Y Combinator Startup Podcast
00:51:23 · Chelsea Finn
[00:51:23] S0: So the Pi0.7 model that I showed has something like this, where it can kind of imagine what a future image should look like and then try to accomplish that. We found that that leads to improvement, and we saw on the shirt folding example, we saw, like, a quantitative bump from using that sort of imagination compared to not using it. At the same time, I think that the model actually performed surprisingly well without that as well. And we're actually hoping... We're thinking about writing an entire paper, an entire technical report just about that capability in that model. But the the model without that was so good that we felt like we needed to actually have that play a bigger part of the story because it it... That seemed like that was really delivering, in terms of actually getting really strong results. And so, I it seems like one design choice. I think it's hard to say if it's going to be a critical component or not. I guess the good news with these kinds of models is that if you develop a good data set, you can run experiments and you can continue to test things with the data set that you have quite effectively. I also think that being able to predict the future seems like a very relevant objective compared to predicting future actions. So that should, I would imagine, help in terms of learning from all the data that you have available to you. Yeah, hard to say if this is going to be a critical component or not. It seems like empirically, so far, it seems to help, although perhaps not as much as you might expect. And even without that imagination, the robot can do pretty incredible things. Okay.
00:53:01 · S1, Chelsea Finn
[00:53:01] S1: Next is right now, it seems that robots are doing amazing tasks but in a very slow manner. What is needed to improve the speed? [00:53:08] S0: I'm really excited about improving the speed. The... And we we did see speed improvements from reinforcement learning. We also have another release, called the RL token that... Where we showed actually even faster speed and actually faster speed than human teleop. I think one of the bottlenecks is that when you teleoperate robots to do things, which is the easiest way to teach robot to do something, people are kind of slow at teleoperating the robot. We have a couple of projects in the pipeline that I think have really promising results in terms of getting fast policies. And so I think more to come there. Yeah, I think it's either you need to figure out how to make the data faster or you need to figure out how to be faster than the data. We've seen evidence of being able to be a little bit faster than the data. And in terms of the next steps, it's either to kind of go even further than that or make the data faster. [00:54:03] S1: Cool. What's the most surprising task you've seen a robot complete recently? What do you want to see it do next?
00:54:10 · Chelsea Finn
[00:54:10] S0: So the most surprising thing was not really a task, but when we were working on Pi0.7, personally trained one of the policies for some of the initial tests for assembling this pinwheel. And when I was working on training the... Training it to, to construct the pinwheel, one thing that really surprised me was, in all the data, we kind of carefully controlled controlled the strategy for how to assemble the pinwheel, where you, basically, will take the the kind of precut piece of paper and take a little pin and insert the pin into a hole in the paper. And in all of the data, we picked up the pin with the right hand and, picked up the paper with the left hand and inserted it. And the robot kinda started doing that. And then it actually made a mistake. And the paper ended up on the right side, and the pin ended up on the left side. And what the robot did is it picked up the paper, and it picked up the pin with its left gripper. And it put the pin with its left gripper and inserted it into the paper, with its right. And it had never seen data of inserting the pin with his left gripper. And it kind of showed that even that wasn't in the post training data at all, like, was only... Like, and it wasn't even in pretraining either. The robot essentially had learned this sort of equivariance between his left hand and his right hand so that it could actually transfer behaviors from one hand to another, despite the fact that that was never in the data. And so that was a really cool moment. I feel like I don't know if other people were as excited about it as I was when I shared it with some people, but it kind of shows this kind of emergent capability in these models that I hadn't seen before. And then, yeah, in terms of what I'd love to see, I don't know. I I love seeing robots do anything. And the... I guess, I think that there's still a lot of... A long way to push in terms of reliability for robots being able to do tasks for a really long periods of time. I don't necessarily think that much about individual tasks, but more so about capabilities and how to get the next capability from these models. The, yeah. And so I... Yeah. Anything, I guess the... Yeah. A robot doing anything always, gets me excited even if it's something that hasn't been done before. I guess one thing that we've been doing recently is, having robots, use knives to slice vegetables.
00:56:32 · Chelsea Finn, S1
I think that there's, a lot that you can do there once you actually can use knives safely, which is one thing that we've done recently. [00:56:40] S1: Okay. And then the last question, is how can someone break into robotics from a software engineering background? Great.
00:56:40 · S1, Chelsea Finn
[00:56:40] S1: Okay. And then the last question, is how can someone break into robotics from a software engineering background? Great. [00:56:47] S0: So I think that the... I guess first, there's a lot of software engineering in robotics. And so, there's... I think you could try joining us a robotics company as a software engineer. And then another thing that I would mention, I've actually seen someone take this path. So someone who actually now works at Physical Intelligence, her name is Jenny. She worked in trading, algorithmic trading for a while. Then she worked at Harvey and was doing legal stuff. And she was really excited about robots. And so she bought a cheap robot and basically in her bedroom played around with it and tried fine tuning an open source model and trying to get it to do something. And then she shared what she had done and she sent me a cold email and was like, hey, I'm interested in working in your lab. It And seemed like her profile was promising and that she actually, like, actually got out there and, like, tried it and done it and that she really was really excited about that. And so the... And now now she works at Physical Intelligence. The... But, yeah, I think just, like, getting your, getting your feet wet, trying stuff out, and learning from that experience, and then, yeah, using that experience to, kind of, yeah, share with people, have it on your resume and so forth, I think is a great way to do stuff. Fortunately, there's a lot of open source stuff out there that can allow you to get started on those kinds of things.
00:56:47 · Chelsea Finn, S1
[00:56:47] S0: So I think that the... I guess first, there's a lot of software engineering in robotics. And so, there's... I think you could try joining us a robotics company as a software engineer. And then another thing that I would mention, I've actually seen someone take this path. So someone who actually now works at Physical Intelligence, her name is Jenny. She worked in trading, algorithmic trading for a while. Then she worked at Harvey and was doing legal stuff. And she was really excited about robots. And so she bought a cheap robot and basically in her bedroom played around with it and tried fine tuning an open source model and trying to get it to do something. And then she shared what she had done and she sent me a cold email and was like, hey, I'm interested in working in your lab. It And seemed like her profile was promising and that she actually, like, actually got out there and, like, tried it and done it and that she really was really excited about that. And so the... And now now she works at Physical Intelligence. The... But, yeah, I think just, like, getting your, getting your feet wet, trying stuff out, and learning from that experience, and then, yeah, using that experience to, kind of, yeah, share with people, have it on your resume and so forth, I think is a great way to do stuff. Fortunately, there's a lot of open source stuff out there that can allow you to get started on those kinds of things. [00:58:12] S1: Great. That was the last question. Thanks everyone for listening.