No Priors: Artificial Intelligence | Technology | Startups
00:09:04 · Tony Zhao, Cheng Chi, Sarah Guo
[00:09:04] S0: you know, the robot can serve you a drink. Yeah. I think that is the moment I was like, hey. Maybe we should start a company. This is actually working so well. I remember, like, just falling in tongue and But you have [00:09:16] S2: times that doesn't work well. [00:09:17] S0: Day. Yes. I think the only exception I saw was when it's under direct sunlight. Yeah. Right? And I think the reasoning was, like, over that whole, like, two, three weeks of data [00:09:26] S2: collection two weeks, it's all raining, so there's no sunlight data, so, like, it fails. That also demonstrates the importance of distribution matching. So in order for a robot to work in a sunny environment, it must have seen sunny environments during the training data. [00:09:39] S1: Yeah. It's really interesting because I remember when I first met you guys, it was like you spent like, I don't know, dollars 200,000 across all of your academic research. And yet the scale of data collection as translated to model capability is leading, right? So it's very interesting that, you know, we look at where we are, maybe going back to Tony's point of scaling and massive capital deployment. But that entire paradigm actually wasn't relevant before people realized, like, you should train on all of the internet data. And we just don't have that in robotics. So the entire field is just blocked on having any scale of data that's relevant.
00:09:39 · Sarah Guo, Tony Zhao
[00:09:39] S1: Yeah. It's really interesting because I remember when I first met you guys, it was like you spent like, I don't know, dollars 200,000 across all of your academic research. And yet the scale of data collection as translated to model capability is leading, right? So it's very interesting that, you know, we look at where we are, maybe going back to Tony's point of scaling and massive capital deployment. But that entire paradigm actually wasn't relevant before people realized, like, you should train on all of the internet data. And we just don't have that in robotics. So the entire field is just blocked on having any scale of data that's relevant. [00:10:16] S0: Yeah. I think these days, you're still, like, assuming debates about, like, what is even the right way to scale. There are, like, world models. There are simulations. There is teleoperation. There are, like, all these new ideas. And I think this is the the sort of area that we really want to innovate, that we want to differentiate, that we want to find out something that is both high quality and scalable. [00:10:38] S1: And then you guys... You decided to start a company pushing this cart around Stanford. Tell me about that decision, and congratulations on the launch and and sort of the the direction and team you've built.
00:10:38 · Sarah Guo, Tony Zhao
[00:10:38] S1: And then you guys... You decided to start a company pushing this cart around Stanford. Tell me about that decision, and congratulations on the launch and and sort of the the direction and team you've built. [00:10:48] S0: Yeah. It's a it's a very interesting journey. I remember in the beginning, it's actually two of us in Cheng's apartment. On his desk, we were, like, clamped a robot there and tried to do some tasks, and it soon becomes, like, I think, an eight person team towards the end of twenty twenty four. And now we're at around, like, 30 to 40 people. We're not the best at everything. Right? But starting a company allows us to find people who we really love working with and then bring all the expertise together from mechanical engineering, supply chain, like, software engineering, like, controls, and to build a system together that is not like a demo, but a real product. [00:11:27] S1: You've built this amazing team. What are people actually signing up for? What's the mission of Sunday? [00:11:31] S0: Yes. It is to put a home robot in everyone's home. I think there are a lot of AIs trying to make you more efficient during the work, but there is not enough AI that actually helps you with all these mundane things that are not creative, that really has nothing to do with what's making us more intensely human. What's ideal for people to spend more time on is actually with their hobbies, with their passions, as opposed to spending more time doing chores.
00:11:31 · Tony Zhao, Sarah Guo
[00:11:31] S0: Yes. It is to put a home robot in everyone's home. I think there are a lot of AIs trying to make you more efficient during the work, but there is not enough AI that actually helps you with all these mundane things that are not creative, that really has nothing to do with what's making us more intensely human. What's ideal for people to spend more time on is actually with their hobbies, with their passions, as opposed to spending more time doing chores. [00:11:56] S1: So if you guys are going from these amazing research breakthroughs to we're actually gonna ship a home robot, that's a product you have to talk about. Cost and capability and robustness. Like, what's the design philosophy? [00:12:11] S0: As these AI models becomes more capable and as hardware cost continues to go down, the home robots or all kinds of robots will be everywhere. So if we start from the most surface level, which is design of the robots, when we design it, we think about what should the robot look like if it is ubiquitous. You need to see it every single day. What should it look like? And what we end up with is that we really think the robot should have a face. It should have a cute face, and it should be very friendly. So instead of, like, a terminator doing your dishes, we want the robot to feel like it's out of a cartoon movie. And then the huge decision is, like, how many arms should a robot have? Should it have, like, four arms? Should it have, like, one arms? Should it have legs? Should it have, like, five fingers, two fingers, three fingers? It's a huge space.
00:12:11 · Tony Zhao, Sarah Guo
[00:12:11] S0: As these AI models becomes more capable and as hardware cost continues to go down, the home robots or all kinds of robots will be everywhere. So if we start from the most surface level, which is design of the robots, when we design it, we think about what should the robot look like if it is ubiquitous. You need to see it every single day. What should it look like? And what we end up with is that we really think the robot should have a face. It should have a cute face, and it should be very friendly. So instead of, like, a terminator doing your dishes, we want the robot to feel like it's out of a cartoon movie. And then the huge decision is, like, how many arms should a robot have? Should it have, like, four arms? Should it have, like, one arms? Should it have legs? Should it have, like, five fingers, two fingers, three fingers? It's a huge space. [00:13:03] S1: Why is that the obvious answer it should just be, like, a full human arm?
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.