Robotics needs to prove it can improve through experience

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 17:04

   if you were to put down, I guess, somewhat of a roadmap, but not so concrete, but just milestones towards that North Star of robot in my home doing... Yeah, yeah, that's a really good question. So maybe to preface this answer, I think that there's one thing that is very important to say about robotics that is very easy to miss from kind of like, I guess, like the hype cycle and the demos that people put out, which is that the hard thing in robotics was always generalization.

2. 17:38

   But when somebody shows a demonstration of their system, you know, the demonstration alone usually doesn't make it clear what level of generalization is being shown. The highly acrobatic robot demos, for example, are really exciting to look at. But typically, if it's something that is like a little bit more staged, like sometimes it's literally on stage, if it's like a show, like, you know, that is obviously like rehearsed. And that's okay, because it's like literally just a show.

3. 18:04

   But it's not the same as doing a task every time reliably in any home. And the generalization piece often doesn't look that impressive when viewed in isolation, because generalization is sort of a property of many trials, not of one trial. So you might see the robot doing something fairly mundane and unimpressive, but what's exciting about it is that it's doing it with an object that it's never seen before in an environment that has never been tested in before. And that's actually harder than doing an acrobatic backflip that it's practiced millions of times. So with that said, my answer to your question is that the roadmap is all about both achieving better generalization and kind of that second order effect of having a mechanism to get more generalization as you generalize.

4. 18:53

   So one of the steps on that roadmap is to have a very concrete demonstration of a robotic system that gets better with autonomous experience that is collected in a setting that it wasn't originally trained for. So I do whatever I do on the back end, you know, in a lab and whatever. I get my model. I get my adaptation algorithm. I put it in a new setting. Maybe it's a home. Maybe it's a factory. Whatever it is, something where it's doing something real.

5. 19:22

   And it does OK. But then over time, it gets better and better. And it gets better and better to the point where it reaches sort of practically relevant levels of robustness without sort of capping out at like 50%. Like that, I think, would be a major milestone. Because now that that says okay if this is truly an automated process it's improving It's getting better even as it's collecting useful experience now I can take it and I can put it in lots of different domains Collect useful experience do something that people actually want and it'll improve the model. So that I think is a really major step on that road. I think another really major step is to demonstrate a very Concrete and practically useful way to transfer knowledge to transfer common sense to achieve robustness so that's like the

6. 20:08

   Kind of the the other scenario we don't get to practice if you're driving your car on the road And you see a fire truck and you see like a bunch of traffic cones Even if you've never been in that situation before like your common sense tells you like hey I should like slow down like whatever what maybe I don't know how to react optimally But I shouldn't just like barrel on through the cones and like you know Upset the firefighters and so on like so that's common sense and if you can apply that common sense to effectively recover from unexpected situations like when the robot put the spatula in the oven like you know it should probably open it up take it out like it knows that this is not the thing you do semantically that I think is like another important step because that tells us that we can use common sense to fix mistakes. I could imagine a more narrowly scoped robot