Robert Playter: Boston Dynamics CEO on Humanoid and Legged Robotics | Lex Fridman Podcast #374

- And so our goal was a natural-looking gait. It was surprisingly hard to get that to work. But we did build an early machine. We called it PETMAN prototype. It was the prototype before the PETMAN robot, and it had a really nice-looking gait. where, you know, it would stick the leg out. It would do heel strike first before it rolled onto the toe, so you didn't land with a flat foot. You extended your leg a little bit, but even then,

it was hard to get the robot to walk. where, when you were walking, that it fully extended its leg. and getting that all to work well took such a long time. In fact, I probably didn't really see. the nice, natural walking that I expected. out of our humanoids until maybe last year. And the team was developing on our newer generation. of Atlas, you know, some new techniques. for developing a walking-control algorithm.

And they got that natural-looking motion as sort. of a byproduct of just a different process. they were applying to developing the control. So, that probably took 15 years, 10 to 15 years to sort. of get that from, you know, the PETMAN prototype was probably in 2008, and what was it, 2022, (laughs) last year. that I think I saw good walking on Atlas. (dramatic music). - The following is a conversation with Robert Playter,

CEO of Boston Dynamics, a legendary robotics company. that, over 30 years, has created some of the most elegant, dextrous, and simply amazing robots ever built, including the humanoid robot Atlas and the robot dog Spot, one or both of whom you've probably seen on the Internet, either dancing, doing backflips, opening doors, or throwing around heavy objects. Robert has led both the development.

of Boston Dynamics humanoid robots. and their physics-based simulation software. He has been with the company from the very beginning, including its roots at MIT, where he received his PhD in aeronautical engineering. This was in 1994 at the legendary MIT Leg Lab. He wrote his PhD thesis on robot gymnastics. as part of which he programmed a bipedal robot to do. the world's first 3D robotic somersault.

Robert is a great engineer, roboticist, and leader, and Boston Dynamics, to me as a roboticist, is. a truly inspiring company. This conversation was a big honor and pleasure, and I hope to do a lot of great work. with these robots in the years to come. This is the Lex Fridman podcast. To support it, please check out our sponsors. in the description. And now, dear friends, here's Robert Playter. When did you first fall in love with robotics?

(Lex laughs). Let's start with love and robots. - Well, love is relevant because I think. the fascination, the deep fascination is. really about movement, and I was visiting MIT looking. for a place to get a PhD, and I wanted to do some laboratory work. And one of my professors in the aero department said, "Go see this guy Marc Raibert. down in the basement of the AI lab.". And so I walked down there and saw him.

He showed me his robots, and he showed me this robot doing a somersault. (Lex laughs). And I just immediately went, "Whoa," you know. - [Lex] Yeah. - "Robots can do that?". And because of my own interest in gymnastics, there was, like, this immediate connection, and, you know, I was in an aeroastro degree. because, you know, flight and movement was. all so fascinating to me. And then it turned out. that, you know, robotics had this big challenge.

How do you balance? How do you build a legged robot that can really get around? That was a fascination, and it still exists today. We're still working on perfecting motion in robots. - What about the elegance and the beauty. of the movement itself? Is there something maybe grounded in your appreciation. of movement from your gymnastics days? Was there something you just fundamentally appreciated. about the elegance and beauty of movement?

- You know, we had this concept. in gymnastics of letting your body do what it wanted to do. When you get really good at gymnastics, part of what you're doing is putting your body. into a position where the physics. and the body's inertia and momentum will kinda push you. in the right direction in a very natural and organic way. And the thing that Marc was doing, you know, in the basement of that laboratory was trying to figure out.

how to build machines to take advantage of those ideas. How do you build something so that the physics. of the machine just kind of inherently wants. to do what it wants to do? And he was building these springy pogo-stick type, you know. His first cut at legged locomotion was a pogo stick. where it's bouncing, and there's a spring mass system. that's oscillating, has its own sort. of natural frequency there and sort of figuring. out how to augment those natural physics with also intent.

How do you then control that but not overpower it? It's that coordination that I think creates real potential. We could call it beauty, you know. You could call it, I don't know, synergy. People have different words for it. But I think that that was inherent from the beginning. That was clear to me that that's part. of what Marc was trying to do. He asked me to do that in my research work. So, you know, that's where it got going. - So, part of the thing that I think I'm calling elegance. and beauty in this case, which was there, even with the pogo stick is maybe the efficiency,

so letting the body do what it wants to do, trying to discover the efficient movement. - It's definitely more efficient. It also becomes easier to control in its own way. because the physics are solving some of the problem itself. It's not like you have to do all this calculation. and overpower the physics. The physics naturally, inherently want. to do the right thing. There can even be, you know, feedback mechanisms, stabilizing mechanisms that occur simply by virtue.

of the physics of the body. And it's, you know, not all in the computer. or not even all in your mind as a person (laughs). And there's something interesting in that melding. - You were with Marc for many, many, many years, but you were there in this kinda legendary space. of Leg Lab and MIT in the basement (laughs). All great things happen in the basement. (Robert laughs). Is there some memories from that time that you have?

Because it's such cutting-edge work. in robotics and artificial intelligence. - The memories, the distinctive lessons, I would say. I learned in that time period. and that I think Marc was a great teacher of was. it's okay to pursue your interests, your curiosity, do something because you love it. You'll do it a lot better if you love it.

That is a lasting lesson that I think we apply. at the company still and really is a core value. - So, the interesting thing is, with people like Russ Tedrake and others, like, the students that work. at those robotics labs are, like, some. of the happiest people I've ever met. I don't know what that is. (laughs). I meet a lot of PhD students. A lot of them are kind of broken.

(laughing) by the wear and tear of the process, but roboticists are, while they work extremely hard. and work long hours, there's a happiness there. The only other group of people I've met like that are people. that skydive a lot. (both laughing). For some reason, there's a deep, fulfilling happiness maybe. from, like, a long period of struggle. to get a thing to work, and it works, and there's a magic to it. I don't know exactly 'cause it's so fundamentally hands-on, and you're bringing a thing to life.

I don't know what it is, but they're happy. - You know, our attrition at the company is really low. People come, and they love the pursuit. And I think part of that is. that there's perhaps a natural connection to it. It's a little bit easier to connect when you have a robot. that's moving around in the world, and part of your goal is. to make it move around in the world. You can identify with that. This is one of the unique things. about the kinds of robots we're building is. this physical interaction lets you perhaps identify with it.

So, I think that is a source of happiness. I don't think it's unique to robotics. I think anybody also who is just pursuing. something they love, it's easier to work hard at it. and be good at it, and not everybody gets to find that. I do feel lucky in that way. And I think we're lucky as an organization. that we've been able to build a business around this. and that keeps people engaged. - So, if it's all right, let's linger on Marc.

for a little bit longer, Marc Raibert. So, he's a legend. He's a legendary engineer and roboticist. What have you learned about life, about robotics from Marc. through all the many years you've worked with him? - I think the most important lesson, which was, you know, have the courage of your convictions. and do what you think is interesting. Be willing to try to find big, big problems to go after. And at the time, you know, legged locomotion,

especially in a dynamic machine, nobody had solved it. And that felt like a multi-decade problem to go after. And so, you know, have the courage to go after that. because you're interested. Don't worry if it's gonna make money. You know, that's been a theme. That's really probably the most important lesson I think. that I got from Marc. - How crazy is the effort of doing legged robotics.

at that time, especially? - You know, Marc got some stuff to work starting. from simple ideas. So, maybe the other, another important idea. that has really become a value of the company is. try to simplify a thing to the core essence. While, you know, Marc was showing videos of animals running. across the Savannah or climbing mountains, what he started with was a pogo stick.

because he was trying to reduce the problem to something. that was manageable, and getting the pogo stick. to balance had in it the fundamental problems. that, if we solved those, you could eventually extrapolate. to something that galloped like a horse, and so look. for those simplifying principles. - How tough is the job of simplifying a robot? - So, I'd say, in the early days, the thing that made. the researchers at Boston Dynamics special is.

that we worked on figuring out. what that central principle was. and then building software or machines. around that principle, and that was not easy. in the early days. And it took real expertise in understanding the dynamics. of motion and feedback-control principles, how to build, you know, with the computers at the time, how to build a feedback-control algorithm.

that was simple enough that it could run in real time. at 1,000 hertz and actually get that machine to work. And that was not something everybody was doing, you know, at that time. Now, the world's changing now, and I think the approaches. to controlling robots are going to change, and they're going to become more broadly available. But at the time, there weren't many groups. who could really sort of work at that principled level.

with both the software and make the hardware work. And I'll say one other thing about you were sort of talking. about what are the special things. The other thing was it's good to break stuff, you know. You know, use the robots, break them, repair them, you know, fix and repeat, (laughs) test, fix, and repeat. And that's also a core principle that has become part.

of the company, and it lets you be fearless in your work. Too often, if you are working with a very expensive robot, maybe one that you bought from somebody else. or that you don't know how to fix, then you treat it with kid gloves, and you can't actually make progress. You have to be able to break something. And so, I think that's been a principle as well. - So, just to linger on that, psychologically, how do you deal with that? 'Cause I remember I built a RC car.

It had some custom stuff like a computer on it. and all that kind of stuff, cameras. and because I didn't sleep much, the code I wrote had an issue where it didn't stop the car, and the car got confused and at full speed. at, like, 20, 25 miles an hour, it slammed into a wall. And I just remember sitting there alone in a deep sadness, sort of full of regret, I think, almost anger,

but also, like, sadness because you think about, well, these robots, especially for autonomous vehicles, like, you should be taking safety very seriously. even in these kinds of things, but just no good feelings. It made me more afraid probably to do these kind. of experiments in the future. Perhaps the right way to have seen that is positively. Like, it's too-. - It depends if you could have built that car. or just gotten another one, right? That would've been the approach.

I remember when I got to grad school, you know, I got some training about operating a lathe. and a mill up in the machine shop, and I could start to make my own parts. And I remember breaking some piece of equipment in the lab. and then realizing 'cause maybe this was a unique part, and I couldn't go buy it, and I realized, "Oh, I can just go make it.".

That was an enabling feeling. - [Lex] Yeah. - Then, you're not afraid. It might take time. It might take more work than you thought. it was gonna be required to get this thing done, but you can just go make it. And that's freeing in a way that nothing else is. - You mentioned the feedback control, the dynamics, sorry for the romantic question, but in the early days and even now, is the dynamics, probably more appropriate for the early days, is it more art or science?

- There's a lot of science around it, and trying to develop, you know, scientific principles that let you extrapolate. from, like, one legged machine to another, you know, develop a core set of principles. like a spring-mass bouncing system and then figure out how. to apply that from a one-legged machine. to a two- or a four-legged machine. Those principles are really important. and were definitely a core part of our work.

There's also, you know, when we started. to pursue humanoid robots, there was so much complexity. in that machine that, you know, one of the benefits. of the humanoid form is you have some intuition. about how it should look while it's moving. And that's a little bit of an art, I think, or maybe it's just tapping into a knowledge. that you have deep in your body and then trying.

to express that in the machine, but that's an intuition. that's a little bit more on the art side. Maybe it predates your knowledge. Before you have the knowledge of how to control it, you try to work through the art channel. (laughs). - [Lex] Yeah. - And humanoids sort. of make that available to you. If it had been a different shape, maybe you wouldn't have had the same intuition about it. - Yeah, so your knowledge about moving. through the world is not made explicit to you. That's why it's art.

- Yeah, it might be hard to actually articulate exactly. (laughing) You know? - Yeah. - And being a competitive athlete, there's something about seeing a movement. You know, a coach, one of the greatest strengths. a coach has is being able to see, you know, some little change in what the athlete is doing. and then being able to articulate that to the athlete, you know, and then maybe even trying to say, "And you should try to feel this.". So, there's something just in seeing, and again, you know, sometimes it's hard to articulate.

what it is you're seeing, but just perceiving the motion. at a rate that is, again, sometimes hard to put into words. - Yeah, I wonder how it is possible to achieve sort. of truly elegant movement. You have a movie like "Ex Machina.". I'm not sure if you've seen it, but the main actress in that who plays the AI robot. I think is a ballerina. I mean, just the natural elegance.

and the, I don't know, eloquence of movement, (laughs). it looks efficient and easy, and just it looks right. It looks beautiful. - It looks right is. sort of the key, yeah? - And then, you look at, especially early robots, I mean, they're so cautious in the way they move. that it's not the caution that looks wrong. It's something about the movement that looks wrong.

that feels like it's very inefficient, unnecessarily so. And it's hard to put that into words exactly. - We think that, and part of the reason. why people are attracted to the machines we build is. because the inherent dynamics. of movement are closer to right. because we try to use, you know, walking gaits, or we build a machine around this gait. where you're trying to work with the dynamics of the machine. instead of to stop them.

You know, some of the early walking machines, you know, you're essentially, you're really trying hard. to not let them fall over, and so you're always stopping the tipping motion, you know. And sort of the insight of dynamic stability. in a legged machine is to go with it, you know, (laughs). let the tipping happen. You know, let yourself fall, but then catch yourself with that next foot. And there's something about getting those physics. to be expressed in the machine that people interpret.

as lifelike, or elegant, or just natural looking. And so, I think if you get the physics right, it also ends up being more efficient, likely. There's a benefit that it probably ends. up being more stable in the long run. You know, it could walk stably over a wider range. of conditions, and it's more beautiful. and attractive at the same time. - So, how hard is it to get the humanoid robot Atlas.

to do some of the things that it's recently been doing? Let's forget the flips and all of that. Let's just look at the running. Maybe you can correct me, but there's something about running. I mean, that's not careful at all. That's you're falling forward. You're jumping forward and are falling. So, (laughing) how hard is it to get that right? - Our first humanoid, we needed to deliver. natural-looking walking, you know. We took a contract from the army. They wanted a robot that could walk naturally.

They wanted to put a suit on the robot. and be able to test it in a gas environment. And so, they wanted the motion to be natural. And so, our goal was a natural-looking gait. It was surprisingly hard to get that to work. But we did build an early machine. We called it PETMAN prototype. It was the prototype before the PETMAN robot, and it had a really nice-looking gait.

where, you know, it would stick the leg out. It would do heel strike first before it rolled onto the toe, so you didn't land with a flat foot. You extended your leg a little bit, but even then it was hard to get the robot to walk. where, when you were walking, that it fully extended its leg. and essentially landed on an extended leg. And if you watch closely how you walk, you probably land on an extended leg, but then you immediately flex your knee. as you start to make that contact, and getting that all to work well took such a long time.

In fact, I probably didn't really see. the nice, natural walking that I expected. out of our humanoids until maybe last year. And the team was developing on our newer generation. of Atlas, you know, some new techniques. for developing a walking-control algorithm. And they got that natural-looking motion as sort. of a byproduct of just a different process. they were applying to developing the control.

So, that probably took 15 years, 10 to 15 years to sort. of get that from, you know, the PETMAN prototype was probably in 2008, and what was it, 2022, (laughs) last year. that I think I saw good walking on Atlas. - If you could just, like, linger on it, what are some challenges of getting good walking? So, is this partially, like, a hardware, like, actuator problem? Is it the control? Is it the artistic element of just observing.

the whole system operating in different conditions together? I mean, is there some kind of interesting quirks. or challenges you can speak to, like the heel strike. or all this kind of stuff? - Yeah, so one. of the things that makes, like, this straight leg. a challenge is you're sort of up against a singularity, a mathematical singularity where, you know, when when your leg is fully extended, it can't go further the other direction, right? You can only move in one direction, and that makes all. of the calculations around how to produce torques.

at that joint or positions makes it more complicated. And so, (laughs) having all of the mathematics. so it can deal with these singular configurations is one. of many (laughs) challenges that we face. And I'd say, you know, in those earlier days, again, we were working with these really simplified models. So, we're trying to boil all the physics. of the complex human body into a simpler subsystem.

that we can more easily describe in mathematics. And sometimes those simpler subsystems don't have all. of that complexity of the straight leg built into them. And so, what's happened more recently is we're able. to apply techniques that let us take the full physics. of the robot into account. and deal with some of those strange situations. like the straight leg. - So, is there a fundamental challenge here that it's,

maybe you can correct me, but is it underactuated? Are you falling? - Underactuated is the right word, right? You can't push the robot in any direction you want to. - Yeah. - Right? And so, that is one of the hard problems. of legged locomotion. - And you have to do that for natural movement? - It's not necessarily required for natural movement. It's just required, you know, we don't have, you know, a gravity force that you can hook yourself onto to apply.

an external force in the direction you want. at all times, right? The only external forces are being mediated. through your feet, and how they get mediated depend. on how you place your feet, and you know, you can't just, you know, God's hand can't reach down and push. in any direction you want, (laughs) you know, so. - Is there some extra challenge to the fact. that Atlas is such a big robot? - There is. The humanoid form is attractive in many ways, but it's also a challenge in many ways.

You have this big upper body that has a lot. of mass and inertia, and throwing that inertia. around increases the complexity of maintaining balance. And as soon as you pick up something heavy in your arms, you've made that problem even harder. And so, in the early work in the Leg Lab. and in the early days at the company, you know, we were pursuing these quadruped robots, which had a kind of built-in simplification.

You had this big rigid body and then really light legs. So, when you swing the legs, the leg motion didn't impact the body motion very much. All the mass and inertia was in the body, but when you have the humanoid, that doesn't work. You have big heavy legs. You swing the legs. It affects everything else. (Lex laughs). And so, dealing with all of that interaction does make. the humanoid a much more complicated platform. - And I also saw that at least recently you've been doing.

more explicit modeling of the stuff you pick up. - [Robert] Yeah, yeah. - Which is (laughs) really interesting. So, you have to, what, model the shape, the weight distribution. I don't know, like, you have to, like, include that. as part of the modeling, as part of the planning. 'cause okay, so for people who don't know, so Atlas, at least in, like, a recent video, like, throws a heavy bag, throws a (laughing) bunch of-.

- [Robert] Yeah. - stuff. So, what's involved in picking up a thing, a heavy thing? And when that thing is a bunch. of different non-standard things, I think it also picked up like a barbell. and to be able to throw in some cases, what are some interesting challenges there? - So, we were definitely trying to show that the robot. and the techniques we're applying to Atlas let us deal. with heavy things in the world. Because if the robot's gonna be useful, it's actually gotta move stuff around.

And that needs to be significant stuff. that's an appreciable portion. of the body weight of the robot. And we also think this differentiates us. from the other humanoid robot activities. that you're seeing out there. Mostly, they're not picking stuff up yet, not heavy stuff anyway. But just like you or me, you know, you need to anticipate that moment. You know, you're reaching out to pick something up, and as soon as you pick it up, your center of mass is gonna shift. And if you're gonna, you know, turn in a circle,

you have to take that inertia into account. And if you're gonna throw a thing, you know, all of that has to be sort of included. in the model of what you're trying to do. So, the robot needs to have some idea or expectation. of what that weight is and sort of predict, you know, think a couple of seconds ahead, "How do I manage now my body plus this big heavy thing. together (laughs) and still maintain balance, right?". And so, that's a big change for us,

and I think the tools we've built are really allowing. that to happen quickly now. Some of those motions that you saw in that most recent video. we were able to create in a matter of days. It used to be that it took six months to do anything new, you know, on the robot, and now we're starting to develop. the tools that let us do that in a matter of days. And so, we think that's really exciting. That means that the ability to create new behaviors. for the robot is gonna be a quicker process.

- So, being able to explicitly model new things. that it might need to pick up, new types of things? - And you know, to some degree, you don't wanna have to pay too much attention. to each specific thing, right? There's sort of a generalization here. Obviously, when you grab a thing, you have to conform your hand, your end effector. to the surface of that shape, but once it's in your hands, it's probably just the mass and inertia that matter,

and the shape may not be as important. - [Lex] Yeah. - And so, you know, in some ways you wanna pay attention. to that detailed shape, and in others, you wanna generalize it and say, "Well, all I really care about is. the center of mass of this thing, especially if I'm gonna throw it up on that scaffolding.". - And it's easier if the body is rigid. What if there's some, doesn't it throw, like, a sandbag type thing? - That tool bag, you know- - Tool bag. - had loose stuff in it, so it managed that. There are harder things that we haven't done yet.

You know, we could have had a big jointed thing. or, I don't know, a bunch of loose wire or rope. - What about carrying another robot? How 'bout that? (laughing). - Yeah, we haven't done that yet. - [Lex] Carry Spot. - I guess we did a little bit of a, we did a little skit around Christmas. where we had two Spots holding up another Spot. that was trying to put, you know, a bow on a tree. So, I guess we're doing that in a small way. (laughing). - Okay, that's pretty good. Let me ask the all-important question. Do you know how much Atlas can curl?

(Robert laughing drown out Lex speaking). (Lex laughs). I mean, you know, for us humans, that's really one. of the most fundamental questions. you can ask another human being, curl, bench, et cetera. (Robert laughs). - It probably can't curl as much as we can yet, but a metric that I think is interesting is, you know, another way of looking at that strength is, you know, the box jump. So, how high of a box can you jump onto? - [Lex] Question.

- And Atlas, I don't know the exact height. It was probably a meter high or something like that. It was a pretty pretty tall jump that Atlas was able. to manage when we last tried to do this. And I have video of my chief technical officer. doing the same jump, and he really struggled, you know, to get- - Oh, the human? - The human getting all the way on top of this box. But then, you know, Atlas was able to do it. We're now thinking about the next generation of Atlas, and we're probably gonna be in the realm. of a person can't do it, you know, with the next generation.

The robots, the actuators are gonna get stronger. where it really is the case that at least some. of these joints, some of these motions will be stronger. - And to understand how high it can jump, you probably had to do quite a bit of testing. - Oh, yeah, and there's lots of videos of it trying. and failing, and you know, that's all, you know, we don't always release those videos, but they're a lot of fun to look at. (laughs). - So, we'll talk a little bit about that. But can you talk to the jumping? 'Cause you talked about the walking,

and it took a long time, many, many years. to get the walking to be natural, but there's also really natural-looking, robust, resilient jumping. How hard is it to do the jumping? - Well, again, this stuff has really evolved rapidly. in the last few years. You know, the first time we did a somersault, you know, there was a lot of kind of manual iteration. What is the trajectory? You know, how hard do you throw? In fact, in these early days,

when I'd see early experiments that the team was doing, I might make suggestions about how to change the technique, again, kind of borrowing from my own intuition. about how backflips work. But frankly they don't need that anymore. So, in the early days, you had to iterate kind. of in almost a manual way trying. to change these trajectories of the arms or the legs. to try to get, you know, a successful backflip to happen. But more recently, we're running. these model-predictive control techniques.

where the robot essentially can think in advance. for the next second or two about how its motion is going. to transpire, and you can, you know, solve. for optimal trajectories to get from A to B. So, this is happening in a much more natural way, and we're really seeing an acceleration happen. in the development of these behaviors, again, partly due to these optimization techniques,

sometimes learning techniques, so it's hard in that there's a lot of mathematics behind it, but we're figuring that out. - So, you can do model-predictive control for, I mean, I don't even understand what that looks like. when the entire robot is in the air. flying and doing a back (laughs). - Yeah, well- - I mean. (laughs). - But that's the cool part, right? So, you know, the physics, we can calculate physics pretty well using, you know,

Newton's laws about how it's going to evolve over time. and you know, the sick trick, which was a front somersault. with a half twist is a good example, right? You saw the robot on various versions of that trick. I've seen it land in different configurations, and it still manages to stabilize itself, and so, you know, what this model-predictive control means is, again, in real time, the robot is projecting ahead, you know,

a second into the future and sort of exploring options. And if I move my arm a little bit more this way, how is that gonna affect the outcome? And so, it can do these calculations, many of them, you know, and basically solve. for where, you know, given where I am now, maybe I took off a little bit screwy. from how I had planned, I can adjust. - [Lex] So, you're adjusting in the air. for the landing. - Adjust on the fly. So, the model-predictive control lets you adjust. on the fly, and of course, I think this is. what, you know, people adapt as well.

When we do it, even a gymnastics trick, we try to set it up so it's close to the same every time. But we figured out how to do some adjustment on the fly, and now we're starting to figure out. that the robots can do this adjustment on the fly. as well using these techniques. - In the air. I mean, it just feels, from a robotics perspective, just surreal. - You talked about underactuated, right? - [Lex] Yes. - So, when you're-. - That's totally true. - When you're in the air, there's some things you can't change, right?

You can't change the momentum while it's in the air. 'cause you can't apply an external force, a torque, and so the momentum isn't gonna change. So, how do you work within the constraint. of that fixed momentum to still get from A to B (laughs). where you wanna be? - That's really (laughing) underactuated. (Robert laughs). You're in the air. I mean, you become a drone for a brief moment in time. No, you're not even a (laughing) drone 'cause you can't-. - [Robert] Can't hover. - You can't hover. You can't. - You're gonna impact soon. Be ready. (laughs) - Yeah. Have you considered like a hover type thing or no?

No? - No. - It's too much weight? - No. (Lex laughing). - I mean, it's just incredible and just even. to have the guts to try a backflip with such a large body. That's wild. (Robert laughs). But, like how-. - We definitely broke a few robots trying that. - [Lex] (laughing) Yeah. (Robert laughs). - But that's where the build it, break it, fix it, you know, strategy comes in. You gotta be willing to break. And what ends up happening is. by breaking the robot repeatedly, you find the weak points, and then you end up redesigning it. so it doesn't break so easily next time, you know. (laughs).

- Through the breaking process you learn a lot, like, a lot of lessons, and you keep improving. not just how to make the backflip work, but everything just- - Yeah. And how to build the machine better. - Yeah. - Yeah. - I mean, is there something about just the guts. to come up with an idea of saying, "You know what? Let's try (laughing) to make it to a backflip"? - Well, I think the courage to do a backflip. in the first place and to not worry too much. about the ridicule of somebody saying, "Why the heck are you doing backflips with robots?".

- [Lex] Sure. - Because a lot of people have asked that, you know. (Lex laughs). (laughing) "Why are you doing this?". - Why go to the moon. (Robert laughs). in this decade and do the other things, JFK? (Robert laughs). Not because it's easy, because it's hard. - [Robert] Yeah, exactly. (laughs). (Lex laughs). - Don't ask questions. Okay, so the jumping, I mean, there's a lot of incredible stuff. If we can just rewind a little bit. to the DARPA Robotics Challenge in 2015, I think, which was, for people who aren't familiar.

with the DARPA challenges, it was first. with autonomous vehicles, and there's a lot. of interesting challenges around that. And the DARPA Robotics Challenge was. when humanoid robots were tasked to do all kinds. of, you know, manipulation, walking-. - Driving a vehicle. - driving a car, all these kinds of challenges with, if I remember correctly, sort of some slight capability to communicate with humans,

but the communication was very poor. So, basically it has to be almost entirely autonomous. - It could have periods. where the communication was entirely interrupted, and the robot had to be able to proceed. - [Lex] Yeah. - But you could provide some high-level guidance. to the robot, basically low-bandwidth communications-. - Yeah - to steer it. - I watched that challenge with kind of tears in my eyes. eating popcorn with- - Us, too. (both laughing). - But I wasn't personally losing, you know,

hundreds of thousands, millions of dollars. and many years of incredible, hard work. by some of the most brilliant roboticists in the world. So, that was why the tragic, that's why tears came. (Robert laughs). So, anyway, just looking back to that time, what have you learned from that experience? And maybe if you could describe what it was. sort of the setup for people who haven't seen it. - Well, so there was a contest. where a bunch of different robots were asked. to do a series of tasks, some of those that you mentioned,

drive a vehicle, get out, open a door, go identify a valve, shut a valve, use a tool to maybe cut a hole in a surface. and then crawl over some stairs. and maybe some rough terrain. So, the idea was have a general-purpose robot. that could do lots of different things, had to be mobility, and manipulation, on-board perception.

And there was a contest, which DARPA likes at the time, was running sort. of follow-on to the grand challenge, which was, "Let's try to push vehicle autonomy along.". Right? They encouraged people to build autonomous cars. So, they were trying to basically push an industry forward. Our role in this was to build a humanoid. At the time, it was our sort.

of first-generation Atlas robot, and we built maybe 10 of them, I don't remember the exact number. And DARPA distributed those to various teams that sort. of won a contest, showed that they could, you know, program. these robots and then use them. to compete against each other, and then other robots were introduced as well. Some teams built their own robots. Carnegie Mellon, for example, built their own robot.

And all these robots competed to see who could sort of get. through this maze the fastest. And again, I think the purpose was to kind. of push the whole industry forward. We provided the robot and some baseline software, but we didn't actually compete as a participant. where we were trying to, you know, drive the robot. through this maze. We were just trying to support the other teams. It was humbling because it was really a hard task.

And honestly, the tears were. because, mostly, the robots didn't do it. (laughs). You know, they fell down, you know, repeatedly. It was hard to get through this contest. Some did, and you know, they were rewarded and won. But it was humbling because of just how hard, these tasks weren't all that hard. A person could have done it very easily, but it was really hard to get the robots to do it, you know. And the- - The general nature of it, the variety of it.

- [Robert] The variety. - And also, I don't know if the tasks were (sighs). sort of the task in themselves help us understand. what is difficult and what is not. I don't know if that was obvious. before the contest was designed, so you kind of tried to figure that out. And I think Atlas is really a general robot platform, and it's perhaps not best suited for the specific tasks. of that contest, like just for example, probably the hardest task is not the driving of the car.

but getting in and out of the car. (Robert laughs). And Atlas probably is, you know, if you were. to design a robot that can get into the car easily. and get out easily, you probably would not make Atlas. that particular car. - Yeah, the robot was a little bit big-. - Yeah. - to get in. and out of that car, right? - [Lex] It doesn't fit, yeah. - This is the curse of a general-purpose robot, that they're not perfect at any one thing, but they might be able to do a wide variety of things. And that is the goal at the end of the day.

You know, I think we all wanna build general-purpose robots. that can be used for lots of different activities, but it's hard, and the wisdom. in building successful robots up until this point have been, "Go build a robot for a specific task, and it'll do it very well.". And as long as you control that environment, it'll operate perfectly, but robots need to be able.

to deal with uncertainty. If they're gonna be useful to us in the future, they need to be able to deal with unexpected situations. And that's sort of the goal. of a general-purpose or multipurpose robot. And that's just darn hard. And so, yeah, there was these curious little failures. Like, I remember a robot, you know, the first time you start to try to push on the world. with a robot, you forget that the world pushes back. and will push you over (laughs) if you're not ready for it.

And the robot, you know, reached to grab the door handle. I think it missed the grasp of the door handle, was expecting that its hand was on the door handle, and so when it tried to turn the knob, it just threw itself over. It didn't realize, "Oh, I had missed the door handle. I was expecting a force back from the door. It wasn't there, and then I lost my balance.". And so, these little simple things. that you and I would take totally for granted. and deal with, (laughs) the robots don't know.

how to deal with yet, and so you have to start to deal. with all of those circumstances. (laughs). - Well, I think a lot of us experience this. even when sober but drunk, too. Sort of, you pick up a thing and expect it to be, what is it, heavy, and it turns out to be light. - [Robert] Yeah, and then, "Whoa.". - Oh, yeah, and then, and I'm sure if your depth perception. for whatever reason is screwed up, if you're drunk or some other reason, and then you think you're putting your hand on the table,

and you miss it, I mean it's the same kind of situation. - [Robert] Yeah. - But there's a- - Which is why you need. to be able to predict forward just a little bit, and so. that's where this model-predictive control stuff comes in. Predict forward what you think's gonna happen, and if that does happen, you're in good shape. If something else happens, you better start predicting again. - So, like, regenerate a plan. (Robert laughs). - [Robert] Yeah. - I mean, that also requires a very fast feedback loop.

of updating what your prediction, how it matches to the actual real world. - [Robert] Yeah, those things have to run pretty quickly. - What's the challenge of running things pretty quickly, 1,000 hertz, of acting and sensing quickly? - You know, there's a few different layers of that. At the lowest level, you like to run things typically. at around 1,000 hertz, which means that, you know, at each joint of the robot, you're measuring position. or force and then trying to control your actuator,

whether it's a hydraulic or electric motor trying. to control the force coming out of that actuator. And you wanna do that really fast, something like 1,000 hertz, and that means you can't have. too much calculation going on at that joint. But that's pretty manageable these days, and it's fairly common. And then, there's another layer. that you're probably calculating, you know, maybe at 100 hertz, maybe 10 times slower, which is now starting to look at the overall body motion.

and thinking about the larger physics of the robot. And then, there's yet another loop that's probably happening. a little bit slower, which is where you start to bring, you know, your perception in, your vision, and things like that, and so you need to run all. of these loops sort of simultaneously. You do have to manage your computer time. so that you can squeeze in all the calculations you need. in real time in a very consistent way.

And the amount of calculation we can do is increasing. as computers get better, which means we can start. to do more sophisticated calculations. I can have a more complex model doing my forward prediction, and that might allow me to do even better predictions. as I get better and better. And it used to be, again, you know, 10 years ago, we had to have pretty simple models that we were running,

you know, at those fast rates 'cause the computers weren't. as capable about calculating forward. with a sophisticated model, but as computation gets better, we can do more of that. - What about the actual pipeline of software engineering, how easy it is to keep updating Atlas, like, do continuous development on it? So, how many computers are on there? Is there a nice pipeline? - It's an important part of building a team around it,

which means, you know, you need to also have software tools, simulation tools, you know, so we have always made strong use. of physics-based simulation tools to do some. of this calculation, basically test it in simulation. before you put it on the robot. But you also want the same code that you're running. in simulation to be the same code you're running. on the hardware, and so even getting to the point. where it was the same code going from one to the other,

we probably didn't really get that working. until, you know, several years ago. But you know, that was a bit of a milestone. And so, you wanna certainly work these pipelines. so that you can make it as easy as possible. and have a bunch of people working in parallel. You know, we only have, you know, four of the Atlas robots, the modern Atlas robots at the company, and you know, we probably have, you know, 40 developers there. all trying to gain access to it. And so, you need to share resources.

and use some of the software pipeline. - Well, that's a really exciting step to be able to run. the exact same code in simulation as on the actual robot. How hard is it to do. a realistic simulation, physics-based simulation of Atlas such that, I mean, the dream is like, if it works in simulation, it works perfectly in reality. (Robert laughs). How hard is it to sort of keep working on closing that gap? - The root of some of our physics-based simulation tools.

really started at MIT, and we built. some good physics-based modeling tools there. The early days of the company, we were trying. to develop those tools as a commercial product, so we continued to develop them. It wasn't a particularly successful commercial product, but we ended up. with some nice physics-based simulation tools. so that, when we started doing legged robotics again, we had a really nice tool to work with. And the things we paid attention to were things. that weren't necessarily handled very well.

in the commercial tools you could buy off the shelf, like interaction with the world, like foot-ground contact. And so, trying to model those contact events well. in a way that captured the important parts. of the interaction was a really important element. to get right and to also do in a way. that was computationally feasible and could run fast. 'cause if your simulation runs too slow, you know,

then your developers are sitting around waiting for stuff. to run and compile, and so it's always. about efficient, fast operation as well. So, that's been a big part of it. You know, I think developing those tools in parallel. to the development of the platform and trying. to scale them has really been essential, I'd say, to us being able to assemble a team of people. that could do this. - Yeah, how to simulate contact, period, so foot-ground contact but sort of for manipulation.

because don't you want to model all kinds of surfaces? - Yeah. So, it will be even more complex with manipulation. 'cause there's a lot more going on. (laughs). - Yeah. - You know. And you need to capture, I don't know, things slipping and moving, you know, in your hand. It's a level of complexity that I think goes. above foot-ground contact. when you really start doing dextrous manipulation. So, there's challenges ahead still.

- So, how far are we away from me being able to walk. with Atlas in the sand along the beach. (Robert laughs). and us both drinking a beer? (Robert laughing). - [Robert] Well, I-. - Sip it out of a can, out of a can. - Maybe Atlas could spill his beer. 'cause he's got nowhere to put it. (laughing). Atlas could walk on the sand. - So, can it? - Yeah, yeah. Yeah, I mean, you know, have we really had him. out on the beach? You know, we take them outside often, you know, rocks, hills, that sort of thing, even just around our lab in Waltham.

We probably haven't been on the sand, but I'm-. - So, soft surfaces, normally? - I don't doubt. that we could deal with it. We might have to spend a little bit of time. to sort of make that work. We had to take BigDog to Thailand years ago, and we did this great video. of the robot walking in the sand, walking into the ocean up to, I don't know, its belly. or something like that and then turning around and walking.

out all while playing- - Oh, that's-. - [Robert] some cool beach music. - Yeah. - Great show, but then, you know, we didn't really clean the robot off, and the saltwater was really hard on it, so you know, we put it in a box, shipped it back. By the time it came back, we had some problems (laughing) with corrosion. - It's the salt water. It's not like- - Salt tough (laughs). - It's not, like, sand getting. into the components or something like this. - [Robert] Yeah, yeah. - But I'm sure, if this is a big priority, you can make it like-. - Right. - waterproof it. or something. - Right, right. That just wasn't our goal at the time. - Well, it's a personal goal of mine.

to walk along, (Robert laughs). walk along the beach, but it's a human problem, too. You get sand everywhere. It's just a giant mess. (Robert laughs). So, soft surfaces are okay. So, I mean, can we just linger on the robotics challenge? There's a pile of, like, rubble they had to walk over. How difficult is that task? - In the early days of developing BigDog, the loose rock was the epitome of the hard walking surface.

because you stepped down, and you had these little point feet on the robot, and the rock can roll, and you have to deal. with that last-minute, you know, change. in your foot placement. - Yeah, so you step on the thing, and that thing responds. to you stepping on it. - Yeah, and it moves where your point of support is. And so, that became kinda the essence of the test. And so, that was the beginning of us starting. to build rock piles in our parking lots,

and we would actually build boxes full of rocks. and bring 'em into the lab, and then we would have. the robots walking across these boxes of rocks. because that became the essential test. - So, you mentioned BigDog. Can we maybe take a stroll. through the history of Boston Dynamics? So, what and who is BigDog? By the way, is who, (Robert laughs). do you try not to anthropomorphize the robots?

Do you try to remember that they're, this is like the division I have. 'cause, for me, it's impossible. For me, there's a magic to the being that is a robot. It is not human, but it is, the same magic that a living being has when it moves. about the world is there in the robot. So, I don't know what question I'm asking, but should I say what or who I guess? Who is BigDog? What is BigDog? (Robert laughs). - Well, I'll say to address the meta question,

we don't try to draw hard lines around it being an it, or a him, or a her. It's okay, right? I think part of the magic of these kinds of machines is. by nature of their organic movement, of their dynamics, we tend to want to identify with them. We tend to look at them and sort of attribute maybe feeling. to that because we've only seen things. that move like this that were alive.

And so, this is an opportunity. It means that you could have feelings for a machine, and you know, people have feelings for their cars. You know, they get attracted to 'em, attached to them. So, that's inherently, could be a good thing. as long as we manage what that interaction is. So, we don't put strong boundaries around this. and ultimately think it's a benefit, but it's also, can be a bit of a curse.

because I think people look at these machines, and they attribute a level of intelligence. that the machines don't have. Why? Because, again, they've seen things move like this. that we're living beings, which are intelligent, and so they wanna attribute intelligence to the robots. that isn't appropriate yet, even though they move like an intelligent being. - But you try to acknowledge. that the anthropomorphization is there and try. to, first of all, acknowledge that it's there.

- And have a little fun with it. - And have little fun. - You know, our most recent video, it's just kind of fun, you know, to look at the robot. We started off the video with Atlas kind of looking. around for where the bag of tools was. 'cause the guy up on the scaffolding says, "Send me some tools.". Atlas has to kinda look around and see where they are. And there's a little personality there that is fun. It's entertaining. It makes our jobs interesting.

and I think in the long run can enhance interaction. between humans and robots in a way. that isn't available to machines that don't move that way. - This is something to me personally is very interesting. I happen to have a lot of legged robots. (both laughing). I hope to have a lot of Spots in my possession. I'm interested in celebrating robotics. and celebrating companies, companies. that do incredible stuff like Boston Dynamics.

You know, I'm a little crazy, and you say you don't want to, you want to align, you wanna help the company. 'cause I ultimately want a company. like Boston Dynamics to succeed. And part of that we'll talk about, you know, success kind of requires making money. And so, the kinda stuff I'm particularly interested. in may not be the thing that makes money in the short term. I can make an argument that will in the long term. But the kinda stuff I've been playing with is a robust way.

of having the quadrupeds, the robot dogs communicate emotion. with their body movement-. - Hmm. - the same kinda stuff. you do with a dog-. - Yeah. - but not hard coded, but in a robust way- - Mm-hmm. - and be able to communicate excitement, or fear-. - Mm-hmm. - boredom, all these kinds of stuff. And I think as a base layer of function, of behavior. to add on top of a robot, I think that's. a really powerful way.

to make the robot more usable for humans, for whatever application. - I think it's gonna be. really important, and it's a thing we're beginning. to pay attention to. A differentiator for the company has always been. we really want the robot to work. We want it to be useful. Making it work at first meant. the legged locomotion really works. It can really get around, and it doesn't fall down. But beyond that, now it needs to be a useful tool.

And our customers are, for example, factory owners, people who are running a process-manufacturing facility. And the robot needs to be able to get. through this complex facility in a reliable way, you know, taking measurements. We need for people who are operating those robots. to understand what the robots are doing. If the robot needs help or, you know, is in trouble. or something, it needs to be able to communicate.

and a physical indication of some sort. so that a person looks at the robot and goes, "Oh, I know what that robot's doing. That robot's going to go take measurements. of my vacuum pump with its thermal camera.". You know, you wanna be able to indicate that, or even just the robot's about to turn, you know, in front of you and maybe indicate (laughs). that it's going to turn. And so, you sort of see and can anticipate its motion. So, this kind of communication is going.

to become more and more important. It wasn't sort of our starting point, you know, but now the robots are really out in the world, and you know, we have about 1,000 of 'em. out with customers right now. This layer of physical indication, I think, is gonna become. more and more important. - We'll talk about where it goes. 'cause there's a lot of interesting possibilities. But if you can return back to the origins of Boston Dynamics. with the more research, the R&D side,

before we talk about how to build robots at scale. So, BigDog. What's- - So-. - Who's BigDog? - So, the company started in 1992, and in probably 2003, I believe, is when we took a contract from, so, basically, 10 years, 11 years we weren't doing robotics.

We did a little bit of robotics with Sony. They had Aibo. They had their Aibo robot. We were developing some software for that. That kinda got us a little bit involved. with robotics again. Then, there was this opportunity to do a DARPA contract. where they wanted to build a robot dog. And we won a contract to build that. And so, that was the genesis of BigDog, and it was a quadruped, and it was the first time. we built a robot that had everything on board. You could actually take the robot.

out into the wild and operate it. So, it had an onboard power plan. It had onboard computers. It had hydraulic actuators that needed to be cooled. So, we had cooling systems built in, everything integrated into the robot. And that was a pretty rough start, right? It was 10 years that we were not a robotics company. We were a simulation company, and then we had. to build a robot in about a year. So, that was a little bit of a rough transition. (Lex laughs). (Robert laughs).

- I mean, can you just comment on the roughness. of that transition? 'Cause BigDog, I mean, this is this big quadruped, four legs robot. - We built a few different versions of them, but the very earliest ones, you know, didn't work very well, (laughs) and we would take 'em out, and it was hard to get, you know, a go-kart engine driving a hydraulic-. - Oh, is that what it was? (Robert laughs). I was- - And you know, having that all work while trying to get,

you know, the robot to stabilize itself, and so- - So. what was the power plan? What was the engine? It seemed like my vague recollection, (laughs). I don't know. It felt very loud, and aggressive, and kind of thrown together is what it kind of-. - Oh, it absolutely was, right? We weren't trying to design the best robot hardware. at the time, and we wanted to buy an off-the-shelf engine. And so, many of the early versions of BigDog had literally.

go-kart engines or something like that. Usually, it- - It was gas powered? - Yeah, a gas-powered two-stroke engine. (Lex laughs). And the reason why it was two stroke is. two-stroke engines are lighter weight, and we generally didn't put mufflers on them. 'cause we're trying to save the weight, and we didn't care about the noise. (laughing) And some of these things were horribly loud, but we're trying to manage weight because managing weight. in a legged robot is always important. because it has to carry everything. - That said, that thing was big-.

- Well- - what I've seen. the videos of. - Yeah. I mean, the early versions, you know, stood. about, I don't know, belly high, chest high. You know, they probably weighed maybe a couple. of hundred pounds, but you know, over the course. of probably five years, we were able to get that robot. to really manage a remarkable level of rough terrain. So, you know, we started out with just walking on the flat, and then we started walking on rocks, and then inclines,

and then mud, and then slippery mud. And you know, by the end of that program, we were convinced. that legged locomotion in a robot could actually work. 'cause you know, going into it, we didn't know that. We had built quadrupeds at MIT, but they used a giant hydraulic pump, you know, in the lab. They used a giant computer that was in the lab. They were always tethered to the lab. This was the first time something that was sort. of self-contained, you know, walked around in the world.

and balanced, and the purpose was to prove to ourself. that the legged locomotion could really work. And so, BigDog really cut that open for us. And it was the beginning of what became. a whole series of robots. So, once we showed to DARPA that you could make. a legged robot that could work, there was a period at DARPA. where robotics got really hot, and there was lots. of different programs, and you know, we were able. to build other robots. We built other quadrupeds like LS3 designed.

to carry heavy loads. We built Cheetah, which was designed to explore, what are the limits to how fast you can run? You know, we began to build sort of a portfolio. of machines and software that let us build. not just one robot, but a whole family of robots. - So, push the limits in all kinds of directions. in terms- - Yeah, and to discover those principles. You know, you asked earlier. about the art and science of legged locomotion. We were able to develop principles of legged locomotion.

so that we knew how to build. a small legged robot or a big one. So, leg length, you know, was now a parameter. that we could play with. Payload was a parameter we could play with. So, we built the LS3, which was an 800-pound robot. designed to carry a 400-pound payload. And we learned the design rules, basically developed the design rules. How do you scale different robot systems. to, you know, their terrain, to their walking speed,

to their payload? - So, when was Spot born? - Around 2012 or so, so, again, almost 10 years into sort of a run with DARPA. where we built a bunch of different quadrupeds. We had sort of a different thread. where we started building humanoids. We saw that probably an end was coming. where the government was gonna kind of back off. from a lot of robotics investment.

And in order to maintain progress, we just deduced. that, "Well, we probably need to sell ourselves. to somebody who wants to continue to invest in this area,". and that was Google. And so, at Google, we would meet regularly with Larry Page, and Larry just started asking us, you know, "What's your product gonna be?". And you know, the logical thing, the thing. that we had the most history with that we wanted.

to continue developing was a quadruped. But we knew it needed to be smaller. We knew it couldn't have a gas engine. We thought it probably couldn't be hydraulically actuated. So, that began the process of exploring if we could migrate. to a smaller, electrically actuated robot. And that was really the genesis of Spot. - So, not a gas engine, and the actuators are electric. - [Robert] Yes. - So, can you maybe comment on what it's like.

at Google working with Larry Page, having those meetings, and thinking. of what will a robot look like that could be built at scale, like, starting to think about a product? - Larry always liked the toothbrush test. He wanted products that you used every day. What they really wanted was, you know, a consumer-level product, something that would work in your house.

We didn't think that was the right next thing to do, because to be a consumer-level product, cost is gonna be very important. Probably needed to cost a few thousand dollars. And we were building these machines. that cost hundreds of thousands of dollars, maybe a million dollars to build. And of course, we were only building, like, two, but we didn't see how to get all the way. to this consumer-level product-. - In a short amount of time. - In a short amount of time.

And he suggested that we make the robots really inexpensive, and part of our philosophy has always been. build the best hardware you can. Make the machine operate well so that you're trying. to solve, you know, discover the hard problem. that you don't know about. Don't make it harder. by building a crappy machine, basically. Build the best machine you can. There's plenty of hard problems to solve. that are gonna have to do. with, you know, underactuated systems and balance.

And so, we wanted to build. these high-quality machines still, and we thought that was important for us. to continue learning about really what was. the important parts that make robots work. And so, there was a little bit. of a philosophical difference there. And so, ultimately that's why we're building robots. for the industrial sector now. because the industry can afford a more expensive machine. because, you know, their productivity depends.

on keeping their factory going. And so, if Spot costs, you know, $100,000 or more, that's not such a big expense to them, whereas at the consumer level, no one's gonna buy a robot like that. And I think we might eventually get. to a consumer-level product that will be that cheap, but I think the path to get in there needs to go. through these really nice machines. so that we can then learn how to simplify. - So, what can you say to almost the engineering challenge.

of bringing down cost of a robot. so that, presumably, when you try to build a robot at scale, that also comes into play when you're trying to make money. on a robot even in the industrial setting? But how interesting, how challenging of a thing is that, in particular probably new. to an R&D company? (Robert laughs). - Yeah, I'm glad you brought that last part up. The transition from an R&D company to a commercial company,

that's the thing you worry about, you know, 'cause you've got these engineers who love hard problems, who wanna figure out how to make robots work. And you don't know if you have engineers that wanna work. on the quality, and reliability, and cost. that is ultimately required. And indeed, you know, we have brought on a lot of new people. who are inspired by those problems. But the big takeaway lesson for me is. we have good people. We have engineers who wanna solve problems,

and the quality, and cost, and manufacturability is. just another kind of problem. And because they're so invested in what we're doing, they're interested in and will go work. on those problems as well. And so, I think we're managing that transition very well. In fact, I'm really pleased that, I mean, it's a huge undertaking by the way, right? So, you know, to get reliability to where it needs to be, we have to have fleets of robots.

that we're just operating 24/7 in our offices. to go find those rare failures and eliminate them. It's just a totally different kind of activity. than the research activity where you get it to work, you know, the one robot you have to work. in a repeatable way, (laughs) you know, at the high-stakes demo. It's just very different. But I think we're making remarkable progress, I guess. - So, one of the cool things, I got a chance. to visit Boston Dynamics, and I mean,

one of the things that's really cool is. to see a large number of robots moving about. because I think one of the things you notice. in the research environment at MIT, for example, I don't think anyone ever has a working robot. for a prolonged period of time. - (laughing) Exactly. - So, like, most robots are just sitting there. in a sad state of despair. waiting to be born, (Robert laughs). brought to life for a brief moment of time. I just remember there's a Spot robot just had,

like, a cowboy hat on and was just walking randomly. for whatever reason. I don't even know, but there's a kind of a sense. of sentience to it because it doesn't seem. like anybody was (laughing) supervising it. - Well- - It was just doing. its thing. - I'm gonna stop. way short of the sentience. - Sure. - It is the case. that, if you come to our office, you know, today. and walk around the hallways, you're gonna see a dozen robots just kind of walking around-. - Yes. - all the time. And that's really a reliability test for us.

So, we have these robots programmed. to do autonomous missions, get up off their charging dock, walk around the building, collect data. at a few different places, and go sit back down. And we want that to be a very reliable process. 'cause that's what somebody who's running a brewery, a factory, that's what they need the robot to do, and so we have to dog-food our own robot. We have to test it in that way. And so, on a weekly basis, we have robots that are accruing.

something like 1,500 or maybe 2,000 kilometers of walking. and you know, over 1,000 hours of operation every week. And that's something that I don't think anybody else. in the world can do 'cause, A, you have to have a fleet. of robots to just accrue that much information. (laughing). You have to be willing to dedicate it to that test. But that's essential. - [Lex] That's how you get the reliability. - That's how you get it.

- What about some of the cost cutting. from the manufacturer's side? What have you learned from the manufacturer's side. of the transition from R&D to-. - And we're still learning a lot there. We're learning how to cast parts. instead of mill it all out of, you know, billet aluminum. We're learning how to get plastic molded parts, and we're learning about how to control that process. (laughs) so that you can build. the same robot twice in a row. There's a lot to learn there. And we're only partway through that process.

We've set up a manufacturing facility in Waltham. It's about a mile from our headquarters, and we're doing final assembly and tests. of both Spots and Stretches, you know, at that factory. And it's hard because, to be honest, we're still iterating. on the design of the robot. As we find failures from these reliability tests, we need to go engineer changes, and those changes need. to now be propagated to the manufacturing line. And that's a hard process,

especially when you wanna move as fast as we do. And that's been challenging. You know, the folks who are working supply chain. who are trying to get the cheapest parts for us, kind. of requires that you buy a lot of 'em to make 'em cheap, and then we go change the design from underneath 'em, and they're like, "What are you doing?". And so, you know, getting everybody on the same page here. that, yep, we still need to move fast, but we also need to try to figure out how to reduce cost, that's one of the challenges.

of this migration we're going through. - And over the past few years, challenges to the supply chain, I mean, I imagine you've been a part. of a bunch of stressful meetings. - Yeah, things got more expensive and harder to get, and yeah, so it's all been a challenge. - Is there still room for simplification? - Oh, yeah, much more, and you know, these are. really just the first generation of these machines. We're already thinking about what the next generation. of Spot's gonna look like. Spot was built as a platform, so you could put. almost any sensor on it.

You know, we provided data communications, mechanical connections, power connections. But for example, in the applications that we're excited. about where you're monitoring these factories. for their health, there's probably a simpler machine. that we could build that's really focused on that use case. And that's the difference. between the general-purpose machine or the platform. versus the purpose-built machine. And so, even though even in the factory.

we'd still like the robot to do lots of different tasks, if we really knew on day one that we're gonna be operating. in a factory with these three sensors in it, we would have it all integrated in a package. that would be easier, less expensive, and more reliable. So, we're contemplating building, you know, a next generation of that machine. - So, we should mention that, so Spot for people who somehow are not familiar, is. a yellow, robotic dog.

and has been featured in many dance videos. It also has gained an arm. So, what can you say about the arm that Spot has, about the challenges of this design, and the manufacturer of it? - We think the future of mobile robots is. mobile manipulation. You know, in the past 10 years, it was getting mobility to work, getting the legged locomotion to work. If you ask, what's the hard problem in the next 10 years,

it's getting a mobile robot. to do useful manipulation for you. And so, we wanted Spot to have an arm. to experiment with those problems. And the arm is almost as complex as the robot itself, you know, and it's an attachable payload. It has, you know, several motors, and actuators, and sensors. It has a camera in the end of its hand, so you know, you can sort of see something,

and the robot will control the motion of its hand. to go pick it up autonomously. So, in the same way the robot walks and balances, managing its own foot placement to stay balanced, we want manipulation to be mostly autonomous, where the robot, you indicate, "Okay, go grab that bottle,". and then the robot will just go do it using the camera. in its hand and then sort of closing in on the grasp. But it's a whole nother complex robot. on top of a complex legged robot.

And of course, we made the hand look a little like a head, (laughs) you know, because again, we want it. to be sort of identifiable. In the last year, a lot of our sales have been people. who already have a robot now buying an arm. to add to that robot. - Oh, interesting. And so, the arm is for sale? - [Robert] Oh, yeah, oh, yeah. It's an option. - What's the interface like to work with the arm?

I could just ask that question in general. about robots from Boston Dynamics. Is it designed to be easily. and efficiently operated remotely by a human being? Or, is there also the capability to push towards autonomy? - We want both. In the next version of the software that we release, which will be version 3.3, we're gonna offer the ability,

if you have a autonomous mission for the robot, we're gonna include the option that it can go. through a door, which means it's gonna have to have an arm, and it's gonna have to use that arm to open the door. And so, that'll be an autonomous manipulation task. that you can program easily with the robot-. - Oh. - strictly through, you know, we have a tablet interface, and so on the tablet, you know, you sort of see the view that Spot sees. You say, "There's the door handle. You know, the hinges are on the left, and it opens in.

The rest is up to you. Take care of it." - Oh, wow. So, it just takes care of everything? - Yeah. So, and for a task like opening doors, you can automate most of that. And we've automated a few other tasks. We had a customer who had. a high-powered breaker switch, essentially. It's an electric utility, Ontario Power Generation. And when they're gonna disconnect, you know, their power supply, right, that could be a gas generator,

could be a nuclear power plant, you know, from the grid, you have to disconnect this breaker switch. Well, as you can imagine, there's, you know, hundreds or thousands of amps and volts (laughing) involved. in this breaker switch. And it's a dangerous event 'cause occasionally you'll get. what's called an arc flash. As you just do this disconnect, the power, the sparks jump across, and people die doing this. And so, Ontario Power Generation used our Spot and the arm. through the interface to operate this disconnect-.

- That's great. - in an interactive way. And they showed it to us, and we were so excited about it. and said, "You know, I bet we can automate that task.". And so, we got some examples of that breaker switch, and I believe in the next generation of the software. now we're gonna deliver back to Ontario Power Generation, they're gonna be able to just point the robot. at that breaker. They'll indicate, "That's the switch.". There's sort of two actions you have to do.

You have to flip up this little cover, press a button, then get a ratchet, stick it into a socket, and literally unscrew this giant breaker switch. So, there's a bunch of different tasks, and we basically automated them so that the human says, "Okay, there's the switch. Go do that part. That right there is the socket. where you're gonna put your tool, and you're gonna open it up.". And so you can remotely sort of indicate this. on the tablet, and then the robot just does. everything in between.

- And it does everything, all the coordinated movement. of all the different actuators that includes the body. and the arm. - Yeah, maintains its balance. It walks itself, you know, into position. so it's within reach, and the arm is in a position. where it can do the whole task. So, it manages the whole body. - So, how does one become a big enough customer. to request features? 'Cause I personally want a robot that gets me a beer. (Robert laughs). I mean, that has to be, like, one of the most, I suppose, in the industrial setting,

that's a non-alcoholic beverage. of picking up objects and bringing the objects to you. - We love working with customers. who have challenging problems like this. and this one in particular because we felt. like what they were doing, A, it was a safety feature. B, we saw that the robot could do it. 'cause they teleoperated it the first time. Probably took 'em an hour to do it the first time, right? But the robot was clearly capable, and we thought, "Oh, this is a great problem for us to work on.

to figure out how to automate a manipulation task.". And so, we took it on not because we were gonna make a bunch. of money from it in selling the robot back to them. but because it motivated us to go solve what we saw. as the next logical step. But many of our customers, in fact, our bigger customers, typically ones. who are gonna run a utility, or a factory, or something like that, we take that kind of direction. from them, especially if they're gonna buy 10, or 20, or 30 robots, and they say,

"I really need it to do this," well, that's exactly. the right kind of problem that we wanna be working on. - Mm-hmm. - Yeah, and so-. - Note to self, "Buy 10 Spots, (Robert laughs). and aggressively push for beer manipulation.". (Robert laughs). I think it's fair to say it's notoriously difficult. to make a lot of money as a robotics company. How can you make money as a robotics company? Can you speak to that? It seems that a lot of robotics companies fail.

It's difficult to build robots. It's difficult to build robots at a low enough cost. where customers, even in the industrial setting, want. to purchase them, and it's difficult to build robots. that are useful, sufficiently useful. - [Robert] Yeah. - So, what can you speak to? And Boston Dynamics has been successful for many years. of finding a way to make money. - Well, in the early days, of course, you know, the money we made was from doing contract R&D work, and we made money, but you know, we weren't growing, and we weren't selling a product.

And then, we went through several owners. who, you know, had a vision of not only developing. advanced technology, but eventually developing products. And so, both, you know, Google, and SoftBank, and now Hyundai, you know, had that vision and were willing. to, you know, provide that investment. Now, our discipline is that we need to go find applications. that are broad enough that you could imagine selling.

thousands of robots because it doesn't work. if you don't sell thousands or tens of thousands of robots. If you only sell hundreds, you will commercially fail. And that's where most. of the small robot companies have died. And that's a challenge because, you know, A, you need to field the robots. They need to start to become reliable, and as we've said, that takes time and investment to get there. And so, it really does take.

visionary investment to get there. But we believe that we are going to make money. in this industrial monitoring space. because, you know, if a chip fab, if the line goes down. because a vacuum pump failed someplace, that can be a very expensive process. It can be a million dollars a day in lost production, maybe you have to throw away some. of the product along the way, and so the robot, if you can prevent that.

by inspecting the factory every single day, maybe every hour if you have to, there's a real return on investment there. But there needs to be a critical mass of this task. And we're focusing on a few that we believe are ubiquitous. in the industrial production environment. And that's using a thermal camera to keep things. from overheating, using an acoustic imager. to find compressed air leaks, using visual cameras.

to read gauges, measuring vibration. These are standard things that you do. to prevent unintended shutdown of a factory. And this takes place in a beer factory. We're working with AB InBev. It takes place in chip fabs. You know, we're working with GlobalFoundries. It takes place in electric utilities. and nuclear power plants. And so, the same robot can be applied. in all of these industries.

And as I said, we have. about, actually it's 1,100 Spots out now. To really get, you know, profitability, we need to be. at 1,000 a year, maybe 1,500 a year, you know, for that sort of part of the business. So, it still needs to grow, but we're on a good path. So, I think that's totally achievable. - So, the application should require crossing. that 1,000-robot barrier. - It really should, yeah. I wanna mention, you know, our second robot, Stretch.

- Yeah, tell me about Stretch. What's Stretch? Who is Stretch? - Stretch started differently than Spot. You know, Spot we built because we had decades. of experience building quadrupeds. We had it in our blood. We had to build a quadruped product, but we had to go figure out what the application was, and we actually discovered this factory-patrol application, basically preventative maintenance. by seeing what our customers did with it. Stretch was very different. We started knowing.

that there was warehouses all over the world. There's shipping containers moving all around the world full. of boxes that are mostly being moved by hand. By some estimates, we think there's a trillion boxes, (laughs) cardboard boxes shipped around the world each year. And a lot of it's done manually. It became clear early on that there was an opportunity. for a mobile robot in here to move boxes around. And the commercial experience has been very different. between Stretch and with Spot.

As soon as we started talking to people, potential customers. about what Stretch was gonna be used for, they immediately started saying, "Oh, I'll buy that robot. You know, in fact, I'm gonna put in an order. for 20 right now.". We just started shipping the robot in January. after, you know, several years of development. - [Lex] Of this year? - Of this year. So, our first deliveries of Stretch to customers were. DHL and Maersk in January. We're delivering to Gap right now, and we have. about seven or eight other customers,

all who've already agreed in advance to buy. between 10 and 20 robots, and so we've already got commitments. for, you know, a couple hundred of these robots. This one's gonna go, right? It's so obvious that there's a need, and we're not just gonna unload trucks. We're gonna do any box-moving task in the warehouse. And so, it too will be a multipurpose robot, and we'll eventually have it doing palletizing, or depalletizing, or loading trucks, or unloading trucks. There's definitely thousands of robots.

There's probably tens of thousands of robots. of this in the future, so it's gonna be profitable. - Can you describe what Stretch looks like? - It looks like a big, strong robot arm on a mobile base. The base is about the size of a pallet, and we wanted it to be the size of a pallet. because that's what lives in warehouses, right, pallets of goods sitting everywhere, so it needed to be able to fit in that space. - [Lex] It's not a legged robot. - It's not a legged robot. So, it was our first, it was actually a bit.

of a commitment from us, a challenge for us to build a non-balancing robot. (Lex laughs). - To do the much easier problem but to do it well. - Well, because, you know, it wasn't gonna have this balance problem. And in fact, the very first version. of the logistics robot we built was a balancing robot, and that's called Handle. And there's- - That thing was epic. - Oh, it's a beautiful machine. - It's an incredible machine. (Robert laughs). (Lex laughs).

I mean, it looks epic. It looks like, I mean, out of a sci-fi movie of some sorts. I mean, can you actually just linger. on, like, the design of that thing? 'Cause that's another leap. into something you probably haven't done. It's a different kind of balancing. - Yeah, so let me- - It's wild. - I love talking about the history of how Handle came about. (Lex laughs). because it connects all of our robots, actually. So, I'm gonna start with Atlas. When we had Atlas getting fairly far along, we wanted to understand, I was telling you earlier,

the challenge of the human form is. that you have this mass up high, and balancing that inertia, that mass up high is its own unique challenge. And so, we started trying to get Atlas. to balance standing on one foot, like on a balance beam using its arms like this, and you know, you can do this, I'm sure. I can do this, right? Like, if you're walking a tightrope, how do you do that balance? So, that's sort of, you know, controlling the inertia, controlling the momentum of the robot.

We were starting to figure that out on Atlas. And so, our first concept of Handle, which was a robot that was gonna be on two wheels, so it had to balance, but it was gonna have a big, long arm. so it could reach a box at the top of a truck, and it needed yet another counterbalance, a big tail, to help it balance while it was using its arm. So, the reason why this robot sort of looks epic,

some people said it looked like an ostrich. or maybe, you know, an ostrich moving around, was. the wheels, the leg. It has legs, so it can extend its legs. So, it's wheels on legs. We always wanted to build wheels on legs. It had a tail, and it had this arm, and they're all moving simultaneously and in coordination. to maintain balance because we had figured. out the mathematics of doing this momentum control, how to maintain that balance. And so, part of the reason. why we built this two-legged robot was.

we had figured this thing out. We wanted to see it in this kind of machine, and we thought maybe this kind of machine would be good. in a warehouse, and so we built it. And it's a beautiful machine. It moves in a graceful way like nothing else we've built. But it wasn't the right machine for a logistics application. We decided it was too slow. and couldn't pick boxes fast enough, basically. - Oh. - And it-. - Do it beautifully. with elegance. - It did beautifully, but it just wasn't efficient enough. - [Lex] Aw. - So, we let it go.

- [Lex] Yeah. - But I think we'll come back to that machine eventually. - The fact that it's possible, the fact that you showed. that you could do so many things at the same time. in coordination and so beautifully, there's something there. - [Robert] Yeah. - That was a demonstration of what is possible. - Basically, we made a hard decision, and this was really kind of a hard-nosed business decision. It indicated us not doing it just for the beauty. of the mathematics or the curiosity, but no, we actually need to build a business. that can make money in the long run.

And so, we ended up building Stretch, which has a big, heavy base with a giant battery in the base. of it that allows it to run for two shifts, 16 hours worth of operation. And that big battery sort of helps it stay balanced, right? So, it can move a 50-pound box around with its arm. and not tip over it. It's omnidirectional, it can move in any direction, and it has a nice suspension. built into it so it can deal with, you know, gaps.

or things on the floor and roll over it. But it's not a balancing robot. It's a mobile robot arm that can work to carry, or pick, or place a box up to 50 pounds anywhere in the warehouse. - Take a box from point A to point B anywhere. - Yeah, palletize, depalletize. We're starting with unloading trucks. because there's so many trucks and containers. where goods are shipped, and it's a brutal job. You know, in the summer, it can be 120 degrees. inside that container. People don't wanna do that job,

and it's backbreaking labor, right? Again, these can be up to 50-pound boxes. And so, we feel like this is a productivity enhancer, and for the people who used to do that job unloading trucks, they're actually operating the robot now. And so, by building robots that are easy to control, and it doesn't take an advanced degree to manage, you can become a robot operator. And so, as we've introduced these robots.

to both DHL, and Maersk, and Gap, the warehouse workers. who were doing that manual labor are. now the robot operators, and so we see this. as ultimately a benefit to them as well. - Can you say how much Stretch costs? - Not yet, but I will say that, when we engage. with our customers, they'll be able to see a return. on investment in typically two years. - Okay, so that's something that you're constantly thinking. about, how-. - [Robert] Yeah.

- And I suppose you have to do the same kind. of thinking with Spot. So- - Yes. - it seems like with Stretch the application is, like, directly obvious. - [Robert] Yeah, it's a slam dunk. - Yeah, and so you have a little more flexibility. - Well, I think we know the target. We know what we're going after. - [Lex] Yeah. - And with Spot, it took us a while to figure. out what we were going after. - Well, let me return to that question. about maybe the conversation you were having. a while ago with Larry Page, maybe looking. to the longer future of social robotics, of using Spot.

to connect with human beings perhaps in the home. Do you see a future there. if we were to sort of hypothesize or dream about a future. where Spot-like robots are in the home as pets, a social robot? - We definitely think. about it, and we would like to get there. We think the pathway to getting there is, you know, likely through these industrial applications. and then mass manufacturing, you know. Let's figure out how to build the robots,

how to make the software so that they can really do. a broad set of skills. That's going to take real investment to get there. Performance first, right? A principle of the company has always been. really make the robots do useful stuff. And so, you know, the social robot companies. that try to start someplace else. by just making a cute interaction, mostly they haven't survived. And so, we think the utility really needs to come first,

and that means you have to solve some. of these hard problems. And so, to get there, we're gonna go through the design. and software development in industrial, and then that's eventually gonna let you reach a scale. that could then be addressed to a commercial, a consumer-level market, and so, yeah, maybe we'll be able. to build a smaller Spot with an arm. that could really go get your beer for you. - Mm-hmm. - But there's things we need to figure out still,

how to safely, really safely, and if you're gonna be interacting with children, you better be safe. (laughs). And right now, we count on a little bit. of standoff distance between the robot and people. so that you don't pinch a finger, you know, in the robot. So, you've got a lot of things you need to go solve. before you jump to that consumer-level product. - Well, there's a kind of trade off in safety. because it feels like, in the home, you can fall. Like, you don't have to be as good.

Like, you're allowed to fail in different ways, in more ways as long as it's safe for the humans. So, it just feels like an easier problem to solve. 'cause it feels like, in the factory, you're not allowed to fail. - That may be true, but I also think the variety. of things a consumer-level robot would be expected. to do will also be quite broad. - [Lex] Yeah. - And they're gonna want to get the beer. and know the difference between the beer and a Coca-Cola.

or my snack. You know, they're all gonna want you to clean up the dishes, you know, from the table without breaking 'em. (laughs). Those are pretty complex tasks, and so there's still work to be done there. - So, to push back on that, here's what application. I think that'll be very interesting. I think the application of being a pet, a friend, so, like, no tasks. Just be cute, not cute, not cute.

A dog is more than just cute. A dog is a friend, is a companion. There's something about just having interacted with them. And maybe 'cause I'm hanging out alone. with robot dogs a little too much, but, like, there's a connection there. And it feels like that connection should not be disregarded. You- - No. It should not be disregarded. Robots that can somehow communicate. through their physical gestures. you're gonna be more attached to in the long run.

Do you remember Aibo- - Mm-hmm. - the Sony Aibo? - Yep. - They sold. over 100,000 of those, maybe 150,000, you know, what probably wasn't considered. a successful product for them. They suspended that eventually, and then they brought it back. Sony brought it back, and people definitely, you know, treated this. as a pet, as a companion. And I think that will come around again. Will you get away without having any other utility?

Maybe in a world where we can really talk. to our simple little pet because, you know, ChatGPT. or some other generative AI has made it possible. for you to really talk in what seems like a meaningful way. Maybe that'll open the social robot up again. That's probably not a path we're gonna go down. because, again, we're so focused on performance and utility. We can add those other things also,

but we really wanna start. from that foundation of utility, I think. - Yeah. But I also wanna predict that you're wrong on that, which is that the very path you're taking, which is creating a great robot platform, will very easily take a leap to adding. a ChatGPT-like capability, maybe GPT 5. And there's just so many open-source alternatives. that you could just plop down on top of Spot. And because you have this robust platform,

and you're figuring out how to mass-manufacture it, and how to drive the cost down, and how to make it, you know, reliable, all those kinds of things, it'll be the natural transition. to where just adding ChatGPT. on top of it could- - Oh, I do think. that being able to verbally converse. or even converse through gestures, you know, part of these learning models is. that, you know, you can now look at video and imagery. and associate, you know, intent with that.

Those will all help in the communication. between robots and people, for sure. And that's gonna happen obviously more quickly. than any of us were expecting. (laughs). - I mean, what else do you want from life? A friend to get you a beer (Robert laughs). and then just talk shit about the state of the world. (Robert laughs). I mean, there's a deep loneliness within all of us. And I think a beer and a good chat solves so much of it. or takes us a long way to solving.

a lot of it. - It'll be interesting. to see, you know, when a generative AI can give you. that warm feeling that you connected, you know, and that, "Oh, yeah, you remember me. You're my friend. You know, we have a history.". You know, that history matters, right? - [Lex] Memory of joint, like-. - Memory of, yeah. (laughs). - Having witnessed, that's what friendship, that's what connection, that's what love is. In many cases, some of the deepest friendships you have is.

having gone through a difficult time together-. - Mm-hmm. - and having a shared memory. of an amazing time or a difficult time. and kind of that memory creating this, like, foundation. based on which you can then experience the world together. The silly, the mundane stuff of day to day is somehow built. on a foundation of having gone. through some shit in the past. And the current systems are not personalized in that way. but- - Right. - I think that's a technical problem, not some kind of fundamental limitation,

so combine that with an embodied robot like Spot, which already has magic in its movement, I think it's a very interesting possibility. of where that takes us. But of course, you have to build that on top of a company. that's making money with real applications, with real customers, and with robots that are safe, and work, and reliable, and manufactured at scale. - And I think we're in a unique position in that.

because of, you know, our investors, primarily Hyundai, but also SoftBank still owns 20% of us. They're not totally fixated on driving us to profitability. as soon as possible. That's not the goal. The goal really is a longer-term vision of creating, you know, what does mobility mean in the future? How is this mobile robot technology going to influence us, and can we shape that?

And they want both. And so, we as a company are trying to strike that balance. between, "Let's build a business that makes money.". I've been describing that. to my own team as self-destination. If I wanna drive my own ship, we need to have a business. that's profitable in the end. Otherwise, somebody else is gonna drive the ship for us. So, that's really important. But we're gonna retain the aspiration that we're gonna build. the next generation of technology at the same time.

And the real trick will be if we can do both. - Speaking of ships, let me ask you about a competitor. and somebody who's become a friend. So, Elon Musk and Tesla have announced they've been. in the early days of building a humanoid robot. How does that change the landscape of your work? So, from an outside perspective, it seems like, well, as a fan of robotics, it just seems exciting.

- Right, very exciting, right? When Elon speaks, people listen. And so, it suddenly brought a bright light onto the work. that we'd been doing, you know, for over a decade. And I think that's only gonna help. And in fact, what we've seen is that, in addition to Tesla, we're seeing a proliferation of robotic companies arise now.

- Including humanoid? - [Robert] Yes. - Oh, wow. - Yeah. And interestingly, many of them. as they're, you know, raising money, for example, will claim whether or not they have. a former Boston Dynamics employee. on their staff as a criteria. (both laughing). - Yeah, that's true. I would do that as a company, yeah, for sure. - Yeah, so- - Shows you're legit, yeah. - Yeah, so, you know, (Lex laughs). it has brung a tremendous validation. to what we're doing and excitement.

Competitive juices are flowing, you know, the whole thing. So, it's all good. - Elon has also kind of stated. that, you know, maybe he implied that the problem is solvable. in the near term, which is a low-cost humanoid robot. that's a relatively general use case robot.

So, I think Elon is known for sort of setting these kinds. of incredibly ambitious goals, maybe missing deadlines. but actually pushing not just the particular team he leads. but the entire world to, like, accomplishing those. Do you see Boston Dynamics in the near future being pushed. in that kind of way, like this excitement of competition. kinda pushing Atlas maybe to do more cool stuff,

trying to drive the cost of Atlas down perhaps? I mean, I guess I wanna ask if there's some kind. of exciting energy in Boston Dynamics. due to this little bit of competition. - Oh, yeah, definitely. When we released our most recent video of Atlas, you know, I think you had seen it, the scaffolding. and throwing the box of tools around.

and then doing the flip at the end, we were trying to show the world. that not only can we do this parkour mobility thing, but we can pick up and move heavy things. because, if you're gonna work. in a manufacturing environment, that's what you gotta be able to do. And for the reasons I explained to you earlier, it's not trivial to do so, you know, changing the center of mass, you know, by picking up a 50-pound block, you know, for a robot that weighs 150 pounds,

that's a lot to accommodate. So, we're trying to show that we can do that, so it's totally been energizing. You know, we see the next phase of Atlas being. more dextrous hands that can manipulate and grab more things. that we're gonna start by moving big things around. that are heavy and that affect balance. And why is that? Well, really tiny dextrous things probably are gonna be hard.

for a while yet, you know. Maybe you could go build a special-purpose robot arm, you know, for stuffing, you know, chips. into electronics boards, but we don't really wanna. really fine work like that. I think more course work where you're using two hands. to pick up and balance an unwieldy thing. maybe in a manufacturing environment, maybe in a construction environment, those are the things that we think robots are gonna be able. to do with the level of dexterity that they're gonna have.

in the next few years, and that's where we're headed. And you know, Elon has seen the same thing, right? He's talking about using the robots. in a manufacturing environment. We think there's something very interesting there. about having a two-armed robot. because, when you have two arms, you can transfer a thing. from one hand to the other. You can turn it around. You know, you can reorient it in a way that you can't do it. if you just have one hand on it, and so there's a lot. that extra arm brings to the table. - So, I think in terms of mission,

you mentioned Boston Dynamics really wants to see. what's the limits of what's possible. And so, the cost comes second, or it's a component, but first figure out what are the limitations. I think, with Elon, he's really driving the cost down. Is there some inspiration, some lessons you see there. of the challenge of driving the cost down, especially with Atlas with a humanoid robot? - Well, I think the thing that he's certainly been learning. by building car factories is.

what that looks like in scaling. By scaling, you can get efficiencies that drive costs down-. - Sure. - very well. And the smart thing that, you know, they have. in their favor is, you know, they know how to manufacture. They know how to build electric motors. They know how to build, you know, computers and vision systems, so there's a lot of overlap. between modern automotive companies and robots.

But hey, we have a modern robotic, I mean, automotive company behind us as well. (Lex laughs). - So, bring it on. - Who's doing pretty well, right? The electric vehicles from Hyundai are doing pretty well. - I love it. So, we've talked about some of the low-level control, some of the incredible stuff that's going on. and basic perception, but how much do you see currently. and in the future of Boston Dynamics's sort.

of higher-level machine learning applications? Do you see customers adding on those capabilities, or do you see Boston Dynamics doing that in house? - Some kinds of things. we really believe are probably gonna be. more broadly available, maybe even commoditized, you know, using a machine learning, like a vision algorithm. so a robot can recognize something in the environment. That ought to be something you can just download. Like, I'm going to a new environment, and I have a new kind.

of door handle or piece of equipment I wanna inspect. You ought to be able to just download that. And I think people. besides Boston Dynamics will provide that. And we've actually built an API that lets people add. these vision algorithms to Spot, and we're currently working with some partners. who are providing that. Levatas is a example of a small provider. who's giving us software for reading gauges, and actually, another partner. in Europe, Reply, is doing the same thing.

So, we see ultimately an ecosystem. of providers doing stuff like that. I think ultimately you might even be able. to do the same thing with behaviors. So, this technology will also be brought to bear. on controlling the robot, the motions of the robot. And you know, we're using learning, reinforcement learning. to develop algorithms for both locomotion and manipulation.

And ultimately, this is gonna mean. you can add new behaviors to a robot, you know, quickly. And that could potentially be done. outside of Boston Dynamics. Right now, that's all internal to us. I think you need to understand at a deep level, you know, the robot control to do that. But eventually, that could be outside. But it's certainly a place. where these approaches are gonna be brought. to bear in robotics.

- So, reinforcement learning is part of the process. So, you do use reinforcement learning. - [Robert] Yes. (Lex sighs). So, there's increasing levels of learning with these robots? - [Robert] Yes. - And that's for locomotion, for manipulation, for perception? - [Robert] Yes. - Well, what do you think in general. about all the exciting advancements. of transformer neural networks, most beautifully illustrated.

through the large language models like GPT 4? - Like everybody else, we're all, you know, I'm surprised at how far they've come. I'm a little bit nervous about the, there's anxiety around them, obviously, for, I think, good reasons, right? Disinformation is a curse, an unintended consequence.

of social media that could be exacerbated with these tools. So, if you use them to deploy disinformation, it could be a real risk. But I also think that the risks associated with these kinds. of models don't have a whole lot to do. with the way we're gonna use them in our robots. If I'm using a robot, I'm building a robot to do, you know, a manual task of some sort.

I can judge very easily, is it doing the task I asked it to? Is it doing it correctly? There's sort of a built-in mechanism for judging. Is it doing the right thing? Did it successfully do the task? - Yeah, physical reality is a good verifier. - It's a good verifier. That's exactly it, and whereas if you're asking. for, yeah, I don't know, you're trying to ask a theoretical question in ChatGPT, it could be true, or it may not be true.

And it's hard to have that verifier, what is that truth (laughs) that you're comparing against, whereas, in physical reality, you know the truth. And this is an important difference. And so, I think there is reason to be a little bit concerned. about, you know, how these tools, large language models could be used. But I'm not very worried about how they're gonna be used, well, how learning algorithms. in general are going to be used on robotics.

It's really a different application that has different ways. of verifying what's going on. - Well, the nice thing about language models is. that I ultimately see, I'm really excited. about the possibility of having conversations with Spot. - [Robert] Yeah. - There's no, I would say, negative consequences to that. but just increasing the bandwidth and the variety. of ways you can communicate with this particular robot. - [Robert] Yeah. - So, you could communicate visually. You can communicate through some interface and to be able.

to communicate verbally again with a beer and so on. I think that's really exciting to make. that much, much easier. - We have this partner Levatas that's adding. the vision algorithms for gauge reading for us. Just this week I saw a demo where they hooked up, you know, a language tool to Spot, and they're talking to Spot. to give commands. - Nice, I love it. - Yeah. - Can you tell me about the Boston Dynamics AI Institute? What is it, and what is its mission? - So, it's a separate organization,

the Boston Dynamics Artificial Intelligence Institute. It's led by Marc Raibert, the founder of Boston Dynamics, and the former CEO, and my old advisor at MIT. Marc has always loved the research, the pure research without the confinement. or demands of commercialization. And he wanted to continue. to, you know, pursue that unadulterated research.

and so suggested to Hyundai. that he set up this institute, and they agree that it's worth additional investment. to kinda continue pushing this forefront. And we expect to be working together. where you know Boston Dynamics is, again, both commercialize and do research, but the sort of time horizon. of the research we're gonna do is, you know, in the next, let's say five years, you know. What can we do in the next five years? Let's work on those problems.

And I think the goal of the AI Institute is. to work even further out. Certainly, you know, the analogy of legged locomotion again, when we started that, that was a multi-decade problem. And so, I think Marc wants to have the freedom. to pursue really hard over-the-horizon problems. That'll be the goal of the institute. - So, we mentioned some of the dangers, some of the concerns. about large language models.

That said, you know, there's been a long-running fear. of these embodied robots. Why do you think people are afraid. (Robert laughs). of legged robots? - Yeah, I wanted to show you this. So, this is in the Wall Street Journal, and this is all about ChatGPT, right? But look at the picture. - Yeah. - [Robert] It's a humanoid robot. - That's saying, "I will replace you.". - That looks scary, and it says, "I'm gonna replace you.". - [Lex] Yeah. - And so, the humanoid robot is.

the embodiment of this ChatGPT tool. that there's reason to be a little bit nervous. about how it gets deployed. - [Lex] Yeah. - So, I'm nervous about that connection. It's unfortunate that they chose to use a robot. as that embodiment. As you and I just said, there's big differences in this. But people are afraid because we've been taught to be afraid. for over 100 years.

So, you know, the word robot was developed. by a playwright named Karel Capek in 1921, a Czech playwright, "Rossum's Universal Robots.". And in that first depiction of a robot, the robots took over (laughs) at the end of the story. And you know, people love to be afraid. And so, we've been entertained by these stories. for 100 years, and I think that's as much. why people are afraid as anything else, as we've been sort of taught that this is.

the logical progression through fiction. I think it's fiction. - I think what people more and more will realize, just like you said, that the threat, like say you have a super-intelligent AI embodied. in a robot. That's much less threatening because it's visible. It's verifiable. It's right there in physical reality. And we humans know how to deal with physical reality.

I think it's much scarier when you have arbitrary scaling. of intelligent AI systems in the digital space. that they could pretend to be human. So, robot Spot is not gonna pretend. It could pretend it's human all it wants. (Lex laughs). You could put ChatGPT on top of it, but you're gonna know it's not human. because you have a contact with physical reality. - And you're gonna know whether or not it's doing. what you asked it to do. - Yeah, like, it's not gonna, (laughs). I mean, I'm sure it can start, just like a dog lies to you.

It's like, "I wasn't part of tearing up that couch.". So, Spot can try (Robert laughs). to lie that like, you know, "It wasn't me that's spilled that thing,". but you're going to kind of figure it out eventually. if it happens multiple times, you know. But I think that-. - Humanity has figured out how to make machines safe. - [Lex] Yeah. - And there's, you know, the regulatory environments. and certification protocols that we've developed in order.

to figure out how to make machines safe. We don't know and don't have that experience with software. that can be propagated worldwide in an instant. And so, I think we needed to develop those protocols. and those tools, and so that's work to be done. But I don't think the fear of that. and that work should necessarily impede our ability. to now get robots out because again, I think we can judge when a robot's being safe. - So, and again, just like in that image,

there's a fear that robots will take our jobs. I took a ride. I was in San Francisco. I took a ride in a Waymo vehicle. It's an autonomous vehicle, and I've done it several times. They're doing incredible work over there, but (laughs) people flicked it off. - Oh, really? - Flicked off the car. So, (laughs) I mean, that's a long story. of what the psychology of that is. It could be maybe big tech, or I don't know exactly.

what they're flicking off. - [Robert] Yeah. - But there is an element of, like, "These robots are taking our jobs,". or irreversibly transforming society such. that it will have economic impact, and the little guy would lose a lot, would lose their well-being. Is there something to be said about the fear. that robots will take our jobs? - You know, at every significant technological transformation,

there's been fear of, you know, an automation anxiety-. - Yes. - that it's gonna have. a broader impact than we expected. And there will be, you know, jobs will change. Sometime in the future, we're gonna look back at people. who manually unloaded these boxes from trailers, and we're gonna say, "Why did we ever do that manually?". But there's a lotta people who are doing that job today. that could be impacted.

But I think the reality is, as I said before, we're gonna build the technology. so that those very same people can operate it. And so, I think there's a pathway. to upskilling and operating. Just like, look, we used to farm with hand tools, and now we farm with machines, and nobody has really regretted that transformation. And I think the same can be said for a lot of manual labor. that we're doing today. And on top of that, you know, look, we're entering a new world where demographics are gonna have.

strong impact on economic growth, and you know, the advanced, the first world is losing population quickly. In Europe, they're worried about hiring enough people. just to keep the logistics supply chain going. And you know, part of this is the response to COVID, and everybody's sort of thinking back. what they really wanna do with their life,

but these jobs are getting harder and harder to fill. And I'm hearing that over and over again. So, I think, frankly, this is the right technology. at the right time where we're gonna need some of this work. to be done, and we're gonna want tools. to enhance that productivity. - And the scary impact, I think, again, GPT comes to the rescue in terms. of being much more terrifying. (Robert laughs). (Lex laughs). The scary impact of, basically,

so I'm, I guess, a software person, so I program a lot. And the fact that people like me could be easily replaced. by GPT, that's going to have a-. - Well, and lot, you know, anyone who deals with texts. and writing a draft proposal might be easily done. with ChatGPT now. - Yeah. - where-. - Consultants. - it wasn't before. - [Lex] Journalists. - Yeah. - [Lex] Everybody is sweating. - But on the other hand, you also want it to be right.

And they don't know how to make it right yet. But it might make a good starting point for you to iterate. - Boy, do I have to talk to you about modern journalism. (Robert laughs). That's another conversation altogether, but yes, more right than the average, the mean journalist, yes. You spearheaded the weaponization letter. Boston Dynamics has.

Can you describe what that letter states. and the general topic of the use of robots in war? - We authored a letter and then got. several leading robotics companies around the world, including, you know, Unitree in China, and Agility here in the United States, and ANYmal in Europe,

and, you know, some others to cosign a letter that said. we won't put weapons on our robots. And part of the motivation there is, you know, as these robots start to become commercially available, you can see videos online of people who've gotten a robot, and strapped a gun on it, and shown that they can, you know, operate the gun remotely while driving the robot around. And so, having a robot that has this level of mobility.

and that can easily be configured in a way. that could harm somebody from a remote operator is. justifiably a scary thing. And so, we felt like it was important. to draw a bright line there and say, "We're not going to allow this,". for, you know, reasons that we think ultimately it's better. for the whole industry if it grows in a way. where robots are ultimately going to help us all. and make our lives more fulfilled and productive.

But by goodness, you're gonna have to trust the technology, to let it in. And if you think the robot's gonna harm you, that's gonna impede the growth of that industry. So, we thought it was important to draw a bright line. and then publicize that. And our plan is to, you know, begin to engage. with lawmakers and regulators.

Let's figure out what the rules are going to be. around the use of this technology and use our position. as leaders in this industry and technology. to help force that issue. In fact, I have a policy, you know, director. at my company whose job it is to engage with the public, to engage with interested parties, including regulators,

to sort of begin these discussions. - Yeah, it's a really important topic, and it's an important topic for people that worry. about the impact of robots on our society. with autonomous weapon systems. So, I'm glad you're sort of leading the way in this. You are the CEO of Boston Dynamics. What's it take to be a CEO of a robotics company? So, you started as a humble engineer, (laughs). a PhD. Just looking at your journey,

what does it take to go from building the thing. to leading a company? What are some of the big challenges for you? - Courage I would put front and center for multiple reasons. I talked earlier about the courage to tackle hard problems. So, I think there's courage required not just of me. but of all of the people who work at Boston Dynamics.

I also think we have a lot of really smart people. We have people who are way smarter than I am. And it takes a kinda courage to be willing to lead them. and to trust that you have something to offer to somebody. who probably is maybe a better engineer than I am. Adaptability, you know, it's been a great career for me. I never would've guessed I'd stayed. in one place for 30 years, and the job has always changed.

I didn't really aspire to be CEO from the very beginning, but it was the natural progression of things. There always needed to be some level. of management that was needed. And so, you know, when I saw something. that needed to be done that wasn't being done, I just stepped in to go do it. And oftentimes because we were full. of such strong engineers, oftentimes that was.

in the management direction, or it was in the business development direction. or organizational, hiring. Geez, I was the main person hiring at Boston Dynamics. for probably 20 years, so I was the head of HR, basically. You know, just willingness to sort of tackle any piece. of the business that needs it and be willing to shift. - Is there something you could say to what it takes. to hire a great team?

What's a good interview process? How do you know the guy or gal are gonna make a great member. of a engineering team that's doing some. of the hardest work in the world? - You know, we developed an interview process. that I was quite fond of. It's a little bit of a hard interview process. because the best interviews, you ask somebody. about what they're interested in and what they're good at,

and if they can describe to you something. that they worked on, and you saw they really did the work, they solved the problems, and you saw their passion for it, but what makes that hard is you have. to ask a probing question about it. You have to be smart enough about what they're telling you. they're expert at to ask a good question. And so, it takes a pretty talented team to do that. But if you can do that, that's how you tap into,

"Ah, this person cares about their work. They really did the work. They're excited about it.". That's the kind of person I want at my company. You know, at Google, they taught us. about their interview process, and it was a little bit different. You know, we evolved the process at Boston Dynamics. where it didn't matter if you were an engineer, or you were an administrative assistant, or a financial person, or a technician.

You gave us a presentation. You came in, and you gave us a presentation. You had to stand up and talk in front of us. And I just thought that was great to tap into those things. I just described to you. At Google, they taught us, and I understand why, right. They're hiring tens of thousands of people. They need a more standardized process. So, they would sort of err on the other side. where they would ask you a standard question. I'm gonna ask you a programming question, and I'm just gonna ask you. to, you know, write code in front of me. That's a terrifying, you know, application process.

- [Lex] Yeah. - It does let you compare candidates really well, but it doesn't necessarily let you tap into who they are. - Yeah. - (laughs) Right? 'Cause you're asking them to answer your question. instead of you asking them about what they're interested in. But frankly, that process is hard to scale. And even at Boston Dynamics, we're not doing that with everybody anymore. But we are still doing that. with, you know, the technical people.

because we too now need to sort. of increase our rate of hiring. Not everybody's giving a presentation anymore. - But you're still ultimately trying to find. that basic seed of passion-. - Yeah, and talent. - for the world. - You know, did they really do it? Did they find something interesting or curious, you know, and do they care about it? (laughs). - I think somebody I admire is Jim Keller, and he likes details.

So, one of the ways you could, (laughs). if you get a person to talk about what they're interested. in, how many details, like, how much. of the whiteboard can you fill out? - Yeah. - What they-. - Well, I think you figure out did they really do the work. if they know some of the details. - Yes. - And if they have. to wash over the details, well, then they didn't do it. - They didn't do it. (Robert laughs). 'Cause especially with engineering, the work is in the details. - Yeah. - I have to go there briefly (sighs). just to get your kind of thoughts. on the long-term future of robotics.

There's been discussions on the GPT side, on the large language model side of whether there's. consciousness inside these language models. And I think there's fear, but I think there's. also excitement or at least the wide world. of opportunity and possibility in embodied robots. having something like, let's start with emotion, love towards other human beings.

and perhaps the display, real or fake, of consciousness. Is this something you think about in terms. of long-term future? Because, as we've talked about, people do anthropomorphize these robots. It's difficult not to project some level. of, I use the word sentience, some level of sovereignty, identity, all the things we think as human.

That's what anthropomorphization is, is we project humanness. onto mobile, especially legged robots. Is that something almost. from a science-fiction perspective you think about? Or, do you try to avoid ever, try to avoid the topic of consciousness altogether? - I'm certainly not an expert in it, (Lex laughs) and I don't spend-. - Is anybody? - a lot of time thinking. about this, right? And I do think it's fairly remote for the machines. that we're dealing with.

You're right that people anthropomorphize. They read into the robots' intelligence and emotion. that isn't there because they see physical gestures. that are similar to things they might even see. in people or animals. I don't know much. about how these large language models really work. I believe it's a kind of statistical averaging. of the most common responses, you know,

to a series of words, right? It's sort of a very elaborate word completion. And I'm dubious that that has. anything to do with consciousness. And I even wonder if that model of sort. of simulating consciousness by stringing words together. that are statistically associated with one another, whether or not that kind of knowledge,

if you wanna call that knowledge, would be the kind of knowledge that allowed a sentient being. to grow or evolve. It feels to me like there's something about truth. or emotions that's just a very different kind of knowledge. that is absolute. The interesting thing about truth is it's absolute, and it doesn't matter how frequently it's represented. in the worldwide web. If you know it to be true, it it can only be,

it may only be there once, but by God, that's true. And I think emotions are a little bit like that, too. You know something, you know, and I just think that's a different kind of knowledge. than the way these large language models derive sort. of simulated-. - It does seem that- - intelligence. - things that are true very well might be. statistically well represented on the Internet. because the Internet's made up of humans.

So, I tend to suspect that large language models are going. to be able to simulate consciousness very effectively. And I actually believe that current GPT 4, when fine-tuned correctly, would be able to do just that. And there's going to be a lot. of very complicated ethical questions that have. to be dealt with that have nothing to do with robotics. and everything to do with-. - There needs to be some process of labeling, I think, (laughs) what is true.

because there is also disinformation available on the web, and these models are going to consider that kind. of information as well. And again, you can't average something that's true. and something that's untrue and get something. that's moderately true. (laughs). It's either right, or it's wrong. And so, how is that process, and this is obviously something. that the purveyors of these, Bard and ChatGPT,

I'm sure this is what they're working on. - Well, if you interact on some controversial topics. with these models, they're actually refreshingly nuanced. Well, you realize there's no one truth, you know. What caused the war in Ukraine, right? Any geopolitical conflict, you can ask any kind of question, especially the ones that are politically tense, divisive, and so on.

GPT is very good at presenting, it presents the different hypotheses. It presents calmly (laughing) sort of the amount. of evidence for each one. It's really refreshing. It makes you realize that truth is nuanced, and it does that well. And I think, with consciousness, it would very accurately say, "Well, it sure as hell feels like I'm one of you humans,

but where's my body? (Robert laughs). I don't understand.". Like, you're going to be confused. The cool thing about GPT is it seems to be easily confused. in the way we are. Like, you wake up in a new room, and you ask, "Where am I?". It seems to be able to do that extremely well. It'll tell you one thing, like a fact. about when a war started, and when you correct it, say, "Well, that's not consistent,". it'll be confused. It'll be, "Yeah, you're right.". It'll have that same element, childlike element.

with humility of trying to figure out its way in the world. And I think that's a really tricky area. to sort of figure out with us humans of what we want. to allow AI systems to say to us. Because then, if there's elements of sentience. that are on display, you can then start. to manipulate human emotion, all that kinda stuff. But I think that's a really serious.

and aggressive discussion that needs to be had. (laughing) on the software side. I think, again, embodiment, robotics are actually saving us. from the arbitrary scaling of software systems. versus creating more problems. But that said, I really believe. in that connection between human and robot. There's magic there. And I think there's also, I think, a lot of money to be made there. And Boston Dynamics is leading the world.

in the most elegant movement. done by robots. (Robert laughs). So, I can't wait- - Well, thank you. - to what maybe other people that built on top. of Boston Dynamics robots or Boston Dynamics by itself. So, you had one wild career, one place on one set of problems but incredibly successful. Can you give advice to young folks today in high school,

maybe in college looking out into this future. where so much robotics. and AI seems to be defining. the trajectory of human civilization. Can you give 'em advice on how to have a career. they can be proud of or how to have. a life they can be proud of? - Well, I would say, you know, follow your heart. and your interest. Again, this was an organizing principle, I think,

behind the Leg Lab at MIT that turned. into a value at Boston Dynamics, which was follow your curiosity. Love what you're doing. You'll have a lot more fun, and you'll be a lot better. at it as a result. I think it's hard to plan, you know. Don't get too hung up on planning too far ahead.

Find things that you like doing. and then see where it takes you. You can always change direction. You will find things that, you know, "Ah, that wasn't a good move. I'm gonna pack up and go do something else.". So, when people are trying to plan a career, I always feel like, "Ah, there's a few happy mistakes. that happen along the way and just live with that it.". You know, but make choices then. So, avail yourselves to these interesting opportunities, like when I happened to run into Marc down in the lab, the basement of the AI lab, but be willing.

to make a decision and then pivot. if you see something exciting to go at, you know, 'cause, if you're out and about enough, you'll find things like that that get you excited. - So, there was a feeling when you first met Marc. and saw the robots that there's something interesting. - "Oh, boy, I gotta go do this.". There was no doubt. (Lex laughs). (Robert laughs). - What do you think in 100 years, whoo, what do you think Boston Dynamics is doing? Even bigger, what do you think is the role.

of robots in society? Do you think we'll be seeing billions of robots everywhere? Do you think about that long-term vision? - Well, I do think that, I think that robots will be ubiquitous, and they will be out amongst us, and they'll be certainly doing, you know, some of the hard labor that we do today.

I don't think people don't wanna work. People wanna work. People need to work to, I think, feel productive. We don't wanna offload all of the work to the robots. 'cause I'm not sure if people would know. what to do with themselves. (Lex laughs). And I think just self-satisfaction and feeling productive is. such an ingrained part of being human. that we need to keep doing this work. So, we're definitely gonna have to work. in a complimentary fashion, and I hope that the robots.

and the computers don't end up being able. to do all the creative work. Right? - Yeah. - 'Cause that's the part that's, you know, that's the rewarding. The creative part of solving a problem is the thing. that gives you that serotonin rush that you never forget, you know, (laughs) or that adrenaline rush. that you never forget, and so, you know, people need. to be able to do that creative work. and just feel productive, and sometimes you can feel productive. over fairly simple work that's just well done,

you know, and that you can see the result of. So, yeah, you know, I don't know, there was a cartoon, was it "Wall-E,". where they had this big ship, and all the people were just overweight, lying on their beach chairs kinda sliding. around on the deck of the movie. because they didn't do anything anymore. - Yeah. - Well, we definitely don't wanna be there, (laughs) you know.

We need to work in some complimentary fashion. where we keep all of our faculties and our physical health, and we're doing some labor, right, but in a complimentary fashion somehow. - And I think a lotta that has to do with the interaction, the collaboration with robots and with AI systems. I'm hoping there's a lot of interesting possibilities there. - I think that could be really cool, right? If you can work in an interaction and really be helpful, robots, you know, you can ask a robot to do a job. you wouldn't ask a person to do,

and that would be a real asset. You wouldn't feel guilty about it, you know. (laughs). You'd say, "Just do it.". - Yeah. - It's a machine. And I don't have to have qualms about that, you know. - The ones that are machines, I also hope to see a future, and it is hope. I do have optimism about the future. where some of the robots are pets, have an emotional connection to us humans. and because one of the problems that humans have to solve is. this kind of general loneliness. The more love you have in your life,

the more friends you have in your life, I think that makes a more enriching life, helps you grow. And I don't fundamentally see why some. of those friends can't be robots. - There's an interesting long-running study, maybe it's in Harvard, just nice report article written. about it recently. They've been studying this group. of a few thousand people now for 70 or 80 years. And the conclusion is that companionship and friendship are. the things that make for a better and happier life.

And so, I agree with you, and I think that could happen with a machine. that is probably, you know, simulating intelligence. I'm not convinced there will ever be true intelligence. in these machines, sentience. But they could simulate it, and they could collect your history. You know, I guess it remains to be seen. whether they can establish that real deep, you know, when you sit with a friend,

and they remember something about you and bring that up, and you feel that connection, it remains to be seen. if a machine's gonna be able to do that for you. - Well, I have to say, inklings. of that already started happening for me. Some of my best friends are robots. (Robert laughs). And I have you to thank for leading the way. in the accessibility, and the ease of use of such robots, and the elegance of their movement. Robert, you're an incredible person. Boston Dynamics is an incredible company. I've just been a fan for many, many years.

for everything you stand for, for everything you do in the world. If you're interested in great engineering, robotics, go join them. Build cool stuff. I'll forever celebrate the work you're doing, and it's just a big honor. that you would sit with me today and talk. It means a lot, so thank you so much. Keep doing great work. - Thank you, Lex. I'm honored to be here, and I appreciate it. It was fun. - Thanks for listening to this conversation. with Robert Playter. To support this podcast, please check. out our sponsors in the description. And now, let me leave you some words. from Alan Turing in 1950,

defining what is now termed the Turing test. "A computer would deserve to be called intelligent. if it could deceive a human. into believing that it was human.". Thank you for listening and hope to see you next time.