No Priors: Artificial Intelligence | Technology | Startups
00:13:03 · Sarah Guo, Tony Zhao, Cheng Chi
[00:13:03] S1: Why is that the obvious answer it should just be, like, a full human arm? [00:13:07] S0: I think the core motivation for us is how can we build a useful robot as soon as possible. So whenever we see something that we can accelerate it with simplification, we'll go simplify that. So one example of that is the hand that we designed, which has three fingers. We kind of combine the three of the fingers that we have together. Mhmm. And the reasoning there is just that most of the time when we use those fingers, we use it together. Let it be, like, grasping a handle, let it be opening the dishwasher. So it really doesn't make sense to add the cost, like, multiply by three x to have separated into three when we can do one with most of the benefits. So this is how we think about the whole robot as well. It's kind of with the constraint that we're building a general purpose robot that can eventually do all your chores and will simplify everything we possibly can so that the robot can be as low cost and as easy to repair as possible. [00:14:03] S2: Yeah. I just wanna add a little bit more to the actuator and, like, mechanical design. Traditionally, most robots are designed for industrial use cases. Uh-huh. And the robot are very fast, they are very stiff, and they were precise. The reason is because all the industrial robots are blind, so they're blindly following a trajectory that's programmed by someone. [00:14:21] S1: It's not reaction to perception Correct.
00:14:21 · Sarah Guo, Cheng Chi
[00:14:21] S1: It's not reaction to perception Correct. [00:14:24] S2: But because of the breakthrough we had in AI, now a robot has eyes, so it can actually correct its own mechanical and hardware inaccuracies. So that kinda opened up a new different space of design. [00:14:37] S1: Intuitively, it should be like, I can't tell you exactly what the distance is that you're on a millimeter scale, but I'm gonna get to the cup because I could stop. [00:14:45] S2: Yeah. Exactly. So that allows us to use these low cost actuators that's cheap, that's compliant, but they're imprecise. But because of the AI's algorithms and systems we build, it allows us to build robots that's mechanically inherently safe and compliant while simultaneously be able to achieve the sufficient accuracy we need for the home tasks. [00:15:05] S1: Where are we in that timeline? You said we're between GPT and ChatGPT. And so, like, when do consumers get ChatGPT, and when will you guys ship something?
00:15:05 · Sarah Guo, Tony Zhao
[00:15:05] S1: Where are we in that timeline? You said we're between GPT and ChatGPT. And so, like, when do consumers get ChatGPT, and when will you guys ship something? [00:15:14] S0: Yeah. It's actually a really exciting time because, like, we have so many prototypes internally. What we will do next year, 2026, is actually start doing beta programs. We'll have these robots, all kinds of different ones, into people's home and see how they react to it. That will be when we learn the most about, like, how people... Like, do you want to talk to a robot? Do people want to have their robots maybe teach their kids some new knowledge about the world? And this will inform us what the eventual product should look like. Internally, we just have a extremely high standard of what is the minimal consumer product we want to ship. It needs to be extremely safe. It needs to be extremely capable and low cost. [00:15:58] S1: Do you feel like you know something now that you didn't when you started the company?
00:15:58 · Sarah Guo, Tony Zhao, Cheng Chi
[00:15:58] S1: Do you feel like you know something now that you didn't when you started the company? [00:16:02] S0: Absolutely. So I I think at the beginning, I'll describe it as, like, we see light at the end of the tunnel of... There are two axes. There's dexterity. There's generalization. When we add more data, things works better. And what this company about is the cross product of these two, how can we scale and have both dexterity and generalization. And this is something we're able to show in our generalization demo, which is that we can pick up these, like, very precise, like, forks, like, actual metallic forks, only ceramic plates with very high success rates. And, honestly, this is not something that we thought that would work so easily just by having so much more data. [00:16:42] S2: Yeah. So, actually, just wanna expand a little bit. You know, it's actually the process was long and painful. So there are so many issues, like just scaling up a robotic system is very, very hard. There are mechanical issues, reliability issues, there's data quality issues that come out of it. In the beginning, I actually thought it's gonna be much easier than this. But, really, just... It takes time and effort to grind out all the little details for this to work. I also think that if we compare TidyOp, it's much harder to get this system scaled up. But once it's scaled up, it's very powerful and very repeatable. [00:17:19] S1: So it is both harder than you thought it would be to get to here, and you are further than you thought you would be. Yes. Yeah.
00:17:19 · Sarah Guo, Tony Zhao, Cheng Chi
[00:17:19] S1: So it is both harder than you thought it would be to get to here, and you are further than you thought you would be. Yes. Yeah. [00:17:24] S0: And I remember in the beginning, we're having this, like, funny conversation of we're like, if we build this, someone can just, like, take our glove, and they'll build the same thing. Like, what mode do we have? Are we worried about that? And at the beginning, actually, we are a little bit worried because we thought, like, oh, you know, they can probably just replicate it. But as we go along the path, it turns out things are so much harder than we thought it was. [00:17:48] S2: There's so so many small demands. No. It's... Yeah. Yes. [00:17:51] S1: And when you say scaling up the robotic system, you mean the data collection to training pipeline and the hardware itself? [00:17:57] S2: Yeah. So actually, like, for for this... Yeah. For this to work at all, you need the data collection system. You need the robotic and control system to be able to deliver the hand to where we wanna go. You also need the data filtering pipeline and data cleaning pipeline and the training pipeline. And all these things need to be iterated together. So I actually gone through several loop of these. It's kinda hard to imagine without having a full stack team in house how this can even be done. [00:18:21] S0: Yeah. The glove we're using right now is... We call it, like, v five. [00:18:25] S2: Mhmm. [00:18:26] S0: And for v zero to v five, each version has, like, around 20 iterations. [00:18:30] S2: Okay. [00:18:31] S1: And So a 100.
00:18:31 · Sarah Guo, Tony Zhao
[00:18:31] S1: And So a 100. [00:18:33] S0: Yes. Yes. And, also, like, just when you make these at scale, right now, we have more than 500 people using these gloves in the wild. Like, all the things that could go wrong will go wrong. For example They they did. They did. Yes. For example, like, how things are assembled. If you don't specify exactly how it should be done, people will assemble it in creative ways. And the creativity doesn't help us here because, like, we really want the data collection device to be extremely precise. [00:19:02] S1: So you guys can't obviously know everything that's happening in every company in in academia and industry. But from what you know, how would you compare the scale of training data that you have today relative to the industry? [00:19:15] S0: At this point, we are almost 10,000,000 trajectories being collected in the wild. And those trajectories are not just like, oh, pick up a cup. It's these long trajectories with, like, walking, with navigation, and then, like, doing these long horizon tasks. [00:19:34] S1: Tony, as you mentioned, like, it's an open question, actually, what the right way to scale data up is. And so there are strong theories around teleop, around, like, pure RL, around video and world models. Like, how did you think about all of these?