No Priors: Artificial Intelligence | Technology | Startups
00:33:30 · Tony Zhao
[00:33:30] S0: Yeah. It's actually really hard. And because it's, like, transparent objects, we need to also load it very precisely into the into the dishwasher. A lot of it is about how much force you apply. Mhmm. Because if you are trying to grasp two, in one hand, if you squeeze a little bit harder, you're going to break one of the glass. And when you load it into a dishwasher, if you're pushing it in the wrong direction and it hits something, it's going to shatter. We we did shatter a ton of glasses when we were, like, experimenting with it. So these are tasks that are, like, really high stake that is not just about recovering from mistakes, but about not making those mistakes in the first place. And this is what's generally the case in a lot of the home tasks, that you're just not allowed to make any mistakes. And then we get into the generalization demos, which we basically show our robot. We book, like, six Airbnbs, and we get it there, zero shots Mhmm. And see if it can do, like, part of the task. So two sides we use. One is I go around the table and collect all the utensils into the caddy. The other is to grasp a plate and then load it into the dishwasher. What makes these these demos very interesting is that we don't need any data when we enter that home. It's pure generalization. And this is as close to getting, like, a real product as you can get. Because when someone buy buy our home robot, we really don't want them to, like, collect a huge dataset themselves just to, like, unbox it. Also, in addition to visualization, those two tasks are also really precise. We're using the exact silver reverse in the home, and you need, like, basically, like, a few millimeter of precision where to grasp it properly. Those force are also hard to perceive because they're reflective. Like, the lights looks weird on it. We help have a transparent table home. Think I that the table looks like nothing, and the robot still, like, reacts very well to it. And, again, the reason that we can do it is because we have all these, like, more than 500 people, and we've seen so many glass tables Mhmm. In that dataset, so the robot is able to do it. I think the last bit of the task that we did is kinda pushing what's possible in terms of dexterity. The two tasks we chose, one is espresso... Operating espresso machine. The other is, like, folding socks.
00:35:44 · Cheng Chi, Tony Zhao, Sarah Guo
[00:35:44] S2: Mhmm. [00:35:45] S0: What makes these hard is that they require very fine grained force that is hard to get if you're doing with tally operation. Because these days, there's not a good tally operation system that can let you feel how much force the robot is feeling. Mhmm. So, basically, when you're tally operating, your hand is numb. Mhmm. And sometimes you are applying, like, a huge amount of force on the robot, but you don't know it. And that can result in, like, very low data quality that robot is also doing in that aggressive way that we really want to avoid for our system. The sock is a very good example that when you're trying to fold it, your two fingers can touch. Mhmm. And that forms a... What we call, like, a force closure. You have a closed loop for the force. And if your robot is stiff, you can apply infinite amount of force at it, and it doesn't look like anything. But for us, because we're using the glove to collect the data, the human who is collecting it can just naturally feel it. It's very intuitive. I think we're the first to do the sock folding and using end to end to do, like, espresso machine out of the whole industry. [00:36:53] S1: One of the things that you will also need to scale as you guys, you know, scale up the company is the team. What are you hiring for? What are you what are you looking for?
00:36:53 · Sarah Guo, Cheng Chi
[00:36:53] S1: One of the things that you will also need to scale as you guys, you know, scale up the company is the team. What are you hiring for? What are you what are you looking for? [00:37:03] S2: One thing I'm really looking forward is Thanks to me, Yeah. Yeah. So it's full stack roboticists and people who aspire to become full stack roboticists. Really, what we learn in this company is just that robotics is such a multidisciplinary field. You need to know a little bit of mechanical, a little bit of electrical, a little bit of code, a little bit data to actually fully optimize the system. And we have a couple examples of training just full stack software engineers to become roboticists, training engineers to become roboticists. And so if you want to learn about robotics, as you move on, you wanna learn the whole thing and not just to be boxing into your small, you know, little cubicle, let us know. [00:37:45] S1: Yeah. And you told me that you didn't write code until you got to college or something.
00:37:45 · Sarah Guo, Cheng Chi, Tony Zhao
[00:37:45] S1: Yeah. And you told me that you didn't write code until you got to college or something. [00:37:51] S2: Yeah. I was super enthusiastic about robotics, but I was mostly doing mechanical and electrical design before that. And then I realized, okay, the bottleneck is actually how the robot will move, and there's something called programming. And then the more I get into it, the deeper it gets. And then toward the end of college, I realized, okay, there's a thing called machine learning, and you figure out how to train models. I think these things just go on and on. I think it's very natural for me to gradually expand my skill set because I'm always looking forward to build a robot. [00:38:21] S1: Well, I hope you discover the next field because you're no longer doing dishes. [00:38:24] S0: It's a very fun place to work. Whatever you can imagine about robotics and consumer products and machine learning, you can find it here because we're just fundamentally such a full stack company. We're not just about the software. We're not just about the hardware, but we're about the whole experience, the whole product, and making sure that product is general and, like, scalable in the future. [00:38:47] S1: Awesome. Congratulations. [00:38:49] S0: It's really exciting. [00:38:58] S1: Find us on Twitter at No Priors Pod. Subscribe to our YouTube channel if you wanna see our faces. Follow the show on Apple Podcasts, Spotify, or wherever you listen. That way, you get a new episode every week. And sign up for emails or find transcripts for every episode at no-priors.com.