00:01:45 · Ty Morse
[00:01:45] S0: I I was thinking about, like, cars when you were talking. And with a car, if you're testing some car just yourself, you're gonna experience most of what it is to own a car just driving around. Whereas with, a humanoid robot, the number of, you know, random, super unusual behaviors that someone might have this robot in is, like, multiple orders of magnitude, more than a car. And so going from, like, zero to 50,000 cars is incredibly difficult. Going to 50,000 robots is, like, I think probably, like, one or two orders of magnitude more difficult. How do you kind of plan for that?
00:02:19 · Bernt Øivind Børnich
[00:02:19] S1: As I think there... There's pros and cons. Right? So a humanoid robot is a lot of parts. It's quite complicated, but it is not the same number of parts as a car. And I see a lot of companies kinda compare humanoid robots to cars. And I think if you do that, you should kinda, like, take a step back and think about what you've made because a car is, like, 50,000 parts. A well designed humanoid robot is probably about 1,000 parts, and a car is, like, 4,000 pounds. A well designed human robot is hopefully less than 70 if it's gonna be safe. So it is actually a simpler system in that sense. Although, like, the individual complexity and number of moving parts is larger. So you you get some on that side, but you're, of course, entirely correct also that, like, it's a way less constrained environment that you're deploying into. So you're gonna see a lot of... Lot more weird shit. That's just the way it's gonna be. I think a lot of it comes down to expectation management. Like, it's gonna be bumpy. It's it's not gonna be perfect. I really hope we can provide a very good customer service and ensure that whatever happens, we're treating you well, and we really figure this out, iterate on this, and make sure that you don't have to face the same problem repeatedly. But, of course, we also need to deploy into some quite structured, more, like, less variance type environments for parts of the volume and not have 50,000 robots into 50,000 different tasks. That that would not
00:03:51 · Ty Morse
[00:03:51] S0: be manageable. Some employees have the robots in their own home, and I think you're one of them that's just got this robot walking around this environment and, like, experiencing it. And you're kinda, like, a few years ahead of everyone else. The other thing too is you probably had multiple versions of your robot in your home, and so I'd like to know what it feels like on just some personal experience level to have the first evolution of that bot in your home environment and also how that's progressed and, like, how the trust has, you know, been built over time.
00:04:19 · Bernt Øivind Børnich
[00:04:19] S1: Yeah. Yeah. So it's actually kinda interesting because it's about three years now since I kinda had the first, like, let's call it pilot at home, where I actually had our previous robot, Eve, the wheeled humanoid, which is a bit, like, bigger and more heavy, but very capable at home. And it was a lot of fun, and we learned a lot. And it was also one of the main inspirations for why, like, we had to kinda speedrun getting to, like, the full biped to be able to do all these tasks around the home and and also in, like, other diverse environments. Legs are actually very useful also for manipulation. It was a hard lesson learned. But I think the evolution over time is surprisingly slow. Like, it takes a long time. Right? So... And and this is often because you find something that doesn't really work and you need some significant data on this, then you need to iterate on your hardware, and you need your hardware to kinda redeploy, and now you get new data. And we're not in that many homes, so, like, it takes time to kinda run that loop. But luckily, we started very early, so we... We've had a lot of time to to think about these things. But I think mainly to me, the kinda, like, important barrier that has to be crossed on the trust side is is safety. And that's something that we're still working really hard on, and we really have a big goal to kinda, like, prove out safety this year in a more formal manner. So I'm very excited about that, and hopefully, we can share some more about that towards the end of the year.
00:05:48 · Ty Morse
[00:05:48] S0: How do you kind of decide on... There may be, you know, a thousand tasks that the robot can do in, like, a lab environment, but then, like you said, kinda there's this safety bar that you wanna hit before you ship it to, you know, your kid is around it. How fast do you go from, you know, creating a new task to actually having that in the environment and, like, testing it in your own home?
00:06:08 · Bernt Øivind Børnich
[00:06:08] S1: So one of the beautiful things about the system is that since it's so close to a human in how it operates, it's actually pretty straightforward to also operate the robot as a human. So this concept of kinda like an expert in place or like an expert operator allows you to really test a lot of tasks way before you're able to automate them, which is very useful. Gives you kinda like the kinda customer impression, how is it to live with this, how how well capable is the hardware to do these tasks. So I think, like, largely, most of the things we've already tested it out. And really, the big thing now is how do you automate it? And here, we follow a very different approach than most of the players in the space. I'm a very big believer in we can solve the general problem. We don't need to go and, like, automate every single task individually. There's a lot of tasks, and there's a lot of complexity. And, also, there's a lot more variability than you kinda, like, would think it is. So it's kinda like deep rabbit hole, right, to solve every task. And I see humanoids almost more like this bridge between human data and human intelligence and the machines. We've designed robots that are so similar to us as possible, even in how... Not just how they move, but how they interact with the world, how stiff they are, how compliant they are, all these things. So that we can take all this data that exists of us and just video and everything else out there on the Internet and train models that allows our system to be very generally capable across essentially all tasks, but less capable than if you specialize right now. But it allows you to solve the full problem way sooner. And I think that's generally what's also needed for the home. So I'm very excited about that. The new 1X world model labs that we stood up, amazing team. And we're kicking off kinda like the first new big training runs now later this month, And I'm very excited to see how the new model's gonna turn out. It's a bit early yet, but, there's some very, very promising, promising results there. And I think we have a pretty good shot at solving the general problem.