Russ Tedrake to unveil stealth AI startup at Robotics Summit

Russ Tedrake has spent years teaching robots how to fall without breaking. Now he wants to teach them how to behave.

The MIT professor and former senior vice president of Large Behavior Models at the Toyota Research Institute is set to unveil a stealth startup focused on Physical AI at the Robotics Summit in Boston on May 28, 2026. His keynote, scheduled for 9:05 a.m. to 9:50 a.m., marks one of the more anticipated cameos in a robotics field that hasnt stopped raising money or making promises.

The event page confirms what Tedrake hasnt quite said aloud: hes the founder of a stealth startup in Physical AI, according to his MIT page. The title of the talk is still fairly vague — but the speakers biography is not. Tedrake led Team MITs entry in the DARPA Robotics Challenge, holds the Toyota Professorship of Electrical Engineering and Computer Science, Aeronautics and Astronautics, and Mechanical Engineering at MIT, and directs the Center for Robotics at MITs Computer Science and Artificial Intelligence Laboratory. He was, until recently, running the LBM program at TRI.

Large Behavior Models are, in the most charitable framing, the next act of what large language models did for text. Where an LLM learns to predict the next word across a corpus of human writing, an LBM learns to predict the next action across a corpus of robot behavior. The idea is that a robot trained on thousands of tasks in simulation and reality should be able to pick up a new one with a fraction of the data it would need if it learned from scratch. The paper is still a preprint — posted to arXiv in July 2025 with more than 100 authors from the Toyota Research Institute — but the results were notable enough to land Tedrake a conference keynote. We find that multi-task pretraining makes the policies more successful and robust and enables teaching complex new tasks more quickly using a fraction of the data when compared to single-task baselines, the paper reads.

That framing — more successful, more robust, fraction of the data — is exactly the promise the robotics world has been chasing since the deep learning revolution. And right now, the money believes it.

Physical Intelligence, a San Francisco startup building what cofounder Sergey Levine calls a kind of ChatGPT for robots, raised 600 million in November 2025 at a 5.6 billion valuation in a round led by CapitalG, Googles independent growth fund, with participation from Jeff Bezos and Thrive Capital, according to Bloomberg. The company has raised over 1 billion total and employs roughly 80 people. Skild AI, another robotics software company, closed close to 1.4 billion in January 2026 at a valuation north of 14 billion in a round led by SoftBank with backing from NVentures (NVIDIAs investment arm), Macquarie Capital, and Bezos Expeditions. The field is not short of capital or ambition.

What Tedrake brings that the others dont is a specific pedigree around manipulation and contact-rich environments — the stuff that makes robots hard. The DARPA Robotics Challenge was about getting robots to do useful things in disaster zones, which is a very different problem from making a chatbot summarize a paragraph. A professor who has thought hard about how to make dextrous manipulation actually work, and who spent years at one of the more serious industrial research labs in the world, is not the same as a team that trained a vision-language model on robot data.

The competitive pressure, though, is real. If LBMs work at scale, the company that owns the model owns the robot OS. Thats the prize. Its also why a room full of well-funded startups and a MIT professor with a new company is worth watching — not because stealth reveals are inherently newsworthy, but because the underlying question of whether behavior learning can follow the same scaling curve as language learning is one of the most consequential open problems in robotics.

The keynote is May 28 in Boston. What he actually shows — a demo, a product name, a funding number, or just a vision — will tell you how close this is to shipping. The research is real. The money is real. The timeline remains the eternal unknown.