills by speaking with them rather than by performing physical jobs, she says.

Feil-Seifer is now a professor of computer science and engineering at the University of Nevada in Reno.

At the same time, she founded the Interaction Lab at USC and made its focus creating robots that provide social, rather than physical, support.

“At this point in my career journey, I’ve matured to a place where I don’t want to do just curiosity-driven research alone,” she says. “Plenty of what my team and I do today is still driven by curiosity, but it is answering the question: ‘How can we help someone live a better life?’”

In 2006 she was promoted to full professor and made the senior associate dean for research in USC’s Viterbi School of Engineering. In 2012 she became vice dean for research.

“In academia, you can be in a leadership role and still do research,” she says. “It’s a wonderful and important opportunity that lets academics be on top of our field and also train the next generation of students and help the next generation of faculty colleagues.”

Research in socially assistive robotics

One of the longest research projects Matarić has led at her Interaction Lab is exploring how socially assistive robots can help improve the communication skills of children with autism spectrum disorder. ASD is a lifelong neurological condition that affects the way people interact with others, and the way they learn. Children with ASD often struggle with social behaviors such as reading nonverbal cues, playing with others, and making eye contact.

Matarić and her team developed a robot, Bandit, that can play games with a child and give the youngster words of affirmation. Bandit is 56 centimeters tall and has a humanlike head, torso, and arms. Its head can pan and tilt. The robot uses two FireWire cameras as its eyes, and it has a movable mouth and eyebrows, allowing it to exhibit a variety of facial expressions, according to the IEEE Spectrum’s robots guide. Its torso is attached to a wheeled base.

The study showed that when interacting with Bandit, children with ASD exhibited social behaviors that were out of the ordinary for them, such as initiating play and imitating the robot.

Matarić and her team also studied how the robot could serve as a social and cognitive aid for elderly people and stroke patients. Bandit was programmed to instruct and motivate users to perform daily movement exercises such as seated aerobics.

Maja Matarić and doctoral student Amy O’Connell testing Blossom, which is being used to study how it can aid students with anxiety or depression.University of Southern California

Over the years, Matarić’s lab developed other robots including Kiwi and Blossom. Kiwi, which looked like an owl, helped children with ASD learn social and cognitive skills, helped motivate elderly people living alone to be more physically active, and mediated discussions among family members. Blossom, originally developed at Cornell, was adapted by the Interaction Lab to make it less expensive and personalizable for individuals. The robot is being used to study how it can aid students with anxiety or depression to practice cognitive behavioral therapy.

Matarić’s line of research began when she learned that large language model (LLM) chatbots were being promoted to help people with mental health struggles, she said in an episode of the AMA Medical News podcast.

“It is generally not easy to get [an appointment with a] therapist, or there might not be insurance coverage,” she said. “These, combined with the rates of anxiety and depression, created a real need.”

That made the chatbot idea appealing, she says, but she was interested to see if they were effective compared with a friendly robot such as Blossom.

Matarić and her team used the same LLMs to power CBT practice with a chatbot and with Blossom. They ran a two-week study in the USC dorms, where students were randomly assigned to complete CBT exercises daily with either a chatbot or the robot. Participants filled out a clinical assessment to measure their psychiatric distress before and after each session.

The study showed that students who interacted with the robot experienced a significant decrease in their mental state, Matarić said in the podcast, and students who interacted with the chatbot did not.

“Joining an [IEEE] society has an impact, and it can be personal. That’s why I recommend my students join the organization—because it’s important to get out there and get connected.”

She and her team also reviewed transcripts of conversations between the students and the robot to evaluate how well the LLM responded to the participants. They found the robot was more effective than the chatbot, even though both were using the same model.

Based on those findings, in 2024 Matarić received a grant from the U.S. National Institute of Mental Health to conduct a six-week clinical trial to explore how effective a socially assistive robot could be at delivering CBT practice. The trial, currently underway, also is expected to study how Blossom can be personalized to adapt to each user’s preferences and progress, including the way the robot moves, which exercises it recommends, and what feedback it gives.

During the trial, the 120 students participating are wearing Fitbits to study their physiologic responses. The participants fill out a clinical assessment to measure their psychiatric distress before and after each session.

Data including the participants’ feelings of relating to the robot, intrinsic motivation, engagement, and adherence will be assessed by the research team, Matarić says.

She says she’s proud of the graduate students working on this project, and seeing them grow as engineers is one of the most rewarding parts of working in academia.

“Engineers generally don’t anticipate having to work with human study participants and needing to understand psychology in addition to the hardcore engineering,” she says. “So the students who choose to do this research are just wonderful, caring people.”

Finding a community at IEEE

Matarić joined IEEE as a graduate student in 1992, the year she published her first paper in IEEE Transactions on Robotics and Automation. The paper, “Integration of Representation Into Goal-Driven Behavior-Based Robots,” described her work on Toto.

As a member of the IEEE Robotics and Automation Society, she says she has gained a community of like-minded people. She enjoys attending conferences including the IEEE International Conference on Robotics and Automation, the IEEE/RSJ International Conference on Intelligent Robots and Systems, and the ACM/IEEE International Conference on Human-Robot Interaction, which is closest to her field of research.

Matarić credits IEEE Life Fellow George Bekey, the founding editor in chief of the IEEE Transactions on Robotics, for recruiting her for the USC engineering faculty position. He knew of her work through her graduate advisor Brooks, who published a paper in the journal that introduced reactive control and the subsumption architecture, which became the foundation of a new way to control robots. It is his most cited paper. Bekey, who was editor in chief at the time, helped guide Brooks through the challenging review process. Matarić joined Brooks’s lab at MIT two years after its publication, and her work on Toto built on that foundation.

“Joining a society has an impact, and it can be personal,” she says. “That’s why I recommend my students join the organization—because it’s important to get out there and get connected.”