00:08:15 · Ty Morse, Bernt Øivind Børnich
[00:08:15] S0: On this training run? [00:08:17] S1: No. Or... Okay. But getting to... It's kinda like a bootstrap problem. Right? Because you train a model that is very capable of essentially attempting all these tasks. And it does a good job of attempting them and sometimes succeeds, sometimes fails. And this gives you more data. And you kinda fold this into the training, and now you get more data. So now the model is even more capable, and then you kinda hill climb like this. But the really important thing here is to have models that are so capable that you don't need a human in the loop to generate the data. You just need a large fleet of deployed robots, and then you will get very capable data of both successes and failures. And you close the loop in the real world essentially to learn. Right? And then you need the capable model. You need a safe robot because if it's not safe, then it will harm itself or the environment where it's trying to do things it doesn't quite know how to do. And then you need a lot of deployed robots. So that's one more reason to see if we can get 50,000 of them out by the end of next year. [00:09:15] S0: On the kind of scaling the manufacturing side, what has the process been like going from the first one to, like, the first 10 to the first 100? And what have been the biggest bottlenecks along that journey?
00:09:25 · Bernt Øivind Børnich
[00:09:25] S1: So to some extent, we're we're a bit lucky in that, like, the system has been designed from day one for manufacturability. So even if you think about, like, this is a tech tree, and how do we choose to build robots, it's a bit different. So while you normally would have, like, kinda like normal motors, which aren't that strong, so now you need very complex gear systems. These gear systems introduces a lot of, like, energy and other artificial dynamics into the system that nature doesn't have. Now you need a lot of sensors and high speed control and all things to kind of mask this. You're kinda like fighting physics. We've taken a very different approach. So we have some very capable motors we develop in house and also manufacture that allows us to do these tendon drives. And they're drastically simpler, actually. So not just more performant, drastically simpler, like less parts, less complexity, less tolerances, less sensors, and all the things that can go wrong. So the system is quite manufacturable in that sense. Now the downside, of course, is that since this is a new type of system, there is no supply chain for this, so we have to manufacture all of it ourselves. And that's taken us a decade. Right? Like, the company is eleven years old now. So we we really had to kinda, like, climb the maturity ladder of these production processes. But now that that is getting quite mature, it's really a superpower because we do it all in house here. So whenever anything happens, we can just go back and iterate very fast and make sure we fix it upstream. So I think the most important metric we have right now is we use about four weeks from when do, like, major changes on the full system in CAD until a new robot walks off the line. And it took us a decade to kinda build that momentum where we can iterate so fast on the hardware. But that allows us to take the feedback from the line, you know, and feed that back into engineering and iterate in about four weeks. So then you can really, really, really kinda, like, tune in your manufacturing process. So you pick off any mistakes that's done in, like, assembly or anything that's hard to do with respect to quality, yield, calibration, all these things down the line. But there's there's no, like, secret sauce. You just need to, like, grind through all these problems.
00:11:25 · Bernt Øivind Børnich, Ty Morse
And in the end, you read across your fingers that you're gonna you're gonna run out of problems. If you just fix enough of them, you're gonna run out of problems. Do you still believe? Yeah. I still believe. But, like, since that's kinda the only way to do it, then you just need to be able to really benchmark yourself on, like, how quickly can I iterate? Because that's how quickly I can kinda, like, churn through these problems. And we see less and less problems. So there's... Seems to be going in the right direction. So sooner or later, we'll run out of the problems. [00:11:54] S0: When you're, like, starting to put these in people's homes that aren't inside of the company, I think the bot at the beginning of next year is going to look significantly different than the bot at the end of next year. And the feedback that you get from January customers are gonna be very diff... You know, you can incorporate that for the December people. How do you kind of, as quickly as possible, incorporate new information and new feedback from the robots in the field, and then basically spin up new manufacturing lines in order to scale that?
00:11:54 · Ty Morse, Bernt Øivind Børnich
[00:11:54] S0: When you're, like, starting to put these in people's homes that aren't inside of the company, I think the bot at the beginning of next year is going to look significantly different than the bot at the end of next year. And the feedback that you get from January customers are gonna be very diff... You know, you can incorporate that for the December people. How do you kind of, as quickly as possible, incorporate new information and new feedback from the robots in the field, and then basically spin up new manufacturing lines in order to scale that? [00:12:23] S1: There's a lot you can do now with just, like, fleet monitoring and data and analytics. That was very... Really harder to do before. So I think that that's a very important part of it. But there's nothing really that replaces the need to just, like, stay close to your customer and really listen to your customer and get that feedback. And then also, like, when we talk about iteration here, right, you you you iterate on the entire fleet. So we do have a big goal in the beginning, at least, for, like, the first units that we will do a lot of remanufacturing also. So, like, if you find things that we need to fix, then we fix it not just for new robots. We should also fix it for the existing robots. [00:13:00] S0: Would that be, like, literally, like, quote, unquote, recall recall back end?
00:13:00 · Ty Morse, Bernt Øivind Børnich
[00:13:00] S0: Would that be, like, literally, like, quote, unquote, recall recall back end? [00:13:05] S1: Well, but not necessarily recall. Right? Most of it is... Like, the the product is pretty small and light, so, like, generally, you can service it in the field. And, like, if you if you figure out that, like, we... You left feet on a robot. Right? We we we replace... Place the left field in the field. We we don't necessarily recall the robot, but, technically, you could call it a recall. [00:13:26] S0: I was looking at the hand video, and I was thinking to myself, this reminds me of this idea from Steve Jobs, where if you look at the inside of, like, an Apple computer, it's gorgeous, and people were kind of wondering to themselves, like, why is this? And it's like, because Steve wanted to design something that was both beautiful on the inside and the outside and also functional. And I think when I look at your robot, it's that... Has that same feel where it's, like, beautiful to look at even when it doesn't have its, like, clothes on or skin on. What's your kind of, like, philosophy on designing something where even the internals look good?
00:13:57 · Bernt Øivind Børnich, Ty Morse
[00:13:57] S1: I... I'm I'm I'm I'm probably, like, blunted here in, like, a special way because I think sufficiently advanced engineering is art, and this is clearly advanced engineering. But I think the beauty of the machine kinda comes from the simplicity, not the complexity. So you take something that's extremely complex, and you manage to boil it down to something is like, it is the simplest form, the simplest implementation that can fully solve the problem. And there's a real beauty to that. I think often you can see this in systems. You can kinda see that, like, oh, first principles wise. This is just, like, the minimum implementation of the system. And there's a real beauty to that simplicity, and that's really hope... What I hope we can deliver. That kinda comes from itself. Right? You don't need to design for it to be beautiful in inside. If you've done your job on, like, how do you design the system, then this just automatically happens because it should always be the minimum complexity implementation that solves the problem. That's how you achieve scale. [00:14:58] S0: Do you ever have a feeling where you're looking at something and you say to yourself, like, it is clearly not there yet because it doesn't look simple, it doesn't look beautiful?