How this headline may connect to industries in Oregon. Technical scores are below — click any ? for what a metric means.
Will Robotics Have a ChatGPT Moment?.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.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.Data Is An Unsolved Challenge Large 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 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.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 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 tool