No Priors Ep. 107 | With Physical Intelligence Co-Founder Chelsea Finn

Hi, listeners. Welcome to No Priors. This week, we're speaking to Chelsea Finn, co-founder of Physical Intelligence, a company bringing general purpose AI into the physical world. Chelsea co-founded Physical Intelligence alongside a team of leading researchers and minds in the field. She's an associate professor of computer science and electrical engineering at Stanford University, and prior to that, she worked at Google Brain and was at Berkeley. Chelsea's research focuses on how AI systems can acquire general purpose skills through interactions with the world. So, Chelsea, uh, thank you so much for joining us today on No Priors.

Yeah. Thanks for having me.

You've done a lot of, um, really important storied work, um, in robotics between your work at Google, at Stanford, et cetera, so I would, I would just love to hear a little bit firsthand your background in terms of your path in the world of robotics, what drew you to it initially, and some of the work that you've done.

Yeah. It's been a long road. At the beginning, I was really excited about the impact that robotics could have in the world, but at the same time, I was also really fascinated by this problem of developing perception and intelli-intelligence in machines, uh, and robots embody all of that. Uh, and also there's... Sometimes there's some cool math that you can do as well that makes... Keeps your brain active, makes you think, uh, and so I think all of that is really fun about working in the field. I started working more seriously in robotics more than 10 years ago at this point, uh, at the start of my PhD at Berkeley, and we were working on neural network control, uh, trying to train neural networks that map from image pixels to directly actually to motor torques on a robot arm. Uh, at the time, this was not very popular, uh, and we've come a long way, and it's-

Mm-hmm.

... a lot more accepted in robotics and also just generally something that a lot of people are excited about. Since that beginning point, it was very clear to me that we could train robots to do pretty cool things, but that getting the robot to do one of those things in many scenarios with many objects was a major, major challenge. So 10 years ago, we were training robots to, like, screw a cap onto a bottle and use a spatula to lift an object into a bowl and kind of do a tight insertion or hang up, um, like, a hanger on a clothes rack. Uh, and so pretty cool stuff, uh, but actually getting the robot to do that in many environments with many objects, that's where a big part of the challenge comes in. And, uh, I've been thinking about ways to make broader data sets, train on those broader data sets, and also different approaches for learning, whether it be reinforcement learning, video prediction, uh, imitation learning, uh, all, all those things. Uh, and so yeah. Moved from... Um, spent a year at, at Google Brain, uh, in between my PhD and joining Stanford. Uh, became a professor at Stanford. Started a lab there. Did a lot of work, um, along all these lines. Uh, and then recently started Physical Intelligence, uh, almost a year ago at this point, so I've been on leave from Stanford for that. And it's been really exciting to be able to try to execute on the vision that, uh, the co-founders, uh, that we collectively have and, um, and do it with a lot of resources and so forth. And, um, I'm also still advising students at Stanford as well.