What does it actually mean to build a foundation model for robots?

In this episode of Eye on AI, Craig Smith sits down with Sergey Levine, co-founder of Physical Intelligence and professor at UC Berkeley, to explore a fundamentally different approach to building robots, one inspired not by programming a single perfect machine, but by training AI on the broadest and most diverse data possible so robots can learn, adapt, and operate in the unpredictable real world.

Sergey explains why the secret to general-purpose robots isn't perfecting one single machine, but training on massive, diverse data from all kinds of robots and even humans. The more variety the model sees, the better it gets. Just like ChatGPT learned from all the text on the internet, robotic foundation models learn from every robot that has ever moved, grabbed, or interacted with the real world.

We also get into the big humanoid robot debate. Are they the future, or is it mostly hype? Sergey gives an honest and technical take on why the form factor conversation is changing now that foundation models exist, and why that actually opens the door for more creativity, not less.

Finally, Sergey shares what he's most excited about next, building a true data flywheel where robots get smarter the more they are deployed, creating a continuous learning cycle that could change everything.

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(00:00) Introduction: What Are Foundation Models for Robots?

(01:44) Meet Sergey Levine: Physical Intelligence and UC Berkeley

(02:51) Breaking Down Foundation Models for Non-Technical People

(06:46) Why Real World Data Beats Simulation

(15:00) Building a Broad Robotics Foundation From Scratch

(24:00) The Open World Problem in Robotics

(40:00) Generalist vs Specialist Robots: Which Wins?

(47:00) Humanoid Robots: Real Innovation or Just Hype?

(55:10) The Future: Continuous Learning and the Data Flywheel

(56:23) Guilty Pleasure: Sci Fi and Thinking Beyond the Limits

#331 Sergey Levine: The Robot Revolution Nobody Is Talking About

Eye On A.I.