No Priors: Artificial Intelligence | Technology | Startups
00:22:34 · Cheng Chi, Tony Zhao, Sarah Guo
[00:22:34] S2: Yeah. [00:22:35] S0: But sort of the behavior we want out of it is actually very difficult to model. Mhmm. Like, it's all these, like, reactive behaviors that when you feel like your leg is hitting something, you should, like, retract and, you know, step again. These are very, very hard to describe or try to learn from demonstrations directly. But in the case of manipulation, I think the difficulty is flipped. That it's a lot easier to capture the behavior itself, and it's a lot harder to simulate the world. Mhmm. For example, if you were to grasp a transparent cup with some orange juice in it, it's ridiculously hard to simulate how, like, your hand deforms around the cup and how all those ripplings, how those... Like, the color of the juice results in, like, the rendering and what the policy end up seeing. Simulating that is very expensive and difficult. But all we need to learn is just to, like, get your hand to be in front of the cup and then close with the appropriate amount of force, and that's actually very easy to learn. That's why, like, we see so many success of imitation learning in the case of robotics manipulation is because the behavior itself is actually not as hard as simulating the world, and that's why we see faster progress there. [00:23:58] S1: Is there anything that you have changed your point of view on in data over the last year?
00:23:58 · Sarah Guo, Cheng Chi, Tony Zhao
[00:23:58] S1: Is there anything that you have changed your point of view on in data over the last year? [00:24:03] S2: Like, it's one thing... I wouldn't say changed, but just data quality really matters. Like, I think we know... I always knew data quality matters, but once you scale it up, like, it it really matters. And and then... Because, you know, just like the diversity of of behavior that you experience in the wild is very hard to control, and the hardware failures are hard to control. You need to constantly monitor them. Just... You need to spend a lot of... Huge amount of engineering effort just to make sure that, you know, the data is clean. [00:24:32] S0: Yeah. And also building all those automatic processes. Right? We have our own way of calibrating the glove before we ship it out, and we have this whole, like, software system to catch if a... Something is broken on the glove, and we can detect it automatically. It's, like the importance of data quality kinda translates to all these repeatable processes, and we don't need a human to be staring at the data to know that something is wrong. [00:24:55] S1: When you described the beta for next year, a lot of it sounded like, we just wanna understand behavior, like how people actually want to use it. We can make some design decisions for the actual product. What technical challenges do you still see?
00:24:55 · Sarah Guo, Cheng Chi
[00:24:55] S1: When you described the beta for next year, a lot of it sounded like, we just wanna understand behavior, like how people actually want to use it. We can make some design decisions for the actual product. What technical challenges do you still see? [00:25:11] S2: So to me, I think there's two kind. The number one is really figuring out the training recipe at scale. We as a field just entered the realm of scaling, and we just got the amount of data that we need. I think now is a perfect time to start doing research and actually figure out what exact training recipe we need to actually get robust behaviors. And I think we're in a unique position because of the amount of data and the entire pipeline we built around data. The second point, I think, is just really hardware is hard. We are still pushing the bound... The envelope, performance envelope of hardware. It's not really clear actually what is needed, what is necessary for the hardware to be reliable, because whenever the mechanical team build a hardware, the learning team will try to... Try harder to push it against the boundary, and then it'll break at some point. But I think what's interesting in this company is that everybody's under the same roof. So immediately after something breaks, it goes straight back into mechanical design, and then we have another iteration, like I said, for the hand parts very quickly. Hardware is hard, but it is important. And I think it's a hard but right thing to do. And I think we as a field shouldn't avoid doing the hard things just because they're hard. Yeah.
00:26:23 · Tony Zhao
[00:26:23] S0: I want to echo Cheng's point about, like, first, the research. I think when there is data scarcity, it is really easy to come up with, like, cute fancy research ideas that doesn't end up scaling very well. And this is why, like, when we build a company, we actually focus on the infrastructure and a scalable data pipeline and operations before we start to, like, really dive into research, which we only started to do, like, three months ago. I think we really want to avoid doing research that doesn't scale, and we want to focus on things that contribute to the final product. The second point is, like, I think robotics is so intrinsically a, like, a system that right now, we don't... Like, there's not a existing general purpose home robot out there, and we don't really know what the interface of different system is. Like, what is even good? And in that case, if you're working with a partner, it's actually really hard for them to understand your standard of good because your standard of good is changing all the time. Mhmm. This is why we are, like, building everything in house in a more full stack approach that we build our own data collection device that is codesigned with a robot. We build our own, like, operation team to be like, how can we most efficiently get the most high quality data out? And, of course, our own AI training team that make the best use of these data. I think these are the things that are really not easy. It makes the company a lot harder, like, to build that right now you suddenly need, like, so many teams, and they need to orchestrate together. But we believe it is the right thing to do.
00:27:59 · Sarah Guo, Tony Zhao
[00:27:59] S1: Okay. I'm gonna ask you a few questions that are uncomfortable guesses now, but when will people be able to buy robots commercially from home? [00:28:06] S0: Like, this is something we're really excited about because we have so many of prototype robots in our office and really wanna get it out there. So the next step of our plan is to have a beta group program 2026. And what it means is that for people who sign up that we selected, they will have a real robot in their home, and it will start doing chores for you. And it's it's going to be a really interesting learning lesson for us because we will see, like, how human interact with the robots. We'll see, like, what kind of things people just really want the robot to do. I think this will be before we actually ship it to the masses because we just have an incredibly high standard of what we are willing to ship as a... For a consumer experience standpoint. We want the robot to be highly reliable, want it to be capable, want it to be cheap. I think it really depends on the results of the beta program that will decide when is a good time to ship it. Is it 2027? Is it 2028? And... But all of those are possible. [00:29:06] S1: But it's not a decade away. [00:29:08] S0: No. It's definitely not a decade away. [00:29:09] S1: How much do you think it could cost?
00:29:09 · Sarah Guo, Tony Zhao
[00:29:09] S1: How much do you think it could cost? [00:29:11] S0: Right now, the prototype robots we have in in house, I think the cost ranges from, like, $6,000 to something like $20,000. And this is actually pretty interesting that the big difference here is not like, oh, we find a better actuator. They're using the same actuators that are, like, very low cost. But, actually, it's the cladding of the robots. When you're trying to make them at low scale, it's just really expensive. Like, the cladding are, like, a few thousand dollars to make. But this is the type of things that as we scale up, it becomes, like, dirt cheap. Because instead of, like, doing CNC, instead of hand painting them, it'll become injection molding. What we see is that as we get the scale to a few thousand units, we can drastically reduce the material cost, likely under 10 k. And what it implies is that when we sell the robot, the price will be somewhere around it. [00:30:07] S1: Okay. So you fast forward two, three years out. If you look like five years and beyond, the home robots are ubiquitous. Like, what does life look like? How does it change for your average person?