Marc Raibert: prediction

Transcript context

…So how big of a role does machine learning play in all of this? Is this more and more learning based? Since Chat GPT, which is a year ago, basically, there’s a huge interest in that and a huge optimism about it. I think that there’s a lot of things that that kind of machine learning, now of course there’s lots of different kinds of machine learning, I think there’s a lot of interest and optimism about it. The facts on the ground are that doing physical things with physical robots is a little bit different than language, and the tokens don’t exist. Pixel values aren’t like words. But I think that there’s a lot that can be done there. We have several people working on machine learning approaches. I don’t know if you know, but we opened an office in Zurich recently, and Marco Hutter, who’s one of the real leaders in reinforcement learning for robots, is the director of that office. He’s still half-time at ETH, the university there, where he has an unbelievably fantastic lab, and then he’s half-time leading, will be leading efforts in the Zurich office. So we have a healthy learning component. But there’s part of me that still says, if you look out in the world at what the most impressive performances are, they’re still pretty much, I hate to use the word traditional, but that’s what everybody’s calling it, traditional controls, like model predictive control. The Atlas performances that you’ve seen are mostly model predictive control. They’ve started to do some learning stuff that’s really incredible. I don’t know if it’s all been shown yet, but you’ll see it over time. And then Marco has done some great stuff and others. So especially for the athletic intelligence piece, the traditional approach seems to be the one that still performs the best.…

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