#374 – Robert Playter: Boston Dynamics CEO on Humanoid and Legged Robotics

29:55So 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. And so dealing with all that, at least recently, you've been doing more explicit modeling of the stuff you pick up. It's just very really interesting. So you have to what model the shape, the weight distribution. I don't know. You have to include that as part of the modeling as part of the planning. For people who don't know, so Alice, at least in the recent video, throws a heavy bag, throws a bunch of stuff.

30:52So 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's also picked up like a barbell. And to be able to throw it in some cases, what are some interesting challenges there? So we were definitely trying to show that the robot and the techniques were applying to the to Alice, let us deal with heavy things in the world. Because if the robot's going to be useful, it's actually got to 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 different chase us from the other humanoid robot activities that you're seeing out there.

31:35Mostly they're not picking stuff up yet. And not heavy stuff anyway. But just like you or me, you need to anticipate that moment. You're reaching out to pick something up and as soon as you pick it up, your center of mass is going to shift. And if you're going to turn in a circle, you have to take that inertia into count. And if you're going to throw a thing, you've got all of that has to be 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 then and predict, think a couple of seconds ahead. How do I manage my body plus this big heavy thing together and to get an instill maintain balance?

32:22That'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 on the robot. And now we're starting to develop the tools and 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 going to be a quicker process. So being able to explicitly model new things that it might need to pick up, new type of things.

32:58And to some degree, you don't want to have to pay too much attention to each specific thing. 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. And so for some ways, you want to pay attention to that detailed shape. And in others, you want to generalize it and say, well, all I really care about is the center of mass of this thing, especially if I'm going to throw it up on that scaffolding.

33:38And 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, had loose 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 about that? Yeah, we haven't we haven't done that yet. 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.

34:15Okay, that's pretty good. Let me ask the all -important question. Do you know how much Alice can curl?

34:24I mean, you know, this for us humans, that's really one of the most fundamental questions. You can ask another human being, curl, bench. It's probably can't curl as much as we can yet. But I 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? Question. And Alice, I don't know the exact height, it was probably a meter high or something like that. It was a pretty tall jump that Alice 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 be human.

35:08But to human, getting all the way on top of this box. But then, you know, was able to do it. We're now thinking about the next generation of Alice. And we're probably going to be in the realm of a person can't do it, you know, with this, with the next generation. The robots, the actuators are going to 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 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 that's, you know, that's all.

35:39Yeah. We don't always release those those videos, but there are a lot of fun to look at. So we'll talk a little bit about that. But if you can talk to the jumping, because you talked about the walking, 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 is really evolved rapidly in the last few years. You know, the first time we did a summer salt, you know, there was a lot of kind of manual iteration. What is the trajectory? You know, how hard do you throw you?

36:16In fact, in these early days, I actually would, 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 we're able to, the robot essentially can think in advance for the next second or two about how its motion is going to transpire.

37:04And 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 backflip. Yeah, I mean, but that's the cool part, right? So, you know, the physics, we can calculate physics pretty well using Newton's laws about how it's going to evolve over time.

37:55And the road, this, this, this, the sick trick, which was a front -summer salt 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, the, 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 going to affect the outcome? And so it can do these calculations, many of them, you know, and basically solve for, you know, given where I am now, maybe I took off a little bit screwy from how I had planned, I can adjust.

38:42So you're adjusting in there. Just on the fly. So the the model predictive control lets you adjust on the fly. And of course, I think this is what, you know, people adapt as well. We, we, when, when we do it, even a gymnastics trick, we try to set it up so it's as 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. It's so, I mean, it just feels from a robotics perspective, just so real. Well, that's sort of the, you talked about under -actuated, right?

39:16So when you're, when you're in the air, there's something, there's some things you can't change, right? You can't change the momentum while it's in the air because you can't apply an external force or torque. And so the momentum isn't going to change. So how do you work within the constraint of that fixed momentum to still get from A to B where you want to be? That's really on track. You're in the air. I mean, you become a drone for a brief moment of time. No, you're not even a drone because you can't hover. You can't hover. You're gonna, you're gonna ampire soon. Be ready. Yeah. Are you considered like a hover type thing?

39:51I know. No, it's too much weight. I mean, it's just, it's just incredible. It's just even to have the guts to try backflip, who's such a large body. That's wild. We definitely broke a few robots trying. But that's where the build it break. It fix it. Strategy comes in. You gotta be willing to break. And what ends up happening is you end up 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, 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.

40:31And how to build a machine better. Yeah. I mean, is there something about just the guts to come up with an idea of saying, you know what, let's try 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? Because a lot of people have asked that, you know, why, why are you doing this? Why go to the moon in this decade and do the other things, JFK? Not because it's easy because it's hard. Yeah, exactly. No one has questions. Okay, so the jump, I mean, there's a lot of incredible stuff.

41:12If we can just rewind a little bit to the DARPA Robotics Challenge in 2015, I think, which was for people who are 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 manipulation, walking, driving 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. You can have periods where the communication was entirely interrupted and the robot had to be able to proceed.

42:03But you could provide some high -level guidance to the robot, basically low -band with communications to steer it. I watched that challenge with tears in my eyes eating popcorn.

42:17But I wasn't personally losing hundreds of thousands of millions of dollars. And many years of incredible hard work by some of the most brilliant robots that's in the world. So that was why the tragic, why the tears came. So you know, what have you 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 of 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 tests, 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.

43:08So 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 the grand challenge, which was let's let's try to push vehicle autonomy along, right? They they encourage people to build autonomous cars. So they're trying to basically push an industry forward. And we were asked our role in this was to build a human way 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.

44:00And DARPA distributed those to various teams that sort of won a contest, showed that they could 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 of 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 we didn't actually compete as a participant where we were trying to drive the robot through this maze.

44:46We were just trying to support the other teams. It was humbling because it was it was really a hard task. And honestly, the robots the tears were because mostly the robots didn't do it. You know, they fell down, you know, repeatedly was hard to get through this contest. Some did. And they were rewarded in one. 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, the general nature of it, the variety of it, the variety. And also that I don't know if the tasks were sort of the task in themselves help us understand what is difficult, what is not.

45:32I 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 for just for example, probably the hardest task is not the driving of the car, but getting in and out of the car. And Atlas probably, 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 to get in and out of that car, right? It doesn't fit.

46:11This 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 want to 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 going to be useful to us in the future, they need to be able to deal with unexpected situations.

46:59And that's sort of the goal of a general purpose or multi -purpose robot. And that's just darn hard. And so some of you know, there's these curious little failures. Like, I remember one of the 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 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.

47:37I didn't have, I didn't, I was expecting a force back from the door. It wasn't there. And then I lost my balance. So these little simple things that you and I would take totally for granted and deal with the robots, don't know how to deal with yet. And so you have to start to deal with all of those circumstances. Well, I think a lot of us experienced this in even one sober, but drunk too. So you pick up a thing and expect it to be, what is it? Heavy? It turns out to be light. Yeah. And then you woo. Oh, yeah. And then so the same. And I'm sure if you're deaf, the 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.

48:22I mean, it's the same kind of situation. But there's 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 going to happen. And then if that does happen, you're in good shape. If something else happens, you've got to start predicting again. So re re re regenerate a plan. Yeah. When you don't, I mean, that that also requires a very fast feedback loop of updating what your prediction matches to the actual real world. Yeah, those things have to run pretty quickly. What's the challenge of running things pretty quickly?

49:01A thousand hurts of acting and sensing quickly? You know, there's a few different layers of that. You want at the lowest level, you like to run things typically at around a thousand hurts, which means that 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 want to do that really fast, something like a thousand hurts. 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.

49:41And then there's another layer that you're probably calculating, you know, maybe at 100 hurts, 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 and 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 your computer time so that you can squeeze in all the calculations you need in real time in a very consistent way.

50:22And 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, we had, you know, 10 years ago, we had to have pretty simple models that we were running, you know, at those fast rates because the computers weren't as capable about calculating forward with a sophisticated model. But as as computation gets better, we can we can do more of that. What about the actual pipeline of software engineering?

51:10How you could continue as 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 work until, you know, a few years, several years ago.

51:58But that was, you know, that was a bit of a milestone. And so you want to work, certainly work these pipelines so that you can make it as easy as possible and have a bunch of people working in parallel, especially when you, 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 these, some of the software pipeline. Well, that's a really exciting step to be able to run the exact same code and simulation as on the actual robot.

52:31How hard is it to do realistic simulation, physics -based simulation of Atlas such that I mean, the dream is like, if it works in simulation, works perfectly in reality, 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 LEGO Robotics again, we had a really nice tool to work with.

53:16And the things we paid attention to were, the things that weren't necessarily handled very well in the commercial tools you could buy out the shelf, like interaction with the world, like foot -ground contact. 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 because if your simulation runs too slow, then your developers are sitting around waiting for stuff to run and compile. So it's always about efficient, fast operation as well.

54:01So that's been a big part of it. 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 does Simulay Contact period? So foot -ground contact, but sort of from manipulation, because don't you want to model all kinds of surfaces? Yeah, so it will be even more complex with manipulation, because there's a lot more going on, and you need to capture, I don't know, things slipping and moving in your hand. It's a level of complexity that I think goes above foot -ground contact when you really start doing dexterous manipulation.

54:50So there's challenges ahead, too. So how far are we away from me being able to walk with Atlas in the sand along the beach? And I was both drinking a beer. So maybe Atlas could spill his beer because he's got nowhere to put it. Atlas could walk on the sand. So can it? Yeah. I mean, have we really had him out on the beach? Yeah, we take them outside often, you know, rocks, hills, that sort of thing, even a surrounded lab in Waltham. We probably haven't been on the sand, but I'm a... Salt surface... 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, but we did take a...

55:34We had to take big dog 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 some cool beach music. Great show, but then, you know, we didn't really clean the robot off, and the salt water was really hard on it. So we put it in a box, shipped it back by the time it came back. We had some problems with... So it's a salt water, it's not like... Cold stuff. It's not like sand getting into the components or something like this, but I'm sure if this is a big priority, you can make it like waterproof.

56:17Right, but right. That just wasn't our goal at the time. Well, it's a personal goal, mine, to walk along the beach, but it's a human problem too, you get sand everywhere, it's just a jam mess. So salt surfaces are okay. So I mean, can we just link on the robotics challenge? There's a pile of, like, rubble to walk over. Is that's... How difficult is that task? In the early days of developing Big Dog, the loose rock was the epitome of the hard walking surface, because you step down and then the rock, and you have 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.

57:04Yes, 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 it's really... That became kind of 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 them 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 Big Dog. Can we maybe take a stroll through the history of us in dynamics? So what, and who's Big Dog? By the way, is who...

57:44Do you try not to anthropomorphize the robots? Do you try to remember that they're... This is like the division I have, because 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 on him 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 Big Dog? What is Big Dog? Well, I'll say to address the medic question. We don't try to draw hard lines around it being an it, or a him, or a her. It's okay, right? People, I think part of the magic of these kinds of machines, is by nature of their organic movement of their dynamics.

58:39We 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 a feeling for a machine, and people have feelings for their cars. They get attracted to them, 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.

59:29Why? Because again, they've seen things move like this that were living beings, which are intelligent. And so they want to attribute intelligence to the robots that isn't appropriate, even though they move like an intelligent being. But you try to acknowledge that the anthropomorphization is there and try to, for sure, acknowledge that it's there and have a little fun with it. 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, because the guy up on the scaffolding says, send me some tools.

1:00:12Atlas has to kind of 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've been, I happen to have a lot of legged robots. I hope to have a lot of spots in my possession. I'm interested in celebrating robotics and celebrating companies. And I also don't want to companies that do incredible stuff like Boston Dynamics.

1:00:50And I, there's, you know, I'm a little crazy. And you say you don't want to, you want to align, you want to help the company. Because I ultimately want a company that Boston Dynamics to succeed. And part of that we'll talk about, you know, success kind of requires making money. And so the kind of stuff I'm particularly interested in may not be the thing that makes money in the short term. I can make an argument that it will in the long term. But the kind of stuff I've been playing with is a robust way of having the quadripe as though the robot dogs communicate emotion with their body movement.

1:01:29The same kind of stuff you do with the dog. But not not hard coded, but in a robust way. And be able to communicate excitement or fear, 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 going to be really important. And it's a thing we're beginning to pay attention to. We really want to start 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 the like it locomotion really works.

1:02:15It can really get around and it doesn't fall down. But beyond that, in 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 gets into needs help or, or you know, as in trouble or something, it needs to be able to communicate. And a physical indication of some sort, so that a person looked at the robot and goes, Oh, I know what that's the robot's doing.

1:02:59The robot's going to go take measurements of my vacuum pump with its thermal camera. You know, you want to be able to indicate that. And we're even just the robots about to turn, you know, in front of you, and maybe indicate that it's great a turn. And so you sort of see and can anticipate its motion. So these, 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 a thousand of them out with customers right now. This layer of physical indication, I think, is going to become more and more important.

1:03:41We'll talk about where it goes, because there's a lot of interesting possibilities. But if you're going to return back to the origins of Boston Dynamics, so that the more research, the R &D side before we talk about how to build robots at scale, so big dog. So who's big dog? So the company started in 1992. And probably, uh, 2003, I believe, is when we took a contract from DARP, so basically 10 years, 11 years, we weren't doing robotics. We did a little bit of robotics with Sony. They had a Ibo, their Ibo robot. We were developing some software for that that kind of got us a little bit involved with robotics again.

1:04:31Then there's this opportunity to do a DARPA contract where they wanted to build a robot dog. And, uh, we, we won a contract to build that. And so that was the genesis of big dog. And it was a quadruped, and it was the first time we built a robot that had everything on board that you could actually take the robot out into the wild and operate it. So it had an onboard power plant, 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, uh, that was a pretty rough start, right? So it was, it was 10 years that we were not a robotics company.

1:05:13We 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. I mean, well, can you just comment on the roughness of that transition? Because, big dog, I mean, it's just big quadruped four legs robot. We built a few different versions of them. But the first one, the very earliest ones, you know, didn't work very well. We would take them out. And it was hard to get, you know, a go -kart engine driving a hydraulic pilot. And, you know, having that all work while trying to get, you know, the robot to stabilize itself. So what was the power plant?

1:05:57What was the engine? It seemed like my vague recollection. It felt very loud and aggressive and kind of thrown together. It's what it kind of, 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 big dog had literally go -kart engines or something like that. You see it as power? Like a gas powered two -stroke engine. And the reason why it was too stroke is two -stroke engines are lighter weight. That they're also, and we didn't generally didn't put mufflers on them because we're trying to save the weight.

1:06:38And we didn't care about the noise. 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, I've seen the videos. 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 it out with just walking on the flat and then we started walking on rocks and then inclines and then mud and then slippery mud.

1:07:23And, you know, by the end of that program, we were convinced that legged locomotion in a robot could actually work because going into it, we didn't know that. We had built quadrupeds at MIT, but they were, 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. But, and the purpose was to prove to ourselves that the legged locomotion could really work. And so, big dog really cut that open for us.

1:08:02And 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 to hand, like LS3 designed to carry heavy loads.

1:08:31We built 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. Yeah. And to discover those principles, you know, you asked earlier about the art and science of a legged locomotion, we were able to develop principles of legged locomotions so that we knew how to build a small legged robot or a big one. So, the legged, legged 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.

1:09:10And 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 a sort of a different thread where we started building humanoids. We saw that probably an end was coming where the government was going to 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.

1:10:04And that was Google. And so, at Google, we would meet regularly with Larry Page. And Larry just started asking us, you know, well, what's your product going to 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.

1:10:49Yes. 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? What, like starting to think about a product? Larry always liked the toothbrush test. He wanted products that you used every day.

1:11:14What 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 going to 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. Of course, we were only building a two, but we didn't see how to get all the way to this consumer level product in a short amount of time. And he suggested that we make the robot really inexpensive. And part of our philosophy has always been, build the best hardware you can.

1:12:01Make 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. You build the best machine you can. There's plenty of hard problems to solve that are going to 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 of the make robots work. And so there was a little bit of a philosophical difference there.

1:12:40And 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 going to 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 getting there needs to go through these really nice machines so we can then learn how to simplify. So what can you say to the almost engineering challenge of bringing down cost of the robot?

1:13:27So that presumably when you try to build the 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. 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, because you've got these engineers who love hard problems, who want to figure out how to make robots work. And you don't know if you have engineers that want to work on the quality and reliability and costs that is ultimately required.

1:14:07And indeed, 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 want to 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 we'll 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, even having to get reliability to where it needs to be, we have to have fleets of robots that were just operating 24 -7 in our offices to go find those rare failures and eliminate them.

1:14:58It'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, 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 that got it just 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 is the MIT, for example, I don't think anyone ever has a working robot for prolonged periods.

1:15:40So like, most robots are just sitting there in a sad state of despair waiting to be born, brought to life for a brief moment of time. Just to have, I just remember there's a spot robot just had like a cowboy head on and was just walking randomly for whatever reason. I don't even know. But there's a kind of sense of sentience to it because it doesn't seem like anybody was supervising it. It was just doing a thing. I'm gonna stop way shorter than sentience. 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 all the time.

1:16:20And 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, because that's what somebody who's running a brewery, a factory, that's what they need the robot to do. So we have to, 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 1500 or maybe 2000 kilometers of walking. And you know, over a thousand hours of operation every week.

1:17:04And that's something that almost, I don't think anybody else in the world can do, because hey, you have to have a fleet of robots to just accrue that much information. You have to be willing to dedicate it to that test. And so that's, but that's essential. 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? And we're still, we're still learning a lot there. We're learning how to cast parts in solo instead of mill it all out of, you know, bill it aluminum.

1:17:39We're learning how to get plastic molded parts. We're, and we're learning about how to control that process 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, we've set up a manufacturing facility in Waltham. It's about a mile from our headquarters. And we're doing final assembly and test of both spots and stretches, you know, at that factory. And, 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.

1:18:20And that's a hard process, especially when you want to move as fast as we do. And that's been challenging. And it makes it, 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 them to make them cheap. And then we go change the design from underneath them. And they're like, what are you doing? And so, you know, getting everybody on the same page here that it, yep, we still need to move fast, but we also need to try to figure out how to reduce costs. That's one of the challenges of this migration we're going through.

1:18:53And over the past few years, challenges to the supply chain. I mean, I'm imagining 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, it's all better 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 spots going to 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.

1:19:28And, 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 it's if we really knew on day one that we're going to be operating in a factory with these three sensors in it, we would have it all integrated in a package that would be easier, more less expensive and more reliable.

1:20:07So we're contemplating building, a next generation of that machine. So we should mention that so spot for people who are somehow not familiar. So it's 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? Bossed the challenges of this design and the manufacture of it. We think the future of mobile robots is mobile manipulation. That's where, you know, in the past 10 years, it was getting mobility to work, getting the leg of 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.

1:20:56And 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 several motors and actuators and sensors. It has a camera in the end of its hand. So you can sort of see something in 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 say balance, 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 that grasp.

1:21:48But it's a whole nother complex robot on top of a complex like a robot. And so and of course we made it the hand look a little like ahead, 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. Oh yeah, it's an option. What's the interface like to work with the arm? Like a is it pretty so are they designed primarily? I could just ask that question in general about robots from both the dynamics. Is it designed to be easily and efficiently operated remotely by human being?

1:22:36Or 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 going to offer the ability of if you have a autonomous mission for the robot, we're going to include the option that it can go through a door, which means it's going to have to have an arm and it's going to have to use that arm to open the door. And so that'll be an autonomous manipulation task that just you can program easily with the robot 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 is the door handle.

1:23:19You know, the hinges are on the left and it opens in. The rest is up to you. Take care. Oh wow. So it just takes care of everything. Yeah. So we want in 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 they have to, when they're going to disconnect, you know, their power supply, right, that could be a gas generator, it 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 involved in this breaker switch.

1:24:03And it's a dangerous event. Because 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 in the arm through the interface to operate this disconnect 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 going to deliver back to Ontario power generation. They're going to be able to just point the robot at that breaker.

1:24:46They'll be out, 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 going to put your tool and you're going to 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 body and on the ground.

1:25:25Yeah, maintains its balance. It walks itself 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? Because I personally want a robot that gets me a beer. I mean, that has to be like one of the most requests 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, it was a safety feature, we saw that the robot could do it because they teleoperated it the first time.

1:26:14Probably took them 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 going to 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, we try to our bigger customers, typically ones who are going to run utility or a factory or something like that. We take that kind of direction from them. And if they're, especially if they're going to buy 10 or 20 or 30 robots, and they say, I really needed to do this.

1:26:54Well, that's exactly the right kind of problem that we want to be working on. And so note to self by 10 thoughts and aggressively push for beer manipulation. 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 the industrial setting, want to purchase them. And it's difficult to build robots that are useful, sufficiently useful. So what can you speak to?

1:27:35And Boston Dynamics has been successful for many years of finding a way to make money. Well, in the early days, of course, the money we made was from doing contract R &D work. And we made money, but we weren't growing and we weren't selling a product. And then we went through several owners who had a vision of not only developing advanced technology, but eventually developing products. And so both Google and Softbank and now Hyundai had that vision. And we're willing to 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.

1:28:28If you only sell hundreds, you will commercially fail. And that's where most of the small robot companies have died.

1:28:38And that's a challenge because, you know, a, you need to field the robots. They need to start to become reliable. And as we 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 of vacuum pump failed someplace, that can be in 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.

1:29:30But 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 ABN Bev. It takes place in chip fabs. You know, we're working with global foundries. It takes place in electric utilities and nuclear power plants.

1:30:16And so the same robot can be applied in all of these industries. And and as I said, we have about actually it's 1100 spots out now to really get, you know, profitability. We need to be at a thousand a year, maybe maybe 1500 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 the cost in that thousand robot area. It really should. Yeah. I want to mention our second robot stretch. Yeah. Tell me about stretch. What stretch? What stretch? Stretch started differently than spot.

1:30:57You know, spot we built because we had decades of experience building quadrupeds. We just 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 this factory patrol application, basically preventative maintenance. By seeing what our customers did with it. Stretch is very different. We started knowing that there was like 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, cardboard boxes shipped around the world each year.

1:31:37And 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 going to be used for, they immediately started saying, Oh, I'll buy all by that robot. You know, in fact, I'm going to put in an order for 20 right now. We just started shipping the robot in January. After several years of development. This year, this year. So our first deliveries of stretch to customers were DHL and Merisk in January.

1:32:16We're delivering a 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 commitment. It's for a couple hundred of these robots. This one's going to go, right? It's so obvious that there's a need. And we're not just going to unload trucks. We're going to do any box moving task in the warehouse. And so it, too, will be a multi -purpose robot. And we'll eventually have it doing palatizing or depalatizing 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.

1:32:54So it's going to 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 palette. And we want it to be the size of a palette because that's what lives in warehouses, right? Palants of goods sitting everywhere. So we needed to be able to fit in that space. It's not a legate, mostly. Not a legate robot. And 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. To do them which easier problem. And to put it to do a well, because it wasn't, you know, it wasn't going to have this balance problem.

1:33:35And in fact, the very first version of the logistics robot we built was a balancing robot. And that's called handle. And there's nothing was epic. All right. It's a beautiful machine. It's an incredible machine. So it was, I mean, it looks epic. It looks like out of the sci -fi movie of some sorts. I mean, just can you actually just linger on the design of that thing? Because that's another leap into something you probably haven't does a different kind of balancing. Yeah. So let me, I'd love, I'd love talking about the history of how a handle came about because it connects all of our robots actually.

1:34:13So I'm going to start with Atlas. When we, 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.

1:34:52We were starting to figure that out on Atlas. And so our first concept of handle, which was a robot that was going to be on two wheels. So it had the balance, but it was going to have a big long arm so it could reach a box at the top of a truck. And it was going to, 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 an ostrich moving around was the wheels, the le, it has legs so it can extend its legs. So it's wheels on legs. We always wanted to build wheels on legs.

1:35:36It had a tail and had this arm and they're all moving simultaneously and in coordination to maintain balance because we 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.

1:36:14And it was doing beautifully with elegance. We lived beautifully but it just wasn't an official enough. So we let it go. Yeah, but I think we'll come back to that machine eventually. The fact that it's possible, the fact that he's show that you could do so many things at the same time in coordination that's so beautifully. There's something there. 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.

1:36:53And 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 of operation. And that big battery helps it stay balanced. So we can move a 50 -pound box around with its arm and not tip over. It's omnidirectional. It can move in any direction. So it has a nice suspension built into it so it can deal with 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 a pick or place a box up to 50 pounds anywhere in the warehouse. Take a box from 0 .8 to 0 .8 B anywhere.

1:37:38Palatize, depalatize. We're starting with unloading trucks because there's so many trucks and containers that were good or shipped. And it's a brutal job. In the summer, it can be 120 degrees inside that container. People don't want to do that job. And it's back breaking 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.

1:38:18And so as we've introduced these robots to both the HL and Marist can 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 you constantly think about how. And I suppose you have to do the same kind of thinking with Spot. So it seems like to stretch the application is like directly obvious. Yeah, it's a slam dunk.

1:39:00Yeah, and so you can have a little more flexibility. Well, I think we know the target. We know what we're going after. 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, as social robots. We definitely think about it.

1:39:38And we would like to get there. We think the pathway to getting there is likely through these industrial applications and then mass manufacturing. 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? The 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.

1:40:22And that means you have to solve some of these hard problems. And so, to get there, we're going to go through the design and software development in industrial and then that's eventually going to let you reach a scale that could then be addressed to a commercial 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. But there's things we need to figure out still. How to safely, really safely. And if you're going to be interacting with children, you better be safe. 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 in the robot.

1:41:06So 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 tradeoff and safety because it feels like in the home, you can fall. Like you don't have to be as good at 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 because 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. Yeah. They're going to want to get the beer and know the difference between the beer and the Coca -Cola or my snack.

1:41:53And they're all going to want you to clean up the dishes you know from the table without breaking them. Those are pretty complex tasks. And so there's there's still work to be done. So to push back on that, here's where application I think they'll be very interesting. I think the application of being a pet a friend. So like no tasks just be cute because I'm not cute, not cute. Like the dog is more 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 because I'm hanging out alone with a robot dog too much. But like there's a there's a connection there and it feels like that connection is not should not be disregarded.

1:42:41No, it should not be disregarded. Robots that can somehow communicate through their physical gestures are you're going to be more attached to in the long run. Do you remember I bow? The Sony I bow. They sold over a hundred thousand of those, maybe a hundred and fifty thousand. You know what probably wasn't considered a successful product for them. They suspended that eventually and then they brought it back. So he brought it back. And people definitely 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 chat GPT or some other generative AI has made it possible for you to really talk and what seems like a meaningful way.

1:43:35Maybe that'll open the social robot up again. That's probably not a path we're going to go down because again we're so focused on performance and utility. We can add those other things also, but we really want to start from that foundation of utility I think. Yeah, but I also want to predict the wrong on that. So 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 chat GPT like capability, maybe GPT five. And there's just so many open source alternatives that you could just plop that on top of spot. And because you have this robust platform and you figuring out how to mass manufacture it and how to drive the cost down and how to make it reliable, all those kinds of things will be a natural transition to where just adding chat GPT until I do think that being able to verbally converse or even converse through gestures.

1:44:40You know, part of these learning models is that you can now look at video and imagery and associate intent with that. Those will all help in the communication between robots and people for sure. And that's going to happen. Obviously, more quickly than any of us we're expecting. I mean, what else do you want from life? Friend, you should be here. And then just talk shit about the state of the world. I mean, where'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. It'll be interesting to see, you know, when a genitive AI can give you that warm feeling that you connected, you know, and that, oh, yeah, you remember me.

1:45:36You're my friend. You know, we have a history. You know, that history matters, right? Memory of joy, like, memory of having witnessed. I mean, 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 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.

1:46:13And the current systems are not personalized in that way. But I think that's the technical problem, not some kind of fundamental limitation. So I'll combine that with an embodied robot, like Spot, which already has magic in its movement. I think it's a very interesting possibility of what, or that takes us. But of course, you have to build that on top of a company that's making money with real application, with real customers and with robots that are safe and work and reliable and manufactured scale. And I think we're in a unique position in that because of our investors, primarily, Hyundai, but also soft banks, so it owns 20 % of us.

1:46:59They don't, 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? Can we shape that? And they want both. And so we are, as a company, we're 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 want to try and drive my own ship, we need to have a business that's profitable in the end, otherwise, somebody else is going to drive the ship for us.

1:47:42So that's really important. But we're going to retain the aspiration that we're going to 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 has become a friend. So you almost can test, I've announced, they've been in the early days of building a humanoid robot. How does that change the landscape of your work? So there's sort of from the outside perspective, it seems like, well, as a fan of robotics, it just seems exciting. It's very exciting, right? When Elon speaks, people listen.

1:48:29And so it suddenly brought a bright light onto the work that we've been doing for over a decade. And I think that's only going to 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? Yes. Yeah. So and interestingly, many of them, as they're raising money, for example, will claim whether or not they have a former Boston Dynamics employee on their staff as a criteria. Yeah, that's true. That's a, I would do that. It's a company for sure. So I'm chose your legit. Yeah. So you know what? It's bringing, it has brought a tremendous validation to what we're doing.

1:49:20And excitement, competitive juices are flowing, you know, the whole thing. So it's all good. You know, I was also kind of stated that, you know, maybe he implied that the problem is solvable in year -term, which is a low -cost humanoid robot that's able to do, that's a relatively general use case robot. So I think Elon has known for sort of setting these kinds of incredible ambitious goals, maybe missing deadlines, but actually pushing not just the particular team Elise, 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 the excitement of competition kind of pushing outless maybe to do more cool stuff, trying to drive the cost of Atlas down perhaps.

1:50:27Or I mean, I guess I want to ask if there's some kind of exciting energy in Boston Dynamics due to this a little bit of competition. Oh yeah, definitely. When we released our most recent video of Atlas, you know, I think you'd seen it 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 park or mobility thing, but we can pick up and move heavy things. Because if you're going to work in a manufacturing environment, that's what you're going to 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 about 50 pound block, you know, for a robot that weighs 150 pounds, that's a lot to accommodate.

1:51:25So we're trying to show that we can do that. And so it's totally been energizing, you know, we see the next phase of Atlas being more dexterous hands that can manipulate and grab more things that we're going to start by moving big things around that are heavy and that affect balance. And why is that? Well, really tiny dexterous things probably are going to be hard for a while yet. And you know, maybe you could go build a special purpose robot arm, you know, for stuffing, you know, chips into electronic boards, but we don't really want to do really fine work like that. I think more coursework where you're using two hands to pick up and balance and then we'll be thing, maybe in a manufacturing environment, maybe in a construction environment, those are the things that we think robots are going to be able to do with the level of dexterity that they're going to have in the next few years.

1:52:22And that's the, that's where we're headed. And I think, 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 this 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, 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, the name X really wants to see what's the limits of what's possible.

1:52:58And 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. Yeah. And scaling, by scaling, you can get efficiencies that drive cost down 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.

1:53:44So there's a lot of overlap between modern automotive companies and robots. But hey, we have a modern robotic coming in that automotive company behind us as well. So bringing on who's doing pretty well, right? The electric vehicles from Hyundai are doing pretty well. I love it. So how much, so we've talked about some of the low level controls, some of the incredible stuff that's going on and basic perception. But how much do you see in currently in the future of Boston Dynamics, sort of more high level machine learning applications? Do you see customers adding on those capabilities or GC Boston Dynamics to that in -house?

1:54:33Some kinds of things we really believe are probably going to 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. I have a new kind of door handle or piece of equipment I want to inspect you ought to be able to just download that. And I think people, besides Boston Dynamics, we'll 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.

1:55:12Levitas is an example of a small provider who's giving a software for reading gauges. And actually another partner in Europe replies doing the same thing. So we see that. We see it ultimately an ecosystem of providers doing stuff like that. And 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 going to mean you can add new behaviors to a robot, you know, quickly.

1:56:00And 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 going to be brought to bear in robotics. So reinforcement learning is part of the processes. You do use reinforcement learning. Yes. So there's increasing levels of learning with these robots. Yes. And that's for both for for locomotion for manipulation and for perception. 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?

1:56:57Like 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 that's 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 going to 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.

1:57:51I 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 that is it doing the right thing? Did it successfully do the task? The physical reality is a good verifier. It's a good verifier. That's exactly it. Whereas if you're asking for, yeah, I don't know, you're trying to ask a theoretical question in chat GPT, it could be true or it may not be true, and it's hard to have that verifier. What is that truth that you're comparing against? Whereas in physical reality, you know the truth. This is an important difference.

1:58:32I think there is reason to be a little bit concerned about how these tools of large language models could be used, but I'm not very worried about how they're going to be used. 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. There's no, I would say negative consequences to that, but just increasing the bandwidth in the variety of ways you can communicate with this particular robot.

1:59:16So you can communicate. Visually, you can communicate through some interface and to be able to communicate verbally again with the beer and so on. I think that's really exciting to make that much, much easier. We have this partner, Levitas, that's adding the vision algorithms for daydreaming for us. They 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 examples. Can you tell me about the Boston Dynamics AI Institute? What is it in what is its mission? So it's a separate organization, the Boston Amics Artificial Intelligence Institute.

1:59:55It's led by Mark Robert, the founder of Boston Dynamics and the former CEO and my old advisor at MIT. Mark has always loved the research, the pure research, without the confinement or demands of commercialization. And he wanted to continue to pursue that unadulterated research.

2:00:23So suggested to Hyundai that he set up this institute and they agree that it's worth additional investment to kind of continue pushing this forefront. And we expect to be working together where Boston Dynamics is again both commercialized and do research. But the sort of time horizon of the research we're going to do is in the next let's say five years. 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 the analogy of a legate look emotion again when we started that was a multi decade problem. And so I think Mark wants to have the freedom to pursue really hard over the horizon problems.

2:01:11And that will be the goal of the institute. So we mentioned some of the dangers of some of the concerns about large language models that said there's been a long running fear of these embodied robots. Why do you think people are afraid of Lincoln robots? Yeah, I wanted to show you this. So this is a Wall Street Journal. And this is all about chat GPT, right? But look at the picture. Yeah. It's a humanoid robot that's saying I will say that looks scary and it says I'm going to replace you. And so the humanoid robot is sort of is the embodiment of this chat GPT tool that there's reason to be a little bit nervous about how I get deployed.

2:02:00So I'm nervous about that connection. It's unfortunate that they chose to use a robot as that embodiment. For 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 the word robot was developed by a playwright named Carol Chapack in 1921 to check playwright for Rossum's universal robots. And in that first depiction of a robot, the robots took over the end of the story. And 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 of anything else, as we've been taught that this is the logical progression through fiction.

2:02:56I 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 pretend to be human. So robot, spot is not going to be pretend it can pretend it's human all it wants. You could tell you, you could put your LGBT at the top of it, but you're going to know it's not human because you have a contact with physical reality.

2:03:44And you're going to know whether or not it's doing what you asked it to do. Yeah. Like it's not going to, like if it like, I mean, I'm sure you can start just like a dog lies to use like I didn't, I wasn't part of tearing up that couch. So it's fucking, it's fucking try to lie that like, you know, it wasn't me that's built that thing, but that's you're going to kind of figure it out eventually. It's, but if I have this multiple times, you know, when I think that humanity has figured out how to make machines safe. 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.

2:04:24We don't know, and don't have that experience with software that can be propagated worldwide in an instant. And so I think we need 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 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 just, I took a ride, I wasn't having this kind of a ride in the way more vehicles and autonomous vehicle. And I was on several times they're doing incredible work over there.

2:05:07But people flick that off. Or right? The car. So either that's a long story of what the psychology of that is. It could be maybe big tech or what I don't know exactly what they're flicking off. 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 will be, would lose a lot, would lose their well -being. Is there something you'd be said about the fear that robots will take our jobs? You know, at every significant technological transformation, there's been fear of an automation anxiety that it's going to have a broader impact than we expected.

2:06:01And there will be jobs will change. Sometimes in the future, we're going to look back at people who manually unloaded these boxes from trailers and we're going to say, why did we ever do that manually? But there's a lot of people who are doing that job today that it could be impacted. But I think the reality is, as I said before, we're going to build the technologies 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.

2:06:41And 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 going to have a strong impact on economic growth. And the, 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 want to do with their life. But these jobs are getting harder and harder to fill. And I just, I'm hearing that over and over again.

2:07:26So I think frankly, this is the right technology at the right time where we're going to need some of this work to be done. And we're going to 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. The scary impact of basically, so I'm a, 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, well, and a lot, you know, anyone who deals with texts and writing a draft proposal might be easily done with chat GPT. No, consultants, journalists, yeah.

2:08:18Everybody 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? That's another conversation altogether. But yes, more right than the average, the mean journalist yes. You spearheaded the NT 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, Unitry and China and agility here in the United States and animal in Europe and some others.

2:09:24To a cosine 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 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.

2:10:08For, 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 going to have to trust the technology to let it in. And if you think the robot's going to harm you, that's going to hurt and 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 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.

2:11:08And so we are, 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 and including regulators to sort of begin these discussions. He has a really important topic and it's an important topic for people that worry about the impact of robots on our society with autonomous weapons systems. So I'm glad you're sort of leading the way in this. You are the CEO of Boston Dynamics. What's the take to be a CEO of our robotics company? So you started as a humble engineer, PhD, just looking at your journey. What does it take to go from being, from building the thing to leading a company?

2:12:01What are some of the big challenges for you? Courage, I would put the 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 kind of 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.

2:12:44Adaptability, you know, part of it. It's been a great career for me. I never would have guessed I'd stayed in one place for 30 years. And the job has always changed. I didn't really aspire to be CEO for the very beginning, but it was the natural progression of things. There was always a, 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.

2:13:31Geez, I was the main person hiring at Boston Dynamics for probably 20 years. So I was the head of any piece of the business that needs it and then 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 galler are going to make a great member of an engineering team that's doing some of the hardest work in the world? 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 asked somebody about what they're interested in and what they're good at.

2:14:21And 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. And you could ask, 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, their expert, 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.

2:15:04And it was a little bit different.

2:15:09We 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 think 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.

2:15:44I'm going to ask you a programming question and I'm just going to ask you to write code in front of me. That's a terrifying, you know, application process. Yeah. It does let you compare candidates really well, but it doesn't necessarily let you tap in who they are. Right? Because 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 the technical people. But because we too now need to sort of increase our rate of hiring, not everybody's giving a presentation anymore.

2:16:27But you're still ultimately trying to find that basic seed of passion. Yeah. And tell the world. Did they really do it? Did they find something interesting or curious, you know, and do they care about it? I think somebody admires Jim Keller. And he he likes details. So one of the ways you could, 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. 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, man, then they don't do it.

2:17:08Especially with engineering, the work is in the details. Yeah. I have to go there briefly just to get your kind of thoughts in 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, that amorifies these robots?

2:18:10It's difficult not to project some level of, I used the word, sentience, some level of sovereignty, identity, all the things we think as human. That's what at the permorphization is. As we project humanness onto mobile, especially legular 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 and you don't spend a lot of time thinking about this, right? And I do think it's fairly remote for the machines that we're dealing with. Our robots, you're right, the people anthropomorphize.

2:18:57They read into the robot's 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 to a series of words. It's sort of a very elaborate word completion. I'm dubious that has anything to do with consciousness. 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 want to call that knowledge, would be the kind of knowledge that allowed a sentient being to grow or evolve.

2:20:01It 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 can only be it may only be there once. But my god, that's true. I think emotions are a little bit like that too. You know something, and I just think there's a different kind of knowledge than the way these large language models derive sort of simulated intelligence. Things that are true very well might be statistically well represented on the internet because the internet has made up a few months.

2:20:53So I tend to suspect that large language models are going to be able to simulate consciousness very effectively. I actually believe that current GBT -4, when fine -tuned correctly, they'll be able to do just that. And that's going to be a lot of very complicated ethical questions that have to be dealt with. They have nothing to do with robotics, and everything to do with... There needs to be some process of labeling, I think, what is true? Because 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.

2:21:40It's either right or it's wrong. And so how how is that process? And this is obviously something that the purveyors of these barred in Chan GBT that 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. They present... Because you realize there's no one truth. What caused the war in Ukraine? Any geopolitical conflict? You can ask any kind of question, especially the ones that are politically tense, divisive, and so on. GBT is very good at presenting. Here's the... It presents the different hypotheses.

2:22:27It presents calmly... the amount of evidence for each one. It's very... 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? I don't understand. Like, you're going to be confused. The cool thing about GBT 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 what a war started.

2:23:13And when you correct this, say, well, this isn't... This is 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 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 being on display, you can then start to manipulate human emotion, all that kind of stuff. But I think that's something that's a really serious and aggressive discussion that needs to be had on the software side.

2:23:56I think, again, embodiment, robotics are actually saving us from the arbitrary scaling of software systems versus creating more problems. But that's that. I really believe in that connection between human robot.

2:24:21There's leading the world in the most elegant movement done by robots. So I can't wait to thank you. Maybe other people that built on top of Boston Dynamics robots or Boston Dynamics by itself. You had one wild career, one place, and one set of problems, but incredibly successful. Can you give advice to young folks today in high school, maybe in college, looking out to this future, where so much robotics and AI seems to be defining the trajectory of human civilization. Can you give them advice on how to have a career that can be proud of or how to have a life that can be proud of? Well, I would say follow your heart and your interest.

2:25:19Again, this was an organizing principle, I think, behind the like 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.

2:25:45I think it's hard to plan. 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 wasn't a good move. I'm going to back up and go do something else. So when people are trying to plan a career, I always feel like, yeah, there's a few happy mistakes that happen along the way and just live with that. But make choices then. So avail yourself to these interesting opportunities like when I happen to run into Mark 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.

2:26:26Because 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 Mark and saw the robots that there's something interesting. Oh boy, I got to go do this. There is no doubt. What do you think in 100 years? What do you think Boston Dynamics is doing? What do you think is the role 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.

2:27:16And they'll be certainly doing some of the hard labor that we do today. I don't think people don't want to work. People want to work. People need to work to I think feel productive. We don't want to offload all of the work to the robots because I'm not sure if people would know what to do with themselves. 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 going to 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.

2:27:55Because that's the part that's the rewarding. The creative part of solving a problem is the thing that gives you that serotonin rush that you never forget, or that adrenaline rush that you never forget. And so people need to be able to do that creative work and just feel productive. And sometimes you can feel productive over fairly simple work. It's just well done. And you can see the result. So yeah, there was a cartoon, was it Wally, where they had this big ship and all the people were just overweight lying on their best chairs kind of sliding around on the deck of the movie because they didn't do anything anymore.

2:28:48Well, we definitely don't want to be there. 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 lot of that has to do with the interaction, the collaboration with robots and with the AI systems. I'm hoping there's a lot of interesting possibilities. I think that could be really cool, right? If you can 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.

2:29:22You wouldn't feel guilty about it. You know, he'd say, just do it. It's a machine. And I don't have to have quombs about that. You know, the ones that are machines, I also hope to see a future. And it is hope. I do have optimism on bot thy 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 a 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.

2:30:00There's an interesting long running study. Maybe it's an Harvard. They've 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. And they could, you know, I guess it remains to be seen whether they can establish that real deep.

2:30:50You 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 is going to be able to do that for you. Well, I have to say, it's in things of that already started happening for me, some of my best friends at robots. 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.

2:31:24If 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 he was sitting with me today and talking. He was a lot. So thank you so much. You're doing great work. Thank you, likes. I'm honored to be here and I appreciate it. It was fun. Thanks for listening to this conversation with Robert Plader. The support that's podcast, please check out our sponsors in the description. And now let me leave you with some words from Alan Turing in 1950 defining what is now termed the Turing test. A computer would deserve to be called intelligent if we could deceive a human into believing that it was human.

2:32:06Thank you for listening and hope to see you next time.