Going Direct Conversations
00:04:15 · Jordan Wolfe, Chris Paxton
[00:04:15] S2: space, getting kind of up to date. So I actually got my start in my PhD at Johns Hopkins University in Maryland. [00:04:26] S0: Initially, we were doing surgical industrial robotics. We did a bunch of work on how to teach robots to do different tasks and try to make them easier to use and all this. And this was all before deep learning really worked. So it's all stuff that it's ironic looking back at it now, because a lot of it doesn't like It ended up being quite different, I think, than the things that actually we use these days. But then after that, I went to... Actually, during that, I missed one, which is that I did a co-op at Zoox, so I went to Zoox for like a year and a half. So Zoox is a self-driving car company. So that was... But then I did post-doc. And was there for a while, moved to Pittsburgh. And then I moved to Pittsburgh, then I moved to Meta. And since then, I've been at startups after Meta, which is, yeah, so I've done, I've worked in a lot of different parts of robotics over the years, actually. I mean, yeah, many different robotics fields and industries. [00:05:24] S2: I don't know. Is there kind of an underlying theme with kind of your research or what you work on that you enjoy doing across the different robotic systems? Or is it kind of pretty wide ranging?
00:05:24 · Jordan Wolfe, Chris Paxton
[00:05:24] S2: I don't know. Is there kind of an underlying theme with kind of your research or what you work on that you enjoy doing across the different robotic systems? Or is it kind of pretty wide ranging? [00:05:35] S0: I think it's been wide-ranging, but my research area, the area that I've always been excited about is long horizon reasoning and planning for robots. How do we get robots to do multi-step tasks? How do we get them to reason and be able to interact with the world? One of the things I was most excited about last year was working on the work on Meta and the work that I did last year was around home robots and reasoning about how can they reason about and represent their environments to be able to make long horizon decisions. What that means in practice is me being able to tell my robot, hey, can you go find me my drink and have it go and search the house and look for it and pick it up and bring it to you, stuff like that. Because obviously this is actually still fairly challenging for robotics, that kind of stuff. And I think home robotics is something that I've always been very excited about, passionate about, which is not quick. [00:06:29] S2: And Mike, so Mike, over to you. So obviously, outside of RoboPapers, what's the day job or what do you do every day?
00:06:29 · Jordan Wolfe, Michael Cho
[00:06:29] S2: And Mike, so Mike, over to you. So obviously, outside of RoboPapers, what's the day job or what do you do every day? [00:06:40] S1: Yeah. So, yeah, I'm the co-founder of this project called BitRobot, where we're trying to use crypto incentives to incentivize, well, gather resources to push forward like embodied AI research. I don't know whether I lost you at the moment I say crypto, it just made me like 10 times less serious. But I genuinely believe you. [00:07:02] S2: Not at all, but go ahead.
00:07:04 · Michael Cho
[00:07:04] S1: Yeah. So how I started in this space was I literally was just with my younger brother, Sam, who's my co-founder. So during the pandemic, we were just like... On the weekend, we'll... So he's in China, I'm in Singapore at the time. And during the pandemic, I want to say it. So it's the end of the world. We don't know what we should do, but we want to have a reason to keep in contact. So every weekend, we'll come together, build our own sidewall robot. It honestly just started out as a weekend hacking project to keep in touch with my brother. And then we decided to start a company because our observation was that a lot of sidewalk robots that were built by these bigger startups and the Amazons of the world, their robots are pretty expensive, like $10,000 or more. Because at the time, we were assembling our own. I have a pretty good handle on the build of materials. And so we thought that if you take the business worldview, maybe we already have the recipe, all this offline RL invitation learning stuff, which I think four years ago is starting to show some signs of life. And if you look at these robots as real-world robotic data collection devices, like $10,000 robot will just not scale, like won't get you the data. So we thought, okay, very naively, to be honest, at that time, me and my brother have never done robotics as a real, we are not roboticists to begin with. We thought that maybe someone should build a toy that's like $500, that's like a science. sidewall robot toy basically and make we thought it can be built at that price by just making a dumb robot so yank out the big gpu very cheap cameras basically and just have it 100 like human control so no ai so just a dumb robot this is during the pandemic right so the other of the fact that we made was that There was this huge investment that was brought forward, let's say, a decade so that we can have video calls like this. Right. So because before that, the Internet wasn't really geared towards like real time communication, but because of that. So we thought, OK, maybe it's possible for someone in New York to control a robot toy somewhere else. And that could be a really interesting gamification. So honestly, it all started off like that. Everything along the way, you know, it's a stumble into it after the other.
00:09:25 · Michael Cho, Jordan Wolfe
I'll say why I got a lot more into this robotic research is from this robotic game. So think of it almost like a Pokemon Go where you have human gamers that drive around, like pick up virtual items on a map. We started gathering data from very early on, and it turns out that data was actually really useful for some of the folks that are doing urban navigation robotic research. And that's how we started working with some of these really top researchers. Like, for example, we co-organized for a couple of times now this competition called Earth Rover Challenge, where we pit the top human gamers in our game. versus AI models that are built by different labs, right? So, UT Austin, Seoul National University, National University of Singapore. And that one is actually co-organized together with many folks from DeepMind. And so, yeah, I mean, I thought that there's something there because I was such a small, tiny little project. to, to get the attention of this top researcher to spend time with me to organize competition like this. I thought there is something there. And so, yeah. So anyway, yeah. [00:10:32] S2: So essentially just like to summarize then, so that kind of the idea was game game, the gamification of basically tele-operation of, of robots to collect data to help in the training process. Is that kind of the best way to describe it?
00:10:32 · Jordan Wolfe, Michael Cho
[00:10:32] S2: So essentially just like to summarize then, so that kind of the idea was game game, the gamification of basically tele-operation of, of robots to collect data to help in the training process. Is that kind of the best way to describe it? [00:10:48] S1: Yeah, I, yeah, pretty much it. Yeah. I would say that's not the only thing that we do now, but that's definitely, we're still, it's, it's a big thesis of mine. I, I, I kind of feel that we're still a long way from having robots that actually work in homes, which is what, you know, Chris's dream. It's my dream too, but I think, you know, there's too many bottlenecks, but, uh, I think these robots are funny before they are useful. So I think like the current product market fit is actually doing entertainment with robots. Whereas we've seen, I actually also have a separate project that do this like humanoid fighting thing. Yeah. So those couple of things that you may have seen in San Francisco, like, uh, you know, I was kind of behind it for a number of these events. So yeah. Um, Yeah.