Fully autonomous robots are much closer than you think – Sergey Levine
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Today, I'm chatting with Sergei Levin, who is a cofounder 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. Sergei, thank you for coming on the podcast.
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 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 get a robot that's truly general, then you can do, you know, 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 b
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 the box even with my hands. You gotta 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, etcetera?
So there are a few things that we need to get right. 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. Yeah. As you mentioned, like folding a box, folding different articles of laundry, cleaning up a table Yeah. Making a coffee, that sort of thing. And that's like, that's good, Like, that works. You know, I think that the results we've been able to show are pretty cool. But again, like, the end goal of this is n
So
it's it's it's ultimately, if if this stuff is successful, it should be a lot bigger. And it should have that ability to learn continuously. It should have the understanding of the physical world, the common sense, the ability to go in and pull in more information if it needs it. Like, if I ask you like, hey, tonight, like, you know, can you can you make me this type of salad? It's okay. You should like figure out what that entails. Like, look it up. Go and buy the ingredients. So there's a lot that goes into this. It requires common sense. It requires understanding that there are certain edge
So this grand vision, what year if you had to give a median estimate? Or 25 percentile, fifty, seventy five?
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