Manufacturing 50,000 Humanoid Robots Next Year | Bernt Børnich, 1X

0:00Bernt Børnich:Today I am sitting down with Bernt Børnich, the founder and CEO of OneX. You want to ship 50 ,000 humanoid robots next year. So can we start off with you just walking me through how do you actually go from just R &D to actually shipping that number of robots in 12 months? Yeah, so I mean the factory we're in now in Hayward is built out to a capacity fully ramped about 10 ,000 units. So that's really been like the goal of this year. We're not going to do 10 ,000 because we're going to be pretty much towards the end of the year when the line is fully ramped. But in parallel, we're building out San Carlos.

0:33So to make this happen, we have to do some parallel building. And that's going to be 100 ,000 units at full capacity per year. So 110k per year, but of course, most of that volume is going to come online pretty late next year. The real gating item here is really not just shipping 50 ,000 units, but ensuring that you don't get them back. right so we we have to kind of scale our volume at a rational rate prototypes are easy like it's a cliche but it's true prototypes are easy production is really hard we've churned through a lot of issues here to kind of get to where we're confident in ramping volume but whenever you take like the next big step on the volume you always find things that you don't find on the previous one so like it's just statistics right when you do a hundred units you don't find the same things that you find when you do a thousand or ten thousand because things that are rare just gets statistically significant and like your yield really needs to come up so we hope to find a good balance where we take the time to really iterate on the product and like get the lessons learned and get that back into the fleet and then go into larger and larger volumes as we kind of cross these gates quality gates but the goal is 50 000 so it's gonna be a lot of work i was thinking about like cars when

1:47Bernt Børnich: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 like 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 fifty thousand cars is incredibly difficult going to fifty thousand robots is like i think probably like one or two orders of magnitude more difficult. How do you kind of plan for that? Yes, I think there's pros and cons, right? So a humanoid robot is a lot of parts.

2:25It's quite complicated, but it is not the same number of parts as a car. And I see a lot of companies kind of compare humanoid robots to cars. And I think if you do that, you should kind of 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 humanoid robot is hopefully less than 70 if it's going to be safe so it is actually a simpler system in that sense although the individual complexity and the number of moving parts is larger so you get some on that side but you're of course entirely correct also that it's a way less constrained environment that you're deploying into so you're going to see a lot more weird shit that's just the way it's going to be I think a lot of it comes down to expectation management.

3:12Like, it's going to be bumpy. It's not going to 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 be manageable.

3:51Bernt Børnich: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 kind of 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 yeah yeah so it's actually kind of interesting because it's about three years now since i kind of had the first like uh let's call it pilot at home where i actually had our previous robot eve the wheeled humanoid uh which is a bit like bigger and more heavy, but very capable at home.

4:34And it was a lot of fun. And we learned a lot. And it was also one of the main inspirations for why we had to kind of speedrun getting to the full biped to be able to do all these tasks around the home and also in other diverse environments. Legs are actually very useful also for manipulation. It was a hard lesson, Lord. 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 kind of redeploy and now you get new data and we're not in that many homes so like it takes time to kind of run that loop but luckily we started very early so we've had a lot of time to to think about these things But I think mainly to me the kind of like important barrier that has to be crossed on the trust side is safety.

5:35And that's something that we're still working really hard on. And we really have a big goal to kind of 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.

5:48Bernt Børnich: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, there's this safety bar that you want to hit before you ship it to your kid is around it. How fast do you go from creating a new task to actually having that in the environment and testing it in your own home? So one of the beautiful things about this 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 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 um gives you kind of like the kind of customer impression how is it to live with this how um 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 and 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 kind of like would think it is so it's kind of 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.

7:45and 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 kind of like the first new big training runs now later this month and I'm very excited to see how the new model is gonna turn out it's a bit early yet but there's some very very promising results there. And I think we have a pretty good shot at solving the general problem. On this training run? No. Okay. But getting to, it's kind of like a bootstrap problem, right? Because you train a model that is very capable of essentially attempting all these tasks.

8:29And it does a good job of attempting them. And sometimes succeeds, sometimes fails. And this gives you more data. And you kind of fold this into the training and now you get more data. so now the model is even more capable and then you kind of 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 when it's trying to do things it doesn't quite know how to do.

9:07And 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.

9:14Bernt Børnich:On the kind of scaling the manufacturing side, what has the process been like going from the first one to the first 10 to the first 100? And what have been the biggest bottlenecks along that journey? So to some extent, we're a bit lucky. And the system has been designed from day one for manufacturability. So even if you think about this as a tech tree, and how do we choose to build robots it's a bit different so while you normally would have like kind of 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 so now you need a lot of sensors and high speed control and all things to kind of mask it so you're kind of like fighting physics we've taken a very different approach so So we have some very capable motors we develop in-house and also manufacture that allows us to do these tendon drives.

10:06And they're drastically simpler, actually. So not just more performant, but drastically simpler, like less parts, less complexity, less tolerances, less sensors, and all things are going to 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 11 years old now. So we really had to kind of 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.

10:40So 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 we 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 kind of 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 kind of like tune in your manufacturing process.

11:15So 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 no like secret sauce. You just need to grind through all these problems. And in the end, you really cross your fingers that you're going to run out of problems. If you just fix enough of them, you're going to run out of problems. Do you still believe? Yeah, I still believe. But since that's kind of the only way to do it, then you just need to be able to really benchmark yourself on how quickly can I iterate? Because that's how quickly I can kind of turn through these problems.

11:46And we see less and less problems. So there seems to be going in the right direction. So sooner or later, we'll run out of problems.

11:54Bernt Børnich: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 going to be very different 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 there's a lot you can do now with just like fleet monitoring and data and analytics that was very very hard to do before um 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 uh and then also like when we talk about iteration here right you you iterate on the entire fleet so uh 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.

12:53So 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.

13:00Bernt Børnich:Would that be literally, quote unquote, recall back end? Well, not necessarily recall. Most of it is, the product is pretty small and light. So generally, you can service it in the field. And if you figure out that we need new left feed on a robot, right? We replace the left field in the field. We don't necessarily recall the robot, but technically you could call it a recall. 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?

13:40Bernt Børnich:And it's like, because Steve wanted to design something that was both beautiful on the inside of the outside and also functional. And I think when I look at your robot, it 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 i i'm i'm i'm probably like blunted here in like a special way because i think uh sufficiently advanced engineering is art and this is clearly advanced engineering um but i think the beauty of the machine kind of 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 kind of 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 what i hope we can deliver um that kind of comes from itself right you don't need to design for it to be beautiful inside.

14:44If you've done your job on how do you design the system, then this just automatically happens, because it should always be the minimum complexity implementation that solves the problem.

14:56Bernt Børnich:That's how you achieve scale. Do you ever have a feeling where you're looking at something and you say to yourself, it is clearly not there yet, because it doesn't look simple, it doesn't look beautiful? Oh, yeah. And every day, every time I look at the robot, that all I see is everything that's wrong. So I'm not that lucky in that sense. Like everyone else sees something as like, this is beautiful. I'm like, no, no, no, no, like this is wrong, this is wrong, this is wrong. And I think that's probably going to be like that forever. So I'm not the right person to ask that. But I do think, like, I find beauty in every time we can do simplifications.

15:28And I think the biggest simplification that's happened over the last year is, for example, wire harnessing and just ensuring like the complexity of the cabling really going down down tremendously uh like minimum number of connectors minimum number of wires and this is just something we drive top down like we literally have like a cable and connector budget for example there's a certain number of connectors that you have to have from like a first principles point of view but it's not that many and every connector that you have more than that that's an exception that really needs to be defended and needs to be approved because it shouldn't be there and we should have a plan for how to get rid of it and this is the same for moving parts right like you want the number of parts in your robot to be the number of moving parts if two parts don't move with respect to each other like why are there two parts like they should be one part you can't always achieve this but you should absolutely know what is the first principles minimum complexity and how far away are you and how are you driving it down towards that because the ultimate goals should be to reach that.

16:36Bernt Børnich:Has there been any specific example where there was like a very obvious this should just be one part you can think of? This is very niche. But my favorite is actually how the rotational part and shaft on the motor is now one part less than what it's always been because some very smart people figured out how to do the bearing assembly of the shaft without needing two parts, which usually it's always two parts because you kind of like need to sandwich it in, but it's actually one part. To me, that's very beautiful. It's half the number of parts for that. If you took Berndt from five years ago and you teleported him to today or teleported yourself back, would you make any decisions drastically differently than you have?

17:16I'd keep the team way smaller and leaner way longer. I think a lesson you learn again and again and again as you do development is that the beautiful products are the simple products and the beautiful simple products are made by very small, extremely talented teams. the more people you have the more complexity you will get in your product and this type of engineering just does not scale with a number of people like you can scale your manufacturing with having lots of people doing manufacturing processes testing automation all these things but like the core product design once you have multiple people you need interfaces and once you get interfaces you kind of introduce artificial complexity there's a real very material advantage to how much of the system can you have in your head and understand so that you as a single person can make the decisions across the full system.

18:07Usually I would say across the subsystem, but ideally there are no subsystems. It's just the system, right? Now, this is not doable for this complex of a product, but you want to get as close to that as possible.

18:18Bernt Børnich:When you are trying to keep the entire idea of this thousand-part product in your head, how do you kind of come up to speed as fast as possible and have the most accurate representation of what this thing is in your head at any given time? I think it just comes down to, like, have you understood the fundamental principles of how the machine operates and why? And if you really understand the fundamentals, then everything else is just logical, like if you have a good design. So then the complexity kind of largely goes away. And then you look at it in the end, and you're like, actually, how do we spend this much time?

18:55Like, this isn't that complicated. and like that should really be the goal right and maybe other people look at it and they're like oh man this is complex but once you really know how it works then the magic goes away right and it's just like this pretty simple machine i think that takes a lot of time so you also need to create an environment where kind of people stick around and get that intuition and under deep understanding of the system but you can also do a lot on the tooling side which is quite important like how do build the design tools on the electromechanical side that allows you to iterate on the product in a way that's like verifiably correct and this is increasingly becoming more and more productive because you have so good tools especially on the coding side but that's always been a challenge like finding people who are very deep on electromechanical but are still general enough that they can kind of develop their own tools to solve the problem.

19:51Because most people that are very deep on the electromechanical side, they're not necessarily software people. But by far the most valuable people are the people who are very broad across all of these domains, preferably even down to like material science and everything else, right? Like how many domains have you actually mastered? And can you start to see kind of like the correlation between them? So kind of like the boundaries disappear. The best people generally end up building a lot of tools for how to do your job.

20:14Bernt Børnich:if long-term millions of people are going to have these things in their homes ideally this is something that humans like form a connection with and they start to love one of the videos that i saw you were like hugging neo and i was thinking to myself how do you go through the process of trying to design something that is fundamentally something that humans can learn to love or decide to love yeah so there's there's the obvious answer which is like oh you do the write things with respect to like warmth and materials and it's easy to design things that are scary and then they look kind of science fiction and modern because they're scary like that's kind of like the simple cheat code we don't do that we try to design things with warmth like technology that just blends into society and you shouldn't even think about it as technology like that would be the success so that's kind of like on the hardware side um and that's hard to do but i think it's pretty straightforward um i think on the intelligence side it's incredibly interesting and one of the things i'm so bullish on with the world models and what we're doing there is the robot actually learns social behaviors also through all this data and a good example is just like if you get the robot to like i mean there's some bags over there and we have the robot to go and like give you a goodie bag of swag um it hands it to you and it doesn't let go right and then you grab the handle and then the robot lets go and there's there's no data that in the training there right the robot doesn't have data of the specific tasks it's just these kind of like social behaviors and how to interact with people just falls out of the model body language falls out of the model like everything kind of collapses into one big omni model that understands everything from how to do the task but also how to like how to be social um how to simulate older agents like older people like if you want to move around and do things in an efficient manner then you need to understand and predict what other people do right and we do this all the time and i think that part of the intelligence is very very interesting but also very very hard to get right i think ultimately that will matter a lot right because that's how you create the trust that's how you kind of like create that warmth and I think it's also going to be quite individual like people don't like it's personalizable like people don't necessarily want the exact same thing but I do think that these machines will they will learn and last for a long time and they will become a part of the family and a part of society and it's going to be very interesting to see see how that evolves I think it's going to be quite beautiful to have like this companion throughout your life that is always on your side, remembers everything that's happened, and it's always there for you.

23:00And I think we could all need that.

23:03Bernt Børnich:I think there's very few products that most people would describe as delightful to have in their lives. And I think this is going to be one of them, where when people think of the experience of interacting with humanoid robots and Neos, it's going to feel like delight. Like things just are happening. you didn't really tell it to do something just like magically did some tasks that you weren't expecting and you have this little surprise and joy how do you think about designing something that is just going to be like a delightful product i think ultimately it's all in the data right so it becomes a data problem of how do you kind of like create the desired behaviors through how you tune your data i think there's still a lot of unsolved the research there just like a lot of work to do i don't have the solution like everyone else were working on it but i think it's something that will really really really greatly improve the enjoyability of using the models and it's been a lot of discussion lately right when you look at the best llms now they're kind of becoming worse personality wise because you have to be so careful to make sure they don't do anything wrong.

24:16And we're clearly going to have the same problem here. Like, we want to make sure these robots are caretakers of humanity and, like, they help us. And finding the balance there, I think, is going to be very hard.

24:31So, yeah, that's something we're going to work a lot on. And I do hope that through kind of exposing the robot the right kind of like behaviors that we would like to see in other people uh this is emergent

24:45Bernt Børnich:behaviors it actually just occurred to me um if you have a robot in a home environment it doesn't not all homes are actually happy um and there might be learned behaviors that are not good well you know remember like most of like the llms are mainly like trained on i don't know like discord forums ready right like worse forums than that like it's like all over humanity all over the place right so like it's like it's like the dark side of humanity um and then you take that and you know there's this like great meme right where you have like the big cthulhu monster and then someone like slaps a smiley on it and says like now it's ready uh and that's kind of like what a modern model is right you have this like beast and then you're just like no no you're going to behave well um and i think there's better ways to do that uh long term as you get more data Right now, everyone is extremely data constrained.

25:35So are we, right? But as you get this incredible tool to learn about how to operate in our world, maybe you can be more selective on your data. I don't know. But maybe we just encourage everyone to like, be kind to our robots and your robots will be kind to you. But there is a balance here because the willingness to kind of like break rules and explore is a large part of how we learn, right? It's how you get new ideas and creativity. And maybe that's one of the reasons you don't see that much creativity in models today. You don't really kind of encourage and expose that kind of behavior.

26:08Bernt Børnich:How do you expose more of it? Like if you were to try to drive the robots to do creative things, how do you like seed that? I think play is just incredibly important. And I dream of a future where like robots kind of like curious and playful. And if they don't have anything else to do, they'll go and figure out how something works. I don't know, go shuffle your feet in the sand and figure out how the dynamics of sand works. I don't know. It gets, of course, it's pure speculation. But I think the ability to kind of like verify your hypothesis because you literally have the real world here grounding things.

26:48Hopefully it will give you some more ability to explore because you can actually figure out whether or not your hypothesis result in what you thought it would.

26:55Bernt Børnich:When we were on the drive over here, I was watching this kid just randomly. we were stopped at a stoplight and this kid was just feeling around and like touching the world I think it was like some handrail and they were touching it and they just feeling it around and then it just occurred to me there's all the tasks that you want your robot to do in your home maybe you want it to do the laundry make you coffee all this stuff but then probably most of the time if you're at work for eight hours this thing is just there sitting idle but you could potentially like have it collecting data by taking actions and just literally like a child experiencing the world you know experiencing the home doing things and i think also like we haven't talked about this yet but you know there's the concept of the neo platform where we open this up to developers and everyone to like build on it which i think is going to be incredibly important because it's such a big problem um and i don't think we can solve it alone uh we really need to enable as many people as possible to do the development and like partake in the journey and these robots will get exposed to very different environments and very different data and i think that's also very important because you want like as wide a distribution as possible if you think about the home i think you're partially right but i think also actually as these systems become generally very capable our standard will just go up right so like everything is going to be like perfectly full all of your clothes will be ironed like even you even your sheets will be iron and they will be like perfect on your bed and like all the glasses will be on the line in the cabinet and like there will be no dust anywhere and like you can create a lot of work if you want to like if there's like no if there's no cost to the labor you can come up with a lot of things that are like marginally useful but i'm gonna do it anyway because like why not so i'm not i'm not sure there will be that much idle time we'll see We'll see you know when you were talking about perfectly arranging the glasses it was I was thinking Like most humans like my laundry is not perfectly folded So if you will let emulated me like it's it's it's folded, but it doesn't like you see creases You know if most people's environments are imperfect in the way that humans actually operate in the real world is just constantly Imperfections.