Figure’s Humanoid Factory Tour – CEO Brett Adcock

25:11It's limps. Yeah, I can basically ask this buddy in the back, say, I need another robot to come fill in. And the robot comes in and fills in and continues to do work. And we basically don't lose any time. It's pretty impressive. Yeah, it's really impressive. I think this is kudos to the controls team. I think we have like one of the best controls team in the world. About a year ago, we made a strong pivot away from code into neural networks. And I think the team has probably shown like, I think some of the best neural networks for control in the world on humanoids. What happens if it loses its knee while it's bent?

25:44It'll just, it'll basically do a velocity lock on that knee and should be able to hobble around. It obviously depends where, if we're like in a full squat down, that might be really tough. It depends what state it's in, but like in most cases, we can basically recover from this at this point and survive. You want to build a real-time operating system that is basically like fault-free. So this team here is all focused on trying to figure out all of the potential faults and risks. This team here is responsible for building our controller, which is responsible for how do we move the entire, like all the joints on the robot, keep balance and ultimately end up doing tasks.

26:18How many different teams are in the company and like how do you portion out who works on what and who gets integrated? So this is part of our AI team. It's one of our biggest teams here. We have like multiple different groups inside the AI division. We also have a team that does hardware, so they design motors, batteries, wire harness, structure, joints, kinematics, like basically a wide range of stuff. We have a platform software team that does all of our embedded software, firmware. We also have an electronics team that basically builds all the PCBs and electronics work that we do here internally.

26:50We design almost, there's over 100 PCBs that we design here on the robot that we do on that team. Things like our motor controllers, all this different type of work. We have a system integration test team that you saw today that helps basically make sure we ship like really good robots out to the world. We also have a team that does like basic design, like industrial design, which we're going to show you towards the end of this tour. How do we design something that's like, how do we design something that's like people really want? And I think we have a pretty high design standard here of designing something that is really delightful piece of technology.

27:23We also have a team that also work on system design and thermals. The robot produces a lot of heat while it's moving around. And how do we get that heat out of the robot? And how do we design for it here? We also have fabrication teams that make fabrication prototypes. We have a BACU team for manufacturing, supply chain team, facilities, and then we have all of our business operations side. So maybe like a dozen teams here internally that are needed to do this. It's basically the same teams that you need to build robots. So like any kind of robots. It's like when I, you know, at Archer, when I was building an aircraft, it's like a flying robot.

27:59So you have like the same type of stuff. You have like electric motors, batteries, control software, embedded systems and sensors. So we have basically teams around all that. And then obviously on the AI side is a big focus here internally. Do you want to see some stuff in the home? Yeah, let's go. All right, let's go. Thanks, Moritz. Thank you.

28:17All right, so. I was hoping it would be more dramatic, but. Lose the knee? Yeah. Yeah, it's like, honestly, it's a really hard engineering problem that we're so proud of internally. We have a large initiative. Like one of the teams that we have here is like a never fall team. And it's a team that basically predicts any potential faults and design around them. This is a case where like, I think you're always going to have a period of time where you're going to lose a lower body, like motor actuator. Then how do we survive this? Right. Okay, outside of stuff we're doing on the commercial market, one of our big focuses here is how do we ship a general purpose robot to do things like in the home.

28:56So, all right, come over here. We have a robot here that is designed to tidy the house. So clean up any, clean up the table, like basically like put away all the different cups, clean the toys up, tidy the couch. Like, you know, things in my house is kind of chaos. It's spraying and cleaning the table. Oh, yeah. Don't you want, we all need this. It's like a... That was a little sassy. It was. So what's cool here is the robot is running an onboard Helix 2 neural network to be able to do all this work, to be able to do all this cleaning. So it's basically just taking a prompt, which is like clean the living room, and it's basically reasoning through what to do from its cameras and it's ultimately telling the whole body what to do from a neural network.

29:50How many hours has this been trained on? Doesn't it take millions of hours to train? We've probably had in Helix, like the kind of base, pre and mid training, Helix models that we're working on now have maybe like a little under a million hours of total data And then we also do from mid training and pre training, we do post training here, which probably is I would say low, like basically thousands of hours that are running here. The goal for us is design a single kind of neural network platform that can basically do things like tidiest living room, but also do things like logistics. And this isn't teleoperated.

30:33This is not teleoperated. There are rumors that these are teleoperated. For sure not teleoperated. You don't have a secret room back here? Who's in there? No secret room. These robots are running purely autonomously from onboard AI policy called Helix, running onboard the robot in the torso. And so this robot's job is to clean this room up, and does this robot do this over and over again each day, or do they take turns? Like, how do you? Any robot in the fleet can do any of this work here. Okay, so it all connects. Yeah, it all connects. We run a single neural network that can basically run. It's the reason why humanoid is so great.

31:08It's like this same humanoid can go over and do logistics and healthcare and manufacturing or do dishes, just like we can. So our platform here is to basically run a single neural network that we call Helix on board the robot that can multitask between different things. When they're in people's homes, will that data still be trained and will it stay local or will it be spread throughout the network? Yeah, we basically, we need data, like basically the biggest blocker for us now of going from where we're at today to like large scale deployment is data. We need like an enormous amount of data. We need to pull a lot of our resources further into pre-training for the Helix team.

31:48And we just need a lot of diverse, really high quality data across the world. This means like data in the home, means maybe data more in the commercial market. So we have like two efforts going on here. One is like basically a large scale data collection effort that we're doing now at the company. And two is when we deploy robots, we do want to be collecting data and we do want to be training on that. And we do want to be sending that out up, training on it into a central training jobs. And then we want to be software updating the robots with the latest neural network weights. Does it get anonymized?

32:15How do you deal with the privacy? Oh, yeah. We like fully want to anonymize all of it. There's a lot of data we really don't care about. Most of the data we care about is like, what's the robot from a state perspective like scene? and how do we use that to basically train the robot to be more, to generalize better in the future at those different areas. Are you sticking mostly to the U.S. now? Because if you go to Europe, there's obviously a lot of data. Most of all of our work today is in the U.S. Okay. Yeah, we do want to be global, though. Yeah, Europe's a little tricky. Yeah, what are your plans?

32:44Like, how do you skirt around their data privacy? You basically, we got to play by the rules in Europe. Yeah. I think our hypothesis here is that we're missing a certain set of data that we're collecting now that will allow the robot to generalize in almost every condition of C's. We're kind of going off and doing the same things every day. We're doing dishes and laundry and tidying the home, the same stuff we're seeing. We're kind of doing the same movements, like grabbing something off the ground, putting it away, or pulling the dishwasher open. It's kind of the same stuff. Our hypothesis now is that we will see enormous amount of positive transfer from the data collection efforts we're doing now into pre-training across basically any environment in the world.

33:21I mean, that'll be like, it'll be somewhat, you know, we'll approach that somewhat like, it'll take, I would say, over time, a large amount of data to find every out of distribution, basically be able to do everything possible in the world, but we think there's a path to do this. And what is the price point differentiation for the in-home robot versus commercial? The in-home, like we're not selling right now to the home. We want to sell here, in the near term. And we want to sell the robot for hundreds of dollars a month. Somewhere like a car lease. Maybe like$400,$500,$600 a month. How are you thinking of deploying them in homes?

33:59Do you have to see if people have enough room? In New York City, I can't imagine these little apartments. It takes a dock. It's like two feet by two feet. You can plug it in a wall outlet. It'll go to its dock and charge. And then throughout the day, it'll just go off and do work. Whatever you want it to do. Like for me, I wanted to do like the laundry probably almost every day, dishes every day, and tidy the house multiple times a day. Do they have a distinctive diet? What do they eat? They eat nothing. They're keto? They just work 24-7. They're just constantly intermittent fasting. They're in this like purgatory state of just working 24-7 for the rest of their lives.

34:36Wow. Yeah. Fun. Yeah. Okay, I want to next go show you how we make them over at Baku. Yeah, great. Let's go. So it's kind of like HQ, but bot-cue. It's the HQ for bots. Oh, it's bot-cue. Yeah, bot-cue. I thought you were saying bot-cue. No. Isn't bot-cue? Bot-cue. Bot-cue. No, anyways. This is robot-cue. This is robot quarters. One thing that's really cool is on the way is we work at BMW. And last year, we deployed for six months robots on the basically body shop factory line to build cars. Yeah. And this was the first... You built this entire car? No, not the entire thing, but we helped build this car.

35:19Okay. This is an X3 that we helped basically... Like we basically helped... The robot helped assemble it. And this was the first car in the world built by a humanoid robot that we're aware of. And straight off the assembly line? Straight off the assembly line. I actually bought the first four. Okay. We have three here on campus and I have one at my house. Nice. And yeah, it's like a collector's item now. That's exciting. Yeah, it's pretty interesting. All right. So we have, this is kind of our campus here. We have four buildings. We're going to go through our manufacturing site, which is BotQ.

35:54And then we also have a site up here that I'll show you called The Grid. And the goal of that facility is to run robots, like, just like we would at our client sites. Could be in the home and also on the commercial side and 24-7 operations. Why is it called The Grid? It's a kind of a nod to like a sci-fi movie. And I don't know. You have a lot of inspiration from sci-fi movies? A sci-fi geek? It's a total sci-fi geek. I've seen every sci-fi movie. What's your favorite? Probably Contact. Jodie Foster. The alien thing? Yeah. It's kind of embarrassing to say, but... Why? I don't know. I just... The Contact's amazing.

36:36If you like it, don't be embarrassed. Yeah. But I'll just... I'll watch... I'm a sci-fi junkie. I'll watch any sci-fi. Yeah, so that's the grid up here. And we'll run robots in that facility 24-7. And the goal of that is last line of defense before we send out any code to our customers. So you don't want robots having any problems. We want to run them close enough to heavy operations that we would see out in the real world. And so we have a whole facility dedicated to basically running robots as hard as possible 24-7. We run on holidays, weekends, two to three in the morning. They just run all day, every day.

37:16Have any escaped? No, we've had one almost escaped. Really? No, nothing's escaped. Do you geofence them within properties? Do you do that when you set them up? We track them. Obviously, this is like for us right now, these are like high IP, very complicated hardware. We don't want to get stolen or out in the wrong hands. So we track it. Has that happened before? Are people stealing your IP? We do a lot of work on security internally here, so we haven't had any known IP thefts at the company. Okay. Yeah. All right. Welcome to Baku. All right. Now - Oh my God! Now, welcome to Baku. So this is where we manufacture figure three robots.

38:01Wow. So we do everything from build heads, batteries, legs, arms, fingers, thumbs, hands. And we basically do all testing here before we box the robots out or they walk next door. What does a box look like? A box? Yeah. We'll show you. We have one over there in a minute. Really? And right now, we basically, if we need them at headquarters or the grid, they basically just walk over. Where do you store them? At the office or client sites. Yeah. On the docks. They basically dock at nighttime. or whenever they're not needed. Okay, we're gonna show you some of the manufacturing lines. So we start first with basically head and battery.

38:40And we do some electronics, like basically quality and EOL checking. EOL is end of line, so we make sure we want every single subsystem to go through a pretty crazy test. Here's our headline. A rack of heads? Rack of heads. So here, here's a head. Our heads have basically Bluetooth, like Wi-Fi, 5G. They have camera systems on board. We have lights. We have thermal systems. We have an IMU. So basically the head is like basically a lot of sensors in here. The heads go through a pretty rigorous test here that we've designed internally for end-of-line testing. So heads here go through a, well first is we basically our flashing software here under the head for the first time, all the firmware.

39:34It's going through a calibration process for the cameras. And then we're basically making sure their head is in a nominal state to basically put onto a robot. So does it getting, are we getting signals out of it? Does it have any issues at all or any errors? If it does, we'll try to triage it. If it doesn't, we'll end up putting it on a robot. This is like when it's first born. Yeah, it's like it's kind of just like just raw hardware and it goes in here and it comes out with software and comes out with all the checks that we can use it with. Wow. Yeah. All right.

40:06How often do you walk through every part of the campus? Every day. Every day? Every day. So here is our battery line. So we have battery cells that come in, and then we basically do cell testing and basically voltage balancing. So here we're basically checking every single cell against the data sheet. We're also checking voltage, and we're balancing out the packs. So if there's any kind of voltage differential, we're basically making sure that all the packs basically have somewhat of a somewhat balanced voltage. Where do you get this machine from? We designed this machine. Oh. Yeah, this was custom design for figure here, for battery.

40:46And then we go through a process for potting, like wire bonding, and there's some polyurethane we put inside the battery pack for thermal runaway. And then at the end, pops out basically a pretty heavy 2.25 kilowatt hour battery pack. Yeah, I don't know, you want to try that? It's really quite heavy. Oh! Yeah. No, I don't think it's... Wait, let me try. You got it? Okay. Oh no, that is actually really heavy. So this battery will basically go right into the torso. It's one of the heaviest components we have. Yeah. How do you, I mean, I know all of this is stabilized, but like, is it better that it's one piece in the torso versus distributed across the body?

41:29Yeah, it's way better one piece. The battery pack has, just even for safety, we have like a lot of thermal runaway properties inside the pack that we've designed here internally only to make sure, in the worst case, you basically want to say like, okay, if a cell or battery cells go into thermal runaway, you never want that to ever propagate outside the pack. So you want it to contain it to the battery system itself. So we have basically a structural system and also a basically a thermal runaway venting process we've designed internally to basically allow for the battery to be extremely safe. Like the requirement is like, you want no flame to ever exit the pack.

42:02You don't want a robot like on fire or something like that out in the world. So we've designed the right CC system. We've never had a robot ever have. Catch fire? No. And then all of our figure threes are designed in a way that basically will prevent the robot from ever catching fire. So that actually was a pretty crazy hard engineering feat that we designed here internally. Also the pack is structural, take loads. So in case we fall, even like sharp objects or corners and things like this, we can never propagate inside the pack, the cells itself. Meaning you don't want anything to kind of like, kind of like send the battery cells itself into thermal runaway conditions.

42:40Have you had any supply chain risks, whether it's with China or other countries? Is that why you do everything here? We do most of the manufacturing here because the product is so new and it needs to be really controlled. And we also think about IP as really important here. We don't want any of the technologies to be stolen. And this is just hard too. We'd be able to put this thing together through a brand new supply chain that we had to design and get it in to make it work. is like it's non-trivial. You see how much testing we do at headquarters for all this work, testing we do at the grid. We'll do a ton of testing we'll show you here today.

43:12It's enormous. And the product is, we're kind of like early in the humanoid kind of like chapter book. So like cars have been around for over a century. The company's been around for four years, no? Yeah, not even four years yet. Yeah. And then humanoids are like really early in that whole process. How did you, we'll talk about this more in the long form, but like in terms of getting this up to scale so fast like you have now created a humanoid robot this is one of the most complex robot like i don't know engineering problems ever so like how did you get up to speed so fast um my company before this designed like flying robots at archer and it's got the same properties we have a battery pack but instead of like uh two kilowatt hours it's 160 kilowatt hours and it's distributed uh we have electric motors we have control software we have embedded systems and sensors, that's a robot.

44:06Archer's aircraft, my aircrafts there at, say, midnight, are highly overactuated. All the propellers have variable pitch. The front leading edge actuators tilt 90 degrees. You have the flaps on the wings and tail all move. You have basically 24 degrees of freedom. You have a little over 40 here on the robot. So in some way, it's similar enough systems here. And then, you know, when I started Figure, we were like, we have a very crisp and clear vision for the how to think about the product and engineering roadmap. And we just like went like went really hard building a team to 40 and putting the right resources in place for us to design stuff really fast.

44:45We had the we'll show you here next visit, but we have our figure one robot. It's kind of gnarly. It's got wires everywhere. It's our first generation. We had that walking before we were a year old. And we think it's probably one of the fastest times in human history. So it was just like a, you know, we were like laser focused pedal to the metal trying to get this thing to work. Yeah, yeah, okay. Let me show you some more stuff. So we have we have a bunch of different lines here that helps build pelvises, install battery, compute arms, legs. Here we're installing the lower leg. Is there a reason there are humans installing the leg?

45:20Well, at some point we will have robots doing all of this work. Don't let them hear that. Yeah. And we're putting more and more automation in lines now. We will be shipping our humanoid robots into the production lines here this year. And yeah, now we're doing the lower leg assembly for this robot. And at some point today, it will go through some testing. We'll show you in a minute. And we'll basically walk over to headquarters and start basically helping us either do AI development or doing use case testing for our customers. Pretty cool, huh? It's pretty cool. Yeah. Yeah, I'm so crazy seeing them get assembled Are you worried they're gonna become sentient?

46:05I Think I think it will be okay These things will get really smart like they're they're able to like do what humans can physically and I think the neural network Technologies were designing or we're trying to give like human common sense to all the robots So in some way, I think we'll get to at or even beyond human level intelligence in these systems. It actually might be the case that we get to artificial general intelligence first in these embodiments. Really? Yeah. Why? Because we're able to, like, this interaction data of, like, touching the world and seeing what happens through trial and error is, like, it basically, most of human intelligence is built this way.

46:46And I think this is the last missing piece to get the true AGI, is this real-world interaction with our environments. Okay, come with me over here. I'm going to show you some of the testing we were. So the robots are basically built starting with the pelvis, torso, head, arms, legs, and hands. And then we basically want to go through a very strenuous testing process to make sure everything is working nominally before we send it out. So we don't want any like loose cables or bad parts or bad communication. So we send the robot through what we call a final EOL test or end of line test. This is where the robots go through all the final checks and they basically go through also a burn-in on these lines.

47:27What do you mean by burn-in? They'll run for several hours and we'll basically make sure there's nothing that basically, no issues pop up over those few hours before we send the robots out. So here we have these bays that are running a combination of burn-in testing and end-of-line checks that we've designed here internally. So we basically go through a process where the robot's basically self, like trying to understand itself, like does anything seem wrong? If it is, we will flag it. If it's not, basically we go through a process where we basically do a bunch of checks and burn-in, make sure the robot's in good condition.

48:03If they pass here, we basically will walk over to headquarters. And if they fail here, we need to go fix it and understand why. Why do these ones have vests on them? When the robots are getting brought up, we basically, I think we hold it through a gantry system on the back. And they have their vests on for that system.

48:24It's just like the robots are like just been born and they're waking up. They're saying, you know, they look at their hands, they start calibrating itself visually. and trying to make sure everything's in a healthy state. Of this campus, what is your favorite part? I think Baku is one of my favorite parts here, like being able to build robots. In March, we had record manufacturing. We made more robots in March than we had ever in our entire lifetime combined. It's just cool to see us being able to do this and then get them out the door. So I would say this is probably one of my favorite places in the campus.

49:05I think maybe my other favorite place that will show some point here is like the robots doing really useful work 24-7 on either commercial customers or in the home. That stuff is just amazing. Because what we're here to do is we're here to basically build human-like intelligence in the world. And to see robots working 24-7, being able to do things like human scan is so special. It's such a hard thing to do. and be able to see us doing it at these levels of reliability, it's awesome. So I think it's probably a couple of my favorite places on campus. If you didn't have your job, what job would you want?

49:39Here? Yeah. I think there's a few things I really like. I like the engineering design process of how do you think about clean sheet, improving the system to be more reliable, cheaper, lower in mass, and overall a better functioning robot that can do more of what humans can with less complexity. I think that job across the hardware engineering and software engineering design org is like, I spent a lot of time in that leading engineering here and it's just like a, yeah, it's just a really fun and I think very hard problem. Every choice you make to try to make the robot better for thermals or better, like lower mass or like lower weight makes something else on the other side worse.

50:22Like if you're trying to make the robot lighter, it means it probably can't hold as much weight then. If you can't hold as much weight, the customers are like, well, if you can't carry 30-pound boxes around, I can't use you on this assembly line. So there's just a lot of interesting and very hard problems to solve there. I think second is how do we get neural networks to run on robots and generalize at scale on the Helix team? And that really is at this point a data and generalization issue. And that's a really hard, fun problem. Manufacturing is another one of how do we manufacture robots at scale?

50:55How do we continue to get robots in the manufacturing process to build themselves? And how do we get them to off the lines into the real world as fast as possible? At some point here, it'll just be full lights out manufacturing. We'll have robots only building robots and sending out to the world. Robots will be getting into boxes themselves. Other robots will be boxing them up and we'll be shipping them out to customers. Yeah. Sounds a bit sentient. It's a gift sentient, yeah. So that's another area where I'm just like, it's extremely interesting. I think the last piece is, we have a whole commercial operations team that's trying to get robots out to the world at scale and make them really useful.

51:30We did this with BMW last year and we're doing it with more customers this year for figure three. And it's a really cool problem because it's really hard. Like robots need to get in the environment, need to get it safely, like basically can almost never fault. And when they do fault, they need to understand that and self-correct. and then we need to be able to do useful human work at human performance. So our comparison is like, what does a human do today in terms of speeds and accuracies and then reliability. So like that's a hard bar to hit. So those are all like kind of like gigantic problems to go solve that I think if I was, you know, here, I'd want to spend all my time on those.

52:04Cool. Yeah, awesome. Okay, we want to see the design studio? Yeah, let's go. Let's go.

52:12What do you think of this? It's really fun. It's really fun. I have the coolest job because I just get to visit people's factories all day. Yeah. And everybody's building something different. I was at Applied Intuition earlier this week. I was then at Skydio. I've been to Archer. I've been to Anderil. We have just been looking through every door. That's awesome. Yeah. Yeah.

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54:27Five years ago when I took Archer Public, I saw this campus, it was all empty, and I was like, I gotta be here. Okay. The buildings are like really big, like it's just like they're all open. It's just like, and you have multiple buildings on campus and they're all close. And it's hard to find space like that where you can build hardware in, make loud noises, but also close where everybody lives in the Bay Area. Are you worried it's too nice? No, not at all. No? We're like, it looks like we're manufacturing in here and running robots, so it's, no, it's fine for us. It has some industrial grit here.

55:01You know what I mean? It feels like we're kind of like builders and makers. So it's kind of nice. It kind of reflects a lot about who we are on the team. And it's just my happy place. I've been looking at this for five years. We finally got it. And it's great. And I can just walk to manufacturing. I can walk to the grid. I can walk to Building 4. I can walk here to headquarters. And I can just be here with my team and pop in where I need to solve like any like whatever the biggest problem the day is. Is most of your talent down in South Bay or do they all commute? Most of the talent is here and then we have a shuttle for folks in the city that want to come down.

55:37So maybe like, I don't know, five or 10 percent of the, you know, folks here working like live in the city and commute down. And then I think maybe the majority of the rest all live kind of pretty local within like 20 or 30 minutes from the office. Yeah, luckily the weather is nice here today. The weather is freezing and cold and rainy yesterday. Yeah, yeah, the weather is pretty much the same here every day Okay, let's oh my god, we're back back so fast How did you come up with the logo? Okay, the logo is I got a couple things that are cool here. It's like one is like it kind of like looks like Like basically how the robot steps and tracking its steps and feet.

56:16Yeah, and then two it's like a little F for figure Here. So this is the fancy secret room now? This is the secret room that nobody's allowed to come in. This is our design studio. So I'm going to show you every robot we've ever built. Wow. Yeah. So we started in 2022. And the goal is like, how do we get human robots to our AI and software team as fast as possible? So we designed figure one here. This is our first generation robot. pretty cyberpunk. How much did this one cost to make and develop going down the line? Oh, wow. This one was built to be expensive and move extremely fast in terms of building it.

57:06So this was like hundreds of thousands of dollars. And the robots we have now are well under$100 ,000 each. And so yeah, this was very expensive mostly expensive because we CNC manufactured the entire thing Like basically the way we made all the metal parts was like extremely high precision like think like Formula One racecar type type type stuff We had this walk-in within the first year And we did we did a lot of the early AI stuff here that we kind of proved out the company was it was great And then we moved on to figure two which is which is here some improvements we did as we moved the battery that was on a backpack into the torso.

57:49Yeah. And then this one had like a basically relatively small computer compared to this. We basically tripled the compute. So we basically doubled the battery pack, tripled the compute. We have new camera sensors in the head and pelvis in the back. We had our next generation hands, and we basically wired the whole robot internally. And we also designed the structure similar to aircraft. Aircrafts take loads to the aircraft skin. and so we designed the basically the structure is an exoskeleton so all the load pass for the structures is exterior of exterior surface we made about I think like 50 of these and we just recently we're just we're just we just recently tired in about a month or two and then we've moved now to generation three this is our figure three robot those are all through the same with different outfits and the robot here we like reduced we reduced the weight we made it skinnier but also keep the same power and torques it's got it was in pic yeah it looks better like this little too like kind of too little body yeah robot less robot too much robot exactly like so we slimmed everything down we soft wrapped it it's got a layer of foam like on the shoulders and toward in the chest so it's soft we have our next we have our newest generation hands on this which have camera tactile sensors is basically able to grasp items much much easier we reduced the cost by about 90 percent between these two really yeah yeah what was the major cost there um we didn't care like we've optimized these first few generations for speed we didn't care as much about costs so that was like the biggest like just like misconception of designing initially with speed?

59:34Yeah, like we were sitting here in 2022, we're like our software folks need a humanoid to do testing and do AI work or whatever it is on it. And so and there's like there wasn't a time still really isn't a good humanoid robot to go by to help us speed us up. So we had to go build it. So it's like even if it's expensive, let's get stuff to the team as fast as possible to start getting like the basically start like working on the development process for commercialization. And same with figure two, the goal was like we had a lot of problems with reliability here that we needed to clean up. We had wires poking out and all kinds of issues here with this robot.

1:00:08It was kind of faulting every few hours. So we just had a ton of reliability issues we needed to clean up. Those were kind of known, though, because we were moving really fast. So I had figure two. It was just way too expensive, too hard to manufacture at scale. So figure three was how do we reduce the cost by almost an order of magnitude? How do we make a lot of them? How do we make it closer to what we think the ideal outcome is for every robot in the world? So these robots basically have the ability to take these clothes on and off. So we have different types of accessories we can put on them.

1:00:40Shoes, gloves, fabrics. How often are you thinking about the next version? What do you want the next version to be? Do you start thinking about it while you're building this one or after it? At what point of the production stage do you start the next iteration? We are now building a new robot almost every year. And we'll have, you know, we were like, we're like late stage now in the design process for figure four. And there are some changes that we like, we want to put into the iterate. This is like a, this is like iPhone sale, like iPhones. You know what I mean? Like everyone's getting better.

1:01:17Yeah, exactly. So everyone's trying to get better. So there are some things we just didn't get to and have enough time to work on on figure three that we want to do risk. We put into four. and there are also some things we've learned from like operating figure three now that we're like man this is like could be way better if we did this or that that we're putting into figure four as well and then we want to keep reducing costs and making it easier to manufacture at every step so we're looking at figure four we're like figure three we're like oh man like some of these things are like kind of hard to manufacture at Bocu or like how are we going to get it out of a box or how are we going to get it like a new user in a home like really easily so like taking all those collective learnings and we're putting them into the engineering design efforts and we go through many different like basically gating processes for that like starting with like an architecture review of what the system should look like in high level like you know like level zero requirements all the way through to detail design which we're we're now in for figure figure four what's kind of crazy here is um I thought at some point we'd like we would saturate out like an iPhone like doesn't really change much anymore I thought I figure three at this point was like this is like it this is like our best human robot in the world and it's every robot's gonna be like it's gonna be better but not by much what's going to happen here is you're going to have figure one kind of a you know to figure two like a step up and figure two to figure three at a step up figure four will be the biggest step up we've ever made by far we'll have it out here at one point and you'll be like oh my gosh it's just like radically different and um so we obviously can't talk too much we can't talk about anything basically on what we're doing there but like uh we are just so early we're like almost in like flip phones and now we're entering like iphone one moment i think maybe figure four will be our first like iPhone one moment for this where it's just like radically different and probably like for me I think it's like almost the perfect humanoid robot I can think of I'm sure there's things on five and six as we iterate through it'll be even better um but we've learned a lot here's a couple examples we have um here's some parts we have on the table for uh this is our generation of hands starting with our first generation hand to our current generation hand today we've gone through like five versions of this yeah one thing we have never shown is our first generation hand really yeah why was really difficult from an IP perspective an engineering perspective to go build and two is we think we learned a lot about it and we learned like things like what are good and good and bad in this case we felt like we had learned a lot about the hand of like why it's probably not the right direction so our first generation hand You can see here is a tendon driven hand.

1:03:47Yeah, we designed all the motors and actuators here ourselves even the gearboxes Basically the the rationale here is that a human hands like this are most of our motors are in our forearm and they're basically were like little like tendons basically driving all the fingers and So our first-generation hand is like how do we get a really dexterous hand built which is like really good for intelligence and AI and and then ultimately I can do a lot of things that human can and how do we mimic the biological kind of architecture of a human and so I was like this is gonna be great. We're gonna put motors in here, they're gonna be really powerful, they're gonna drive a really high degree of freedom hand and it ended up becoming the wrong engineering choice and we ended up pivoting away from it really early.

1:04:31Most of the wrist motors are also in here. So on figure one, I don't know if you noticed, but the wrists look crazy and the reason for this is that we pivoted away really early, away from this tendon-driven hand. We had to figure out a way to get new motors in for the wrist. So instead of waiting four months to redesign those from scratch, I took the motors from the feet. So we have three foot motors here in the forearm, and it's just like this Frankenstein forearm. And it bends, instead of bending here at the wrist, it bends halfway through the forearm, which is just really weird. And I was so ashamed.

1:05:06I'm like, we're going to get this thing out. it was like you know at the time I was like this is incredible. Did you sell any of these? We didn't sell them. We just used them internally. But we showed it and it was like this big like forearm. I was like everybody's gonna notice this big forearm. It seems so weird. Yeah. And I don't think I've ever had a single person in like three years ask why the forearm was like this. Really? And everyone was just like it's a robot. Not a single one. No questions. Yeah so we so we ended up pivoting away from like the tendon driven hand to our current generations of hands and And we've learned a lot, but like I, yeah, we ultimately, I think, are building like some of the best hand technology in the world.

1:05:43We recently unveiled our high degree of freedom hand as a teaser, our next generation hand, about a month ago. And this hand has basically a human level dexterity, like as many joints in the hand as a human. And this is really important, not just for like being able to dexterous tasks, but we need to be able to learn passively from humans at scale. And if humans can move hands in all these different crazy way, we need to be able to map to this at test time on the robot. So we have, I think this is extremely important to get to, if we want to solve like AGI and get to human intelligence in the physical world, it's all going to start here with the hands for us.

1:06:22Yeah, it's intense. It's complex. I did see a couple people walking around the campus in spandex outfits. Yeah, it's a mandatory outfit. to work it yeah like it just got to be in spandex so you can't come down um yeah so we basically are doing a lot of data collection here where we're trying to i basically do like joint level tracking uh and different type of uh data collection efforts like learning from humans like our training set is like how do we like we're a humanoid we need to learn from humans at scale and so we're trying to learn as much as possible about human movements and like image conditioning in these policies here figure.

1:07:00Is that the oddest job? What is that called? What is the oddest job in the office? Is, you think that's odd? I think it's kind of cool. It's uncommon. Yeah, it's uncommon. Probably the oddest job we have there, yeah. How does one apply? You apply on the site. You apply on the website. We do like, we basically have like data collection folks that we basically are here, that we have both here and out in the world doing data collection for Helix. That's cool. Yeah. Yeah. I actually think it's a really cool job. Have you ever tried it? Full spandex? Yeah. I haven't tried the full spandex, but I've done every other type of data collection effort here.

1:07:37Maybe that'll be your next job. Maybe you should. Yeah. I'm going to go get in some spandex later, and I'll mess it out. OK. So we saw the generation of hands. OK. So generation of hands. We also have some mock-ups for the head and feet. I think the feet are actually really interesting here. This is kind of our first prototype for figure three. It's like basically like we really wanted to get a toe in the robot which is important both of like a natural looking gait Like as you walk but also like getting off off the ground Yeah, it's really important or even squatting down when we squat down like on our toe box And it's really difficult with a flat foot and then basically this is our this is our figure two like foot It's basically just a fixed piece of metal nothing too crazy impressive and then this is our current generation of For the fly nets.

1:08:22It's for figure three. We basically have a toe. We have this opening here in the foot. Some people ask about this. This is basically for thermal venting as we're charging. We're pushing air through the calf and the shin through the foot to cool it down as it's charging because it's got inductive coils on the bottom. So these feet are basically stepping onto the charger. We then initiate charge wirelessly, And the robot can charge at two kilowatts. So basically we can charge for an hour by standing there That's really cool. That's really cool. Yeah, we can charge like at client sites like this we can walk over We can dock to it.

1:09:03We can just stand on it over time we're gonna get these systems even smaller and Be able to put in basically build put it anywhere and you can plug this into a normal wall outlet for charging Are you gonna do any brand deals? Like foot deals? I would love to do. Maybe not foot, but sneaker deals. Sneaker deals. If Nike's watching, you're going to be our next Nike sneaker. Shoe dealer. Yeah, I really wanted a high top for figure three. I think it's just so cool. Yeah, it's pretty cool. Our figure two looks like a penny loafer. You know what I mean? Can't be having that, no. We can't have a penny loafer out here.

1:09:36This high top's kind of made to do work. So of the designs here, you have quite a sleek design. It's very futuristic. how did how like how did you get to this point yeah we have a design we have a industrial design team here internally run by uh david uh he just met and we we have a team that are obsessed with trying to create like uh yeah trying to create like the um we want to create something that uh is really delightful to be around and um it's it's not just like the way the robot looks or the size of it it's how it walks and interacts and how its body language and how the human machine interaction is it does it look at you while it's while it's talking uh how do we deal with speech what do we do with like we have like three screens in the head like what do we show there how to make us like really pleasant to be around are there any i mean you love sci-fi were there any sci-fi movies where you wanted oh yeah like the robot movies i robot ex machina like which ones i think a thing we always talk about here is like there's like two roads for humanoids there's a road to head to like down like the robotic road which is like iRobot and there's a road to head down for like Westworld okay also humanoid yeah where do we go what do you think I mean I'm a big fan of X Machina okay so what we go to Westworld yeah yeah okay let's do it yeah we're heading to Westworld I think lastly is we're like super proud to be on the cover of Time magazine this past which was really cool.

1:11:07We had a robot in a home basically doing like full like, you know, like doing housework with Helix AI system that we designed here internally. What's with the Deadmau5 record? Yeah, we had Deadmau5 at our holiday party two years ago and he was like, this is insane. We had robots on stage like, I got to get you guys out to concerts with me and we got to be like dancing on stage. So we We opened for him at Red Rock end of last year in Colorado. I don't know if you've been to the concert. I flew in for it. It was amazing. Just like an amazing venue. And we had multiple robots on stage just dancing and they're all tuned in to the music.

1:11:45And they kind of dance with it. And it was just, it was awesome. We had him also here at our last holiday party in December. And I don't know, we just raged with Deadmau5. Hey, it's Molly. If you enjoy our interviews, check out our newsletter, Sorcery.VC, where we deliver a once a week top deals and tech headlines email and also go deeper on our podcast interviews. Subscribe to Sorcery today and don't forget to subscribe to the podcast on YouTube, Spotify, Apple, or wherever you listen. Link in description to sign up.