IEEE Spectrum
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, VIENNARSS 2026: 13–17 July 2026, SYDNEYSummer School on Multi-Robot Systems: 29 July–4 August 2026, PRAGUEActuate 2026: 18–19 August 2026, SAN FRANCISCOEnjoy 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 $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 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.[ DEEP 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 $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 ]
IEEE Spectrum
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 builtOpen-source robotics software has been around since the mid-1990s, with early projects like Carnegie Mellon’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 at 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 roboticsComputer 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 about 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, Alibaba released RynnBrain earlier this year, 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 fieldThe 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 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.”
IEEE Spectrum
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 sideThe 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 loopWetour 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 RoboticsThe architecture: three layers, four engines, one loopOrchestra 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. Wetour RoboticsThe trade-offs we’re honest aboutNo 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 fieldThe 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.
IEEE Spectrum
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 $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 RealFor 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 ChallengeLarge Language Models 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 RoboticsGiving 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 AIWe 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 on-board 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 HardRobots 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” TasksThere’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 TimeAs 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.
IEEE Spectrum
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 RecoveryTraditional 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.TuurnyMinghui 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 StrategiesGhashghaei 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.”
IEEE Spectrum
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 LeeWhy 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.
IEEE Spectrum
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 harmIt’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 taintedColloquially, 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 autonomyWhen 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 frameworkIn 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.RAILet’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.Dull, Dirty, Dangerous: Understanding the Past, Present, and Future of a Key Motivation for Robotics, by Nozomi Nakajima, Pedro Reynolds-Cuéllar, Caitrin Lynch, and Kate Darling from the RAI Institute, was presented at the 21st ACM/IEEE International Conference on Human-Robot Interaction (HRI) in Edinburgh, Scotland.
IEEE Spectrum
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 learningsProvides an introduction to LLM-based AI AgentsDescribes an approach to applying LLM-based AI Agents to robotic teamsProvides demonstrations of the approach running in hardware with a heterogeneous team of robotsPresents lessons learned and future work in this areaDownload this free whitepaper now!
IEEE Spectrum
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, VIENNARSS 2026: 13–17 July 2026, SYDNEYSummer School on Multi-Robot Systems: 29 July–4 August 2026, PRAGUEActuate 2026: 18–19 August 2026, SAN FRANCISCOEnjoy 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 ]
IEEE Spectrum
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 UpgradesThe 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 RobotStretch 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 AutonomyHello 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 PilotsWhile 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 RobotPart 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 RobotEvans 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 RobotAnd 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 SaleHowever 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.