Robot data must be diverse to drive a capability flywheel

Transcript

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

1. 13:15

   You can imagine that the lab that is first to get to scale will be the first to break out. There may be this exponential growth effect where once you deploy, deployment helps you grow faster and growing faster helps you deploy and this flywheel. And so do you believe that that will happen in this industry where one lab, whether in U.S. or China, will hit some breakout point and they'll kind of jump everyone else?

2. 13:46

   There is a lot of truth to that. I think that there's an important detail to keep in mind. The detail is that you kind of have to scale the right thing. So I think that basically the statement that, like the way that I would phrase this is having an effective positive feedback loop where more deployed robots translates to more model capability, that's kind of the key. And that makes total sense. The trick is that there are lots of ways that it can be done wrong.

3. 14:23

   So I'll tell you a few obvious ones. One obvious one is, let's say that I'm a car company and I have a robotic arm that is welding cars. And it's there on the assembly line and it welds cars every day and it gets a million welds every month and so on. If I just use that as my data flywheel, I'm unlikely to get something more capable than a robot that welds cars. So since the robot is already there and it's already welding cars, presumably the marginal improvement for that is not all that valuable.

4. 14:55

   So that's an example of how you kind of have to scale the right thing. Because data is not quite as fungible. It's not like electricity or oil. You can't just buy more of it. It has to be heterogeneous. So I think that basically it's right, but you have to scale the right thing with the right technology and the right kind of source of diverse learning. Data is more like an education program for your robot than it is a fungible commodity.

5. 15:19

   If you think about that data flywheel and I guess putting out the proper platform that would generate data that matters that would then improve that platform, when do you foresee this kind of happening in the world? I think on the technology side, like things are advancing very rapidly towards that. And I think that maybe a particular balancing act to strike that would significantly determine that timeline is kind of how much structure somebody is willing to admit.

6. 15:47

   So, you know, on the one on the one extreme you could imagine like jumping straight to fully unstructured deployment domains like home robots And that could be really exciting because then you have a lot of diversity like right off the bat But the bar is a lot higher to be effective enough in that domain, to be safe enough. Safety is a much bigger issue there because it's around people in their home.

7. 16:09

   On the other extreme, you could imagine much more structured tasks, maybe not quite the welding robot, but sort of like, you know, maybe the robot down the hall that does something more unstructured, right? And that could be a much easier domain in the sense that there's much less safety concerns because it might be around trained humans. The task might be more predictable and so on. but there is the marginal value of each bit of data you get in that domain is lower because there's less variety. So it's like you could take off earlier but with a smaller slope or later but with a larger slope and you kind of have to like calibrate that. But my sense is that whichever end of that extreme we're talking about, it's in the single digit years rather than the double digit years at this point. So it could be that the more structured ones might be happening like

8. 16:53

   now or next year. The less structured ones might be a few more years out but probably not like