Sergey Levine, one of the world’s top robotics researchers and co-founder of Physical Intelligence, thinks we’re on the cusp of a “self-improvement flywheel” for general-purpose robots. His median estimate for when robots will be able to run households entirely autonomously? 2030.If Sergey’s right, the world 5 years from now will be an insanely different place than it is today. This conversation focuses on understanding how we get there: we dive into foundation models for robotics, and how we scale both the data and the hardware necessary to enable a full-blown robotics explosion.Watch on YouTube; listen on Apple Podcasts or Spotify.Sponsors* Labelbox provides high-quality robotics training data across a wide range of platforms and tasks. From simple object handling to complex workflows, Labelbox can get you the data you need to scale your robotics research. Learn more at labelbox.com/dwarkesh* Hudson River Trading uses cutting-edge ML and terabytes of historical market data to predict future prices. I got to try my hand at this fascinating prediction problem with help from one of HRT’s senior researchers. If you’re curious about how it all works, go to hudson-trading.com/dwarkesh* Gemini 2.5 Flash Image (aka nano banana) isn’t just for generating fun images — it’s also a powerful tool for restoring old photos and digitizing documents. Test it yourself in the Gemini App or in Google’s AI Studio: ai.studio/bananaTo sponsor a future episode, visit dwarkesh.com/advertise.Timestamps(00:00:00) – Timeline to widely deployed autonomous robots(00:22:12) – Why robotics will scale faster than self-driving cars(00:32:15) – How vision-language-action models work(00:50:26) – Improvements needed for brainlike efficiency(01:02:48) – Learning from simulation(01:14:08) – How much will robots speed up AI buildouts?(01:22:54) – If hardware’s the bottleneck, does China win by default? Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
Full Episode
Today, I'm chatting with Sergey Levin, who is a co-founder of Physical Intelligence, which is a robotics foundations model company, and also a professor at UC Berkeley, and just generally one of the world's leading researchers in robotics, RL, and AI. Sergey, thank you for coming on the podcast. SERGEY LEVIN Thank you, and thank you for the kind introduction. Let's talk about robotics.
So before I pepper you with questions, I'm wondering if you can give the audience a summary of where physical intelligence is at right now. You guys started a year ago. And what does the progress look like? What are you guys working on?
Yeah. So physical intelligence aims to build robotic foundation models. And that basically means general purpose models that could, in principle, control any robot to perform any task. We care about this because we see this as a very fundamental aspect of the AI problem. Like the robot is essentially encompassing all AI technology.
So if you can get a robot that's truly general, then you can do hopefully a large chunk of what people can do. And where we're at right now is I think we've kind of gotten to the point where we've built out a lot of the basics. And, you know, I think those basics actually are pretty cool. Like they work pretty well.
We can get a robot that will like fold laundry and that will go into a new home and like try to clean up the kitchen. But in my mind, what we're doing at Physical Intelligence right now is really the very, very early beginning. It's just like putting in place the basic building blocks on top of which we can then tackle all these like really tough problems.
And what's the year-by-year vision? So one year in, now I got a chance to watch some of the robots, and they can do pretty dexterous tasks, like folding a box using grippers. And it's like, I don't know, it's pretty hard to fold a box, even with my hands. If you had to go year-by-year until we get to the full robotics explosion, what is happening every single year?
What is the thing that needs to be unlocked, et cetera?
So there are a few things that we need to get right. I mean, dexterity obviously is one of them. And in the beginning, we really wanted to make sure that we understand whether the methods that we're developing have the ability to tackle like the kind of intricate tasks that people can do.
As you mentioned, like folding a box, folding different articles of laundry, cleaning up a table, making a coffee, that sort of thing. And that's like, that's good. Like that works. I think that the results we've been able to show are pretty cool. But again, the end goal of this is not to fold a nice t-shirt.
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