No Priors: Artificial Intelligence | Technology | Startups
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?
00:30:07 · Sarah Guo, Tony Zhao
[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? [00:30:18] S0: This is a different answer for everyone. For me, like, I just really hate dishes. Like, in my sink, there's always, like, four or five dishes that are, like, somewhat dirty out there that kinda stinks a little bit. And after a long day of work, it really doesn't feel good to come out... Like, see a home like that. So I think the world we'll live in is It's gonna be cleaner. It's gonna be cleaner. And I was just thinking about it as, like, the marginal cost of labor in homes goes to zero. [00:30:47] S1: The last thing I wanna make sure we do is, like, talk about demos. Right? There's a lot of robotics launch videos today. It's been years since you saw an optimist serving drinks at a bar. Why are those not available, and what is actually hard? Yeah. [00:31:03] S0: I I think the way I will put it is make zero assumptions. No priors. [00:31:08] S1: Okay.
00:31:08 · Sarah Guo, Tony Zhao, Cheng Chi
[00:31:08] S1: Okay. [00:31:08] S0: As in Nice. If you see a robot handing one drink to one person, first, ask the question of, is that autonomous or is that tally operated? So this is the first thing. So we should look at the tweet and see what they say about it. And and then is that... Does it show giving another slightly different color cup to the same person or not? If they didn't show it, it means that a robot can literally only pick up that single cup and give it to that same person. When we look at demos, we tend to put our human instinct into it. They're like, oh, if you can hand a cup to that person, it must be able to hand a different cup to another person. Maybe you can also do more dishes. Maybe you can do a laundry. There are lot of, like, visual thinking that we can have about it, which is what's great about robotics that there are lot of imaginations. But I think when we look at demos, only index on things that is strong, and that's likely the full scope of that task. I think another aspect is... That is [00:32:07] S2: me as a review researcher. I I appreciate the number of interactions that happens in in in a demo. So, usually, the more interactions you have... Like, every interaction, there's a chance of failure. So the longer the sequence is, the harder it actually is. So that's something we really emphasize here. And that's actually somewhat uniquely easy for us because the GloVe way of data collection is so intuitive to people. [00:32:31] S0: Yeah. It's really about, like, generalization and reliability.
00:32:31 · Tony Zhao, Sarah Guo
[00:32:31] S0: Yeah. It's really about, like, generalization and reliability. [00:32:34] S1: So can you explain the demos that you guys are showing? [00:32:36] S0: Yeah. Of course. So we are showing, like, basically three categories of demos. The first one, as you saw, is we have this whole, like, messy table, and what the robot does is to clean up the whole table and, you know, dump the food into the food waste bin and load the dishes into a dishwasher and then operate the dishwasher. What makes this demo really hard is that it is a mix of really fine grained manipulation with these super long horizon full range task, as in, like, you need to go up and you need ask them to go down very much. It's a mobile manipulation task. Exactly. The reason that we can show this is just how nimble and easy for us to collect these datasets to make Verizon Dexter's demo possible. And it's also about the forces as well. So you might have seen, like, we're trying to pick up two wine glasses with one hand. [00:33:28] S1: Mhmm. I struggle with this, but... Yes.