How Virgin Atlantic used Codex to ship its revamped mobile app on a fixed holiday travel deadline, reaching near-total unit test coverage and zero P1 defects.
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.
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.
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.
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.
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.
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.
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!!
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.
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.
OpenAI is named a leader in the 2026 Gartner Magic Quadrant for Enterprise AI Coding Agents, with Codex recognized for innovation and enterprise-scale deployment.
How a former Navy officer’s will to serve is shaping the future of hardware development
Although Cameron McCord wasn’t himself present at Edwards Air Force Base, what was about to transpire that May 2025 day in the Mojave Desert would still make it one he would never forget. AJ Piplica, CEO of Hermeus, was huddled with his team on the tarmac. No matter the outcome of the day’s flight test, he too was going to have a company-defining day. After years of labor, Piplica and his team would be testing their new hypersonic airplane engine, first taxiing down the runway, and assuming that went well, actual liftoff.
Even more nerve-wracking than the prospect of fundraising if the test failed was that of the dual taxi and liftoff test in a single day, a feat that would have been unthinkable even a few years ago. “Our data from the taxi needed to give us confidence that we’re not taking a dumb risk by attempting takeoff and landing. And that’s a huge amount of data to review, that historically took weeks, if not months, to parse between high-speed taxi and first flight,” Piplica says. The time they’d been allotted on the runway for both tests: two hours.
But his team had a new secret weapon. After years of employing a messy patchwork of data review tools that were not designed for hardware testing, Piplica had recently signed a contract with McCord’s Nominal, a company whose sole focus was testing hardware for real-world deployment. Hermeus had seen success with the platform for preliminary Hardware-In-the-Loop (HITL) tests in their Atlanta warehouse, but never in the field with the Air Force breathing down their necks, and certainly never with such high stakes and so little time.
As Hermeus’s plane started taxiing smoothly down the runway just under liftoff speed, data began to stream in through Nominal’s platform: the health of the brakes, actuators, avionics, electronics, control surfaces and more—terabytes of data giving a real-time window into the system’s health. As the plane slowed its taxi on the end of the tarmac, Piplica checked in with his engineers, hoping for good news. “Data review was done by the time we’d towed the airplane back to the other end of the runway,” he remembers, “everybody was thumbs up, so we concluded that, and we were at a safe level of risk to attempt a flight.” Minutes later, their Quarterhorse Mark One was in the air for the first time. When it landed, Piplica snapped a picture and texted it to McCord, Nominal’s CEO. Under the photo it read simply, “Hey, we got it back.”
McCord’s Mojave flight test assist was far from his first brush with the Armed Forces. He was raised on stories about his grandfather, a greatest generation archetype, who had been rushed through the Naval Academy in three years to join the war in the Pacific, witnessed the Japanese foreign minister sign the instruments of surrender, had seen nuclear weapons detonations, and flown the longest flight in history over the South Pole. McCord’s father was a federal attorney in the Department of Justice, and his mother was a special needs teacher. In their home, being of service was a mantra, and an action; most days, McCord and his siblings were explicitly urged by their parents to question how they were being a force for good in the world. For McCord, inspired by his grandfather (and uncle and cousins in the Navy), “Good,” meant joining the Armed Forces.
He paid his way through MIT by doing ROTC, which is where McCord “got bitten by the entrepreneurial bug,” he remembers, but more via osmosis than in practice. In addition to ROTC, he played varsity soccer, double majored in Nuclear Engineering and Physics and minored in Political Science, for practical reasons (“All of this engineering, how do I transition that into something that impacts the world?”). Between training and practice and problem sets and lectures and more lectures, McCord observed his classmates tinkering and creating. In particular, he remembers not infrequently passing by his fraternity brother, Jason Hoch at 4 a.m.. Hoch would still be awake, finishing a CS problem set or hacking away at a new idea as McCord, just up and in uniform on his way to ROTC training, urged Hoch to get some sleep. He admired his classmates’ will to build and create, knowing it was an exercise he couldn’t yet fully embrace, but one that he filed away to explore in some eventual, less time-constrained future.
Within days of graduation, McCord was onboard the USS Helena, SSN-725, the newest officer on the submarine. Any notion that respect would be granted by virtue of his positional authority or pedigree was quickly disabused. “You just start from scratch with first principles—how do you build trust, rapport, and respect with these people where everything you were is stripped away to zero?” says McCord. He did manage to win over his crew after they observed that what he lacked in revolutions around the sun, he made up for in thoughtful leadership, attention to detail, and commitment to understanding the nuances of the underwater behemoth they called their home. “It was my duty to understand that complexity,” says McCord. “Especially for those around me that were relying on me.” Before his service was finished, he’d have a chance to prove just how well he could navigate that complexity.
“We think it’s his appendix,” one of McCord’s crewmates informed him, as a fellow sailor nearby doubled over in pain. “It seems bad.” A few years had passed since McCord was the ‘new guy,’ during which he had experienced a midnight fire, a change in presidential administrations, and hundreds of nights underwater—but this was new. “Appendicitis on a deployed submarine on a mission is not a good thing. So the time was ticking,” says McCord. The ship’s only medically trained officer was not equipped to perform emergency surgery, and since the sub was mid-clandestine mission in North Atlantic waters, reaching a hospital before rupture meant navigating the vessel through Nordic fjords. McCord was tapped to ‘drive.’
Assuming the Conn (naval parlance for, “control of the ship”), he checked above water conditions: temperatures hovered around zero, and a blizzard made visibility nonexistent and conditions turbulent. But despite inclement conditions, this time, McCord wasn’t at a loss. Anxious, certainly, but his years of training, and his dedication to ensuring that he could be of service to his crew, paid off. “Because of my familiarity by then with all of the complexities of the submarine, I was able to gut into how to do this,” says McCord.
By then, McCord was accustomed to managing machinery designed during the Cold War, but that didn’t mean he relished the challenge. “We had to do a lot of very unnatural things with the submarine to get through this fjord and open that rear hatch.” When they neared the shore, a particularly burly soldier “essentially threw the sailor over to the Norwegian coast guard, who picked him up and rushed him to a hospital,” says McCord. Twelve hours later, a WhatsApp message from their new Nordic friend gave them the all clear health-wise.
For two more years, McCord lived many of his days underwater. In his free time, eager to stay apace with the outside world, McCord Coursera’d. “I would watch pre-downloaded Andrew Ng Stanford AI classes. This was in 2015, 2016, and I would be teaching myself and actually building out early models.” His autodidactic afternoons keeping up only reinforced how far his surroundings had fallen behind. “Learning AI, and then going into the control room where you have 1970s software, old hardware—it was a crazy cognitive dissonance.”
As he neared his five-year mark on the sub, it began dawning on McCord that the model of service and impact that had worked so well for his grandfather and uncle, both with decades-long careers in the military, might not be the right fit for him. “The military allowed them to make a huge impact on the world over 30 or so years,” says McCord. “But in today’s world, technology seemed like the way to make that impact. I wanted to use service-aligned technology, but I was not cool with the idea of having to wait 30 years.”
McCord on USS HELENA the day of the rescue mission
McCord had put in his time, “but then this incredible opportunity came by,” he says. He was selected to be one of the Navy’s liaisons to the House of Representatives. Between 2017 and 2019, McCord got a front-row seat to another deeply complex system. “You get to understand how a bill becomes a law,” says McCord. “But you also get to understand the personalities, the relationships, the executive branch, how budgets get passed, the back doors on The Hill—how it actually happens. I lived that viscerally for two years.”While his job description was to “sort of be a good steward of the Navy, tell war stories, build support,” he also made time for an extracurricular: “I was someone that members of Congress and staffers could go to to understand cutting-edge technology,” says McCord. “I think it’s hard for them to find, frankly, a low-threat way to do this. I developed this reputation of, ‘Hey, you can actually just go down to the Navy liaison shop and there’s this MIT guy Cameron who can just explain LLMs or cybersecurity or why technical stuff in this bill is relevant.” In 2020, capping off his career in public service, McCord’s technical acumen earned him an invite to help develop the House Armed Services Committee’s report on how technology was going to change the nature of warfare (and world). “It’s a little bit crazy to say now, but it was really some of the first governmental writing about AI,” says McCord, whose name is listed in the footnotes among four-star admirals and heads of agencies.
From his years on the sub, McCord was no stranger to waking up at the crack of dawn, but now he found himself doing so in a decidedly more terrestrial context. It was 2020, and he and his first private sector team, Anduril’s drone defense system engineers, were cruising through California’s Central Valley towards the desert to test their system. When they found a suitably remote, dusty patch, they unpacked their hardware—a tower covered with cameras and infrared sensors and miniature drones—and set up their Wi-Fi ‘pucks’ to run flight tests. These sessions would often last days, with team members camping in their trucks to avoid the sunrise commute. Each morning, they would begin tests anew, generating telemetry and sensor data and logs and video and images. “And it was so difficult with the tools we had to capture that data, intuitively organize it, and just answer the question: Did the test work?” As the Product Manager of the system, he led his team in long whiteboard computational sessions, hours in MATLAB, or dated academic graphing software. “None of this was production scale. And none of this was modern in any sense,” says McCord.
The process itself he enjoyed—the rolling up of sleeves, the camaraderie—all of that was pleasantly, arduously familiar. But day after dusty day, McCord remembers feeling renewed shock at the state of hardware testing. Here he was, working in a startup ecosystem where software innovation thrived on cycles of rapid iteration, but hardware testing was stalled in a different century. And it wasn’t lost on him that if Anduril, a company with “all of the venture dollars in the world and incredibly smart people had challenges getting this right, what is like at old organization X or massive company Y? In the back of my head this was just so clearly an area where better software could improve quality of life, and improve outcomes,” says McCord. “To be clear, I didn’t have the answers, but I just was obsessed with the problem.”
McCord first attempted to solve that problem with emerging data technologies: tools built for business intelligence, data marketing, SQL-based tools. In short, nothing “built for the types of telemetry and high-frequency sensor data that robotic hardware systems generate,” says McCord. “So they don’t work.” After fifteen months at Anduril, McCord couldn’t ignore his obsession any longer. Eager to get his bearings in a new complex system—the world of entrepreneurship and VC funding—McCord pitched his rough idea, improving hardware testing, and himself as a part-time entrepreneur-in-residence, to Josh Wolf at the VC firm, Lux Capital. In return for an opportunity to speak with dozens of hardware CTOs to pressure test his thesis, McCord offered to help Lux develop a dual-use, government-private sector business strategy.
For a year, co-enrolled at Harvard for his MBA, McCord effectively moonlit for Lux. “This was only possible because COVID was happening,” says McCord. “I would do Zoom classes and I would shut my Harvard Business School laptop and open my Lux laptop and basically alternate between the two.” HBS is where McCord first encountered Bryce Strauss, a kindred spirit with an aerospace background. McCord asked him to coffee, expecting a 30-minute conversation, and the pair ended up venting for nearly three hours. “It was this winding back-and-forth where we both obsessed about post-test analysis and data review, and how it’s essential to quickly get insight into performance data —whether you’re building an airplane, a car, a nuclear reactor, a drone, or a robot. Every person that builds hardware does this thing,” says McCord. “And we’re like, if we could just make that process 10 times better, we think we can build something valuable here.”
Strauss concluded they needed to turn this idea into a company together, and he wouldn’t take no for an answer. “I’m always doing a lot of things, and Bryce was this incredible unifying north star that was like, ‘Cameron, when we graduate, we’re doing this,’” says McCord. The duo decided to enlist a third, software co-founder. For McCord, there was really only one choice: “I was like, ‘Hey, I know this guy, Jason Hoch from MIT. He’s the smartest software engineer I’ve ever met. I think he’s the person to be the third leg of the stool.’” Strauss, the aerospace expert, came up with a name, a play on “All systems nominal,” common parlance for “all good” during rocket launches. 30 days before graduation, Lux Capital wrote Nominal their first check.
Nominal Cofounders Jason Hoch, Bryce Strauss, Cameron McCord
“There’s a bug,” Hoch said from the backseat of their rental car. Six months into their tenure as co-founders, the trio had flown to Los Angeles to demo an early prototype of Nominal’s hardware-in-the-loop (HITL) testing system for a major satellite company, who they hoped would be their first paying customer. “Something’s off with time synchronization of different data sources,” added Hoch. With 20 minutes to go before their pitch, he’d have to do some en route hacking.
When they arrived, the trio walked into a boardroom to find roughly a dozen company leaders waiting. Strauss demoed their product, demonstrating the speed with which it would allow engineers to manage and interpret satellite test data. The last minute hack held, and the time sync across different data sources—telemetry, engine health, computation speeds and more—worked. The trio was ecstatic, even when the satellite company only signed on for an unpaid pilot of their software. “They took a bet on us and they let us learn with them, frankly, which is more valuable than anything,” says McCord.
With some assurance their product worked and had utility for customers, McCord, Hoch, and Strauss decided to hire four more full-time engineers. Still the latter days of COVID in fall 2021, everyone worked remotely half of the time, and would converge in Austin every other week, rent an Airbnb, and build from wakeup to well into Wendy’s-fueled late nights. As the company grew, their Austin reunions became every third week, then once a month. “Finally, we reached a point when we had around 40 people where we declared, we’re not going to have regularly scheduled Austin weeks anymore.”
When he wasn’t sleep deprived in Austin, McCord lived in Washington, D.C., a home that allowed him to leverage his public sector connections while growing his private sector customer base. That’s where he first met AJ Piplica for coffee at Commonwealth Joe’s. Before witnessing the pain of hardware testing as CEO of Hermeus, Piplica had experienced it in the public sector working for NASA. “Within the Department of Defense, which is a very vast test infrastructure—all the data is siloed,” he says. “You were literally moving CSVs around by hand and opening things in Excel.” After witnessing Nominal’s utility during his first Mojave flight test, Piplica recognized the step change it could represent for all hardware development. “Any organization that’s taking data out of the real world, which is, like, every major company in the world could benefit from this,” says Piplica. “Yeah, robotics and AI are cool, but what’s actually cool is when you put them together. That nexus between the digital and the physical world is what really unlocks a huge amount of growth for humanity.”
Ten days before his wedding in January 2025, McCord got a call from Alfred Lin, partner at Sequoia. Nominal was eyeing another round of fundraising to support the expansion of their team and their product. Lin, who had met McCord during Nominal’s Series A process but ultimately deferred investing (“We wanted more evidence in support of his hypothesis before investing”), understood the tailwinds accelerating Nominal’s growth, and wasn’t about to let another round pass him by. “We are living through a hardware renaissance, and we were looking for a new platform that supports modern hardware engineers on this journey”, says Sequoia partner, Anas Biad.
McCord wanted to work with Sequoia, but he wanted to get married first even more. “I told Alfred, look, I’m getting married. But can we schedule time for me to come to SF right when I get back? I will walk you through everything in the business.” Lin agreed, and as promised, McCord flew straight from honeymooning in New Zealand to meet with Lin and Biad. The partners were impressed by McCord, but told him they needed to do their due diligence on the product before any decisions were made. “For days, Anas basically didn’t sleep. He called every single one of their customers,” says Lin. In the end, McCord was reassured by the seriousness with which Sequoia took the whole process. He found it grueling, but ultimately affirming. “There was something pretty powerful in having Sequoia come back and be like, we spoke to 20 customers. People really did love the product.” Ten days after their SF meeting, McCord had a term sheet from Sequoia in hand.
At the time of the final interview for this piece in late March 2026, the war with Iran had started just days earlier. News had just come out about the first casualties on both sides of the conflict, among them, three US soldiers. That reality was weighing on McCord for many reasons, but resonated particularly in the context of his chosen means of service and field of impact. “I’m obviously reading about those casualties and I’m thinking, could it have been prevented? Could Nominal’s technology in some way, shape, or form, have improved the hardware they were using and helped prevent their deaths? I have no idea,” says McCord.
He’s acutely aware that the state of the world has changed the way people think about hardware manufacturing, and he’s ambivalent about what it took for that shift to occur. “I don’t like that there’s a global land conflict in Ukraine and a war in Iran, but the reality is that it’s happening,” says McCord. “And I think it is pushing everyone to rethink and say, ‘Hey, building physical things is critical.”
McCord sees a silver lining to this macro shift in attention to hardware development. He’s hopeful it will enable innovations outside the realm of defense and war, and is actively expanding Nominal’s capabilities for teams building rockets and medical devices, tools for water desalination and electric vehicles. The shift is also apparent in Nominal’s rapid growth: as of May 2026, Nominal has achieved unicorn status, with 75 global customers across aerospace, defense, energy, and transportation, a rapidly growing team, and a constantly expanding product surface area with an eye towards enabling anyone to build hardware efficiently and intelligently. His team is integrating AI to further speed up data collection and analysis, and enable edge computing for systems operating beyond connectivity range—particularly essential in aeronautics and astronautics. McCord understands that service is an ongoing act, and the urgency his parents instilled in him to do something has only grown more acute with time. With each innovation, McCord returns to a mantra, one influenced by his upbringing surrounded by people inspiring him to be of service. “I call it the grandpa test. I basically ask myself all the time, ‘how will I feel when I’m old and sitting in my chair and the grandkids are around?’” says McCord. “I think I will look back very fondly if Nominal played a part in moving the physical ambitions of humanity forward. We talk about flying cars, but yes, there’s also defense, and advanced energy, and compute to power this next generation of AI. There’s advanced transportation, mobility, and water purification. There’s so much we want to do.”
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.
Emerging AI capabilities in coding, tool utilisation, and multi-step reasoning, which is core to agentic AI, are shaping a more complex digital security environment. AI-based applications and services are becoming increasingly integrated into software, public services, vital sectors, and the broader economy, offering new opportunities for productivity and innovation. However, they also introduce new types of security exposure – misconfigurations and weaknesses that can lead to unauthorised access.
What AI security means in practice
AI security builds on traditional cybersecurity and goes beyond it. Today’s AI applications are trained on vast, diverse data and increasingly embedded in workflows that retrieve information, use tools, store memory, and perform multi-step tasks. Distinct AI security risks arise because models learn from data, behave probabilistically, and, in agentic systems, act more autonomously.
Many of the most important AI security risk patterns are only beginning to emerge. The evidence base remains uneven across issues such as prompt injection, agentic misuse, model poisoning, and the security of connected tools. AI technologies are by nature cross-border and cross-sectoral, meaning that no single country, company, or institution can adequately address the security challenges they create alone. This is especially true as AI agents become more capable and more embedded in real-world contexts.
Because AI models and applications operate across borders, security measures require international cooperation on shared methods, common tools and information-sharing channels. The same foundational research can also support adjacent safeguards, from content provenance against deepfakes to safer AI chatbot design.
To meet new security requirements, AI developers and governments across jurisdictions and sectors will need to drive additional joint research, more comparable evaluation methods and robust best practices. These should cover the full AI security lifecycle, from protecting models and pre-deployment assets such as model weights and datasets to the security and resilience of AI applications and services during deployment, especially in critical sectors.
A first approach to strengthening AI security requirements would be to pool resources across three areas: resilience against scalable, reusable attacks, such as prompt injection, particularly at the interaction layer; ensuring the secure connection of AI agents to tools and services; and strengthening safeguards for models, model weights, and training data integrity.
Priority 1: Defending against transferable and automated prompt-injection attacks
Direct and indirect prompt-injection attacks are becoming more reusable, scalable, and security relevant. That is why security researchers at the Open Worldwide Application Security Project (OWASP) rank prompt injections among the most prevalent AI security risks. While evidence on transferability is still developing, recent research suggests that some attacks can be adapted across harmful tasks and, in some cases, across different models.
For example, an AI assistant used in a workplace might be asked to summarise a document or webpage. If that content contains hidden malicious instructions, the system could be manipulated into ignoring the user’s request, revealing sensitive information, or taking an unintended action. That raises practical questions for enterprise deployment, testing, and incident response.
This matters because reusable or automated prompt-injection methods reduce attacker costs and make attacks easier to repeat. For policymakers, this shifts the question from whether a model can be bypassed in a lab setting to whether it remains resilient to attacks that are reused, adapted and improved over time.
The frontier security community should prioritise understanding why some prompt-injection attacks transfer more readily than others and develop evaluations that reflect adaptive, automated attack strategies. Rather than simply blocking known prompt patterns, they should also identify techniques that improve robustness. Recent benchmark work from MLCommons, a technical safety organisation, suggests that classifying attacks by model-manipulation techniques would help evaluators get closer to the underlying mechanisms of vulnerability.
Automated prompt-injection techniques are becoming more transferable and scalable, making it easier to discover, reproduce and act on vulnerabilities across systems. Evaluation capacity will therefore need to support ongoing assessment of how advances in frontier AI may accelerate vulnerability discovery and exploitation. This will be critical both for ensuring defensive testing keeps pace with evolving attack methods and for helping countries use AI more effectively to strengthen cyber resilience.
International collaboration could improve prompt injection evaluations and universal jailbreaks by making it easier to identify reusable and transferable vulnerabilities. International collaboration could improve evaluations of prompt injection and universal jailbreaks by identifying reusable and transferable vulnerabilities. It could also help to direct research investments from governments and frontier labs toward more robust evaluations and more durable defences.
Priority 2: Securing AI agents
AI agents create a new set of security challenges. Attacks can manipulate content before an agent application retrieves it, misuse connected tools, or hide malicious instructions in documents processed by the agentic system.
Consider an AI agent connected to email, calendars or payment tools. With broad permissions and malicious instructions, failures can become concrete: the agent may share sensitive information, trigger unwanted actions, or move the user’s money in unintended ways.
Because agents can plan tasks, use tools, retain memory and interact with other systems, each capability creates new opportunities for misuse and failure. Three examples illustrate this challenge:
Memory poisoning: Attackers insert malicious content into an agent’s memory or long-lived context, causing harmful behaviour that goes undetected and persists in future interactions.
Tool misuse: As AI systems adopt standardised methods, such as the Model Context Protocol (MCP), to connect to external tools, databases, and services, weak identity controls or excessive permissions can create new security gaps.
Context-based instruction attacks: Malicious instructions are hidden in emails, documents, or web pages and then retrieved by an agentic AI system, which treats them as part of its working context, potentially leading to unintended actions or data leakage.
These patterns go beyond traditional threat models. Recent work, including OWASP’s Top 10 for Agentic Applications, reflects a broader recognition that memory, connected tools, identity, permissions and multi-step autonomy create a distinct security profile for agentic systems.
Developments in the financial sector illustrate how quickly these questions are becoming pressing. On 2 March 2026, Banco Santander and Mastercard announced what they described as Europe’s first live, end-to-end payment executed by an AI agent within a regulated banking framework. As agentic AI systems move into higher-stakes domains, coordination on security baselines and implementation practices will become increasingly important.
AI agents will need to be made secure by design, and the external tools and services they connect to will need to be made secure. Design improvements could include stronger memory safeguards, better permission design and more effective sandboxing. Measures to make services reliant on agentic AI more robust could include research into deterministic controls that constrain how an agent can transfer information among untrusted content, memory and tool actions.
Cybersecurity institutions need to be involved from the beginning because issues related to AI agents—such as permissions, connected tools, and secure deployment in sensitive settings—build on well-understood challenges faced by cyber agencies. International collaboration among AI Institutes could help adapt existing security practices to agentic systems, identify shared vulnerabilities, and promote more uniform methods for testing, incident management, and secure deployment.
Priority 3: Detecting and mitigating model poisoning
Model poisoning happens when attackers introduce vulnerabilities or alter a model’s behaviour by manipulating training or fine-tuning data. Until recently, it was often assumed that this would require control over a significant share of a model’s training data, implying that larger models trained on broader datasets would be harder to compromise.
In practice, this means that poisoned content can propagate through the AI supply chain, shaping the behaviour of downstream models even when the original model developer has not been directly compromised.
That can have serious consequences for the security of open-weight models. An attacker could seed a small number of poisoned public documents that would propagate into multiple downstream training runs and forks without access to the original developer’s pipeline.
Promising research by Microsoft’s AI Red Team has explored ways to detect backdoored models at scale after they have been trained and shared. Early techniques identify poisoned models by analysing behavioural signatures and links between backdoors and memorisation, pointing to a promising solution in a previously difficult-to-detect risk area.
However, simply demonstrating that poisoning is possible is not enough. Research to improve the detection of compromised models and to strengthen safeguards during training and fine-tuning would help. So would developing more practical methods for recovery when poisoning is suspected. Promising research techniques are emerging, but they are not yet reliable across all models and deployment settings. This is where coordinated action and international collaboration among universities, frontier labs and AI institutes would be particularly valuable.
What shared AI security could look like in practice
These three security priorities show that no country is likely to build the necessary evidence base, testing capacity and operational practices on its own. The more effective objective is therefore to strengthen the foundations of shared AI security: maturing best practices, advancing targeted research, building clearer expectations around deployment conditions and improving cross-country coordination in ways that support more secure adoption over time.
Practical progress can start by establishing building blocks that enhance interoperability and gradually boost the effectiveness of national efforts.
International efforts led through the G7 and the OECD – GPAI are well placed to advance this agenda through a few practical building blocks :
Shared benchmarks and testing environments are essential. Countries and research organisations require standardised methods to evaluate poisoning risks, agentic vulnerabilities, indirect prompt injections and resilience to jailbreaks across various models and deployment scenarios.
Better channels for information-sharing on vulnerabilities. AI-related vulnerabilities often do not align well with existing cyber-disclosure procedures. Enhancing AI security calls for reliable means for sharing exploit techniques, mitigations and lessons learned in ways that support both transparency and the secure handling of sensitive information.
Common reference points for secure design. International coordination can help align threat-modelling practices that clarify emerging failure modes, risk-management approaches that translate those risks into governance best practices and security standards for critical components and infrastructure, including model weights. Together, these can support more secure implementation in practice.
Enhanced public-private dialogue. AI security is evolving too quickly for governments, researchers and companies to work in isolation. Regular exchange channels can help policymakers understand emerging risks while giving researchers and firms clearer signals on where shared benchmarks, disclosure practices and secure design guidance are most needed.
AI institutes, including members of the AI Network for Advanced AI Measurement, Evaluation and Science, can help align evaluation priorities and testing methods. Universities and independent researchers can deepen the knowledge base in fast-moving domains where attack and defence techniques evolve rapidly. Cybersecurity authorities can help turn emerging lessons into operational practices for secure deployment, especially in higher-stakes environments.
These communities should invest in resources, tools, policies, and operational capabilities to assess how frontier AI might. speed up vulnerability discovery. Beginning with France’s G7 Presidency, the next immediate steps could facilitate closer alignment on key research and evaluation priorities and improve information-sharing channels to manage these risks effectively.
For nations aiming to expand AI throughout their economies, integrating security into AI development and deployment processes will be crucial to promoting AI diffusion and adoption. As new models develop stronger reasoning and coding capabilities, governments, researchers, and companies will need better ways to measure their capabilities and define limitations. That makes shared research roadmaps, stronger testing methods and practical security practices more urgent for trusted deployment.
An OpenAI model solved the 80-year-old unit distance problem, disproving a major conjecture in discrete geometry and marking a milestone in AI-driven mathematics.
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
As we look to the future, there is no doubt that the world is bringing AI into the physical world through robots. We are at the beginning of a “Cambrian explosion“ of useful, intelligent machines. We believe AI is not one tool, but a huge frontier of technical approaches that is unlocking new capabilities so powerful, they will define our economy moving forward. This will happen not in one single definitive moment, but as an ongoing set of small and large breakthroughs, where AI-driven robots begin to provide real value in a few tasks, and then a few more, with impacts unfolding across numerous $100 billion-plus markets that will dramatically improve the quality of our lives.
OpenAI advances Education for Countries, expanding AI adoption in schools with new partnerships, teacher training, and tools to improve global learning outcomes.
OpenAI for Singapore launches a multi-year AI partnership to expand deployment, build local talent, and support businesses and public services with AI.
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.”
In 12 months, thousands of customers migrated hundreds of thousands of servers, saved 1.6+ million hours, and processed 4.5+ billion lines of code with AWS Transform. Celebrating its 1-year anniversary, AWS Transform agents now available in Kiro, Claude, Cursor, and Codex, including the agent builder toolkit Kiro power for building customized transformation agents.
To learn what happened in 12 months, the four things we learned, and how that evolved our roadmap, visit the one-year anniversary blog post.
Last week’s launches Here are last week’s launches that caught my attention:
The general availability of Claude Platform on AWS – You can get direct access to Anthropic’s native Claude Platform experience, including APIs, console, and early-access beta features, directly through your existing AWS account, without managing separate accounts, billing, or tracking. Claude Platform on AWS is operated by Anthropic, and customer data is processed outside the AWS security boundary. To learn more, visit the deep dive blog post.
Amazon EC2 M3 Ultra Mac instances – These instances are built on Apple M3 Ultra Mac Studio computers featuring a 28-core CPU, 60-core GPU, 32-core Neural Engine, and 256GB of unified memory. Compared to EC2 M4 Max Mac instances, M3 Ultra Mac instances provide 2x the unified memory, 1.75x the CPU cores, 1.5x the GPU cores, and 2x the Neural Engine cores, giving Apple developers the headroom to run significantly more Xcode simulators in parallel and accelerate on-device ML workflows to improve product time to market.
Amazon Redshift RG instances powered by AWS Graviton – These instances deliver better performance, running data warehouse and data lake workloads up to 2.4x as fast as previous generation RA3 instances, at 30% lower price per vCPU. RG instances include Redshift’s custom-built vectorized data lake query engine that processes Apache Iceberg and Parquet data on your cluster nodes.
Amazon Bedrock Advanced Prompt Optimization – You can optimize your prompts for any model on Bedrock, while comparing your original prompts to your optimized prompts across up to 5 models simultaneously. You can also use this if you are migrating to a new model or just want to get better performance on your current model.
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For a full list of AWS blog posts, be sure to keep an eye on the AWS Blogs page.
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.
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