#412 – Marc Raibert: Boston Dynamics and the Future of Robotics

Marc Raibert is founder and former long-time CEO of Boston Dynamics, and recently Executive Director of the newly-created Boston Dynamics AI Institute. Please support this podcast by checking out our sponsors: – HiddenLayer: https://hiddenlayer.com/lex – Babbel: https://babbel.com/lexpod and use code Lexpod to get 55% off – MasterClass: https://masterclass.com/lexpod to get 15% off – NetSuite: http://netsuite.com/lex to get free product tour – ExpressVPN: https://expressvpn.com/lexpod to get 3 months free Transcript: https://lexfridman.com/marc-raibert-transcript EPISODE LINKS: Boston Dynamics AI Institute: https://theaiinstitute.com/ Boston Dynamics YouTube: https://youtube.com/@bostondynamics Boston Dynamics X: https://x.com/BostonDynamics Boston Dynamics Instagram: https://instagram.com/bostondynamicsofficial Boston Dynamics Website: https://bostondynamics.com/ PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ YouTube Full Episodes: https://youtube.com/lexfridman YouTube Clips: https://youtube.com/lexclips SUPPORT & CONNECT: – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/lexfridman – Twitter: https://twitter.com/lexfridman – Instagram: https://www.instagram.com/lexfridman – LinkedIn: https://www.linkedin.com/in/lexfridman – Facebook: https://www.facebook.com/lexfridman – Medium: https://medium.com/@lexfridman OUTLINE: Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time. (00:00) – Introduction (10:12) – Early robots (15:15) – Legged robots (33:55) – Boston Dynamics (37:13) – BigDog (45:20) – Hydraulic actuation (47:12) – Natural movement (52:59) – Leg Lab (59:51) – AI Institute (1:03:09) – Athletic intelligence (1:11:04) – Building a team (1:14:05) – Videos (1:21:53) – Engineering (1:25:21) – Dancing robots (1:30:08) – Hiring (1:34:00) – Optimus robot (1:42:30) – Future of robotics (1:47:24) – Advice for young people

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Lex Fridman?

  The following is a conversation with Mark Raibert, a legendary roboticist, founder and longtime CEO of Boston Dynamics, and recently, the Executive Director of the newly created Boston Dynamics AI Institute that focuses on research and the cutting edge, on creating future generations of robots that are far better than anything that exists today. He has been leading the creation of incredible-legged robots for over 40 years. At CMU, at MIT, the legendary MIT Leg Lab, and then, of course, Boston Dynamics with amazing robots like Big Dog, Atlas, Spot and Handle. This was a big honor and pleasure for me.

• 0:46Lex Fridman?

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• 1:30Lex Fridman?

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• 2:56Lex Fridman?

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• 3:38Lex Fridman?

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• 4:20Lex Fridman?

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• 5:02Lex Fridman?

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• 5:47Lex Fridman?

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• 6:29Lex Fridman?

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• 6:41Lex Fridman?

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• 7:24Lex Fridman?

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• 8:07Lex Fridman?

  That's netsuite.com/lex for your own KPI checklist. This episode is brought to you by ExpressVPN. I use them to protect my privacy on the internet. I've used them for many years. You know, it's the privacy thing, for sure. It's also the happiness thing. I press the button, turns on. I pick a location, instantaneously, space-time travel, solved. I don't understand what all the hoopla is about, like, faster than the light, travel is not possible. You click a button, if you click it fast enough, right there, you're a different location, a different country. It opens up all kinds of possibilities. Just press the button, close your eyes, and imagine you're...

• 8:51Lex Fridman?

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• 9:32Lex Fridman?

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• 9:47Lex Fridman?

  This is the Lex Fridman Podcast. To support it, please check out our sponsors in the description. And now, dear friends, here's Mark Reitberg.

• 10:12Lex Fridman?

  When did you first fall in love with robotics?

• 10:15Speaker 01

  Well, I was always a builder from a, from a young age. I was, I was lucky. My father was a frustrated engineer. And by that, I mean, he wanted to be an aerospace engineer, but his mom from the old country thought that that would be like a grease monkey. And so she said, no. So he became an accountant. But the, but the result of that was our basement was always full of tools and equipment and electronics. And, you know, from a young age, I would watch him assembling a kit, an Ico kit or something like that. I still have a couple of his old

• 10:52Speaker 01

  Ico kits. And, but it was really during graduate school when I followed a professor back from class. It was Bertolt Horn at MIT, and I was taking a An interim class, it's IAP, Independent Activities Period. And I followed him back to his lab. And on the table was a Vic arm, robot arm, taken apart in probably a thousand pieces. And when I saw that, you know, from that day on, I was a roboticist.

• 11:26Lex Fridman?

  Do you remember the year?

• 11:28Speaker 01

  1974.

• 11:29Lex Fridman?

  1974, so there's just this arm in pieces.

• 11:32Speaker 01

  Yeah.

• 11:33Lex Fridman?

  And you saw the pieces and you saw in your, in your vision, the, the arm, when it's put back together and the possibilities that holds.

• 11:41Speaker 01

  Somehow it spurred my imagination. I was in the Brain and Cognitive Sciences Department as a graduate student doing neurophysiology. I'd been a electrical engineer as an undergrad at Northeastern. And the neurophysiology wasn't really working for me, you know, it wasn't conceptual enough. I couldn't see really how by looking at single neurons, you were going to get to a place where you could understand, you know, control systems or thought or anything like that. And, you know, the AI lab was always an appealing, this was before C-Sale, right? This was in the seventies, so the AI lab was always an appealing idea. And so when I went back to the AI lab with, you know, following him and I saw the arm, I just thought, you know, this is it.

• 12:27Lex Fridman?

  It's so interesting, the tension between the BCS, Brain and Cognitive Science approach to understanding intelligence and the robotics approach to understanding intelligence.

• 12:38Speaker 01

  Well, BCS is now morphed a bit, right? They, they have the Center for Brains, Minds and Machines, which is trying to bridge that gap. And even when I was there, you know, David Marr was in the AI lab. David Marr had models of the brain that were appealing both to biologists, but also to computer people. So he was a visitor in the AI lab at the time, and I guess he became full time there. So that was the first time a bridge was made between those two groups. Then the bridge kind of went away and then there was another time in the eighties. And then recently, you know, the last five or so years, there's been a stronger connection.

• 13:16Lex Fridman?

  You said you were always kind of a builder. What stands out to you in memory of a thing you've built? Maybe a trivial thing that just kind of like inspired, inspired you in the possibilities that this direction of work might hold.

• 13:30Speaker 01

  I mean, we were just doing gadgets when we were kids, you know, I had a friend, we were. Taking, you know, the, I don't know if everybody remembers, but fluorescent lights had this little aluminum cylinder, I can't even remember what it's called now, that you needed a starter, I think it was. And we would take those apart, fill them with match heads, put a tail on it and make it into little rockets.

• 13:54Lex Fridman?

  So it wasn't always about function, it was, well.

• 13:58Speaker 01

  Well, rocket was pretty much.

• 14:00Lex Fridman?

  I guess that is pretty functional. But yeah, I guess that is a question. How much was it about function versus just creating something cool?

• 14:07Speaker 01

  I think it's, it's still a balance between those two. There was a time though, when I was, I guess I was probably already a professor or maybe late in graduate school, when I thought that function was everything and that, you know, mobility, dexterity, perception and intelligence, those are sort of the function, the key functionalities for robotics, that that's what mattered and nothing else mattered. And I even had the kind of this platonic ideal that a robot, if you just looked at a robot and it wasn't doing anything, it would look like a pile of junk, which a lot of my robots looked like in those, in those days. But then when it started moving, you'd get the idea that it, you know, it had some

• 14:48Speaker 01

  kind of life or some kind of interest in its movement. And I think we purposely even designed the machines, not, not worrying about the aesthetics of the, of the structure itself. But then, but then, you know, it turns out that the aesthetics of the thing itself add and combine with the lifelike things that the robots can do. But the heart of it is, you know, making them do things that are, that are interesting.

• 15:15Lex Fridman?

  So one of the things that underlies a lot of your work is that the robots you create, the systems you have created for, for over 40 years now, have a kind of, they're not cautious. So a lot of robots that people know about move about this world very cautiously, carefully, very afraid of the world. A lot of the robots you built, especially in the early days, were very aggressive, under-actuated. They're hopping. They're wild, moving quickly. So is there a philosophy underlying that?

• 15:49Speaker 01

  Well, let me tell you about how I got started on legs at all. When I was still a graduate student, I went to a conference. It was a biological-legged locomotion conference, and I think it was in Philadelphia. So it was all biomechanics people, researchers who would look at muscle and maybe neurons and things like that. They weren't so much computational people, but they were more biomechanics. And maybe there were 1,000 people there. And I went to a talk. One of the talks, all the talks were about the body of either animals or people and respiration, things like that. But one talk was by a robotics guy, and he showed a six-legged robot that walked very slowly. It always had at least three feet

• 16:34Speaker 01

  on the ground, so it worked like a table or a chair with tripod stability. And it moved really slowly. And I just looked at that and said, wow, that's wrong. That's not anything like how people and animals work, because we bounce and fly. We have to predict what's going to happen in order to keep our balance when we're taking a running step or something like that. We use the springiness in our legs, our muscles in our tendons and things like that as part of the story. The energy circulates. We don't just throw it away every time. So I'm not sure I understood all that when I first thought, but I definitely got inspired to say, let's try the opposite. And I didn't have a clue as to how to make

• 17:18Speaker 01

  a hopping robot work, not balancing 3D. In fact, when I started, it was all just about the energy of bouncing. And I was going to have a springy thing in the leg and some actuator so that you could get an energy regime going of bouncing. And the idea that balance was an important part of it didn't come until a little later. And then I made the one-legged, the pogo stick robots. Now I think that we need to do that in manipulation. If you look at robot manipulation, we've been working, we, a community, has been working on it for 50 years. We're nowhere near human levels of manipulation. I mean, it's come along. But I think it's all too safe.

• 18:03Speaker 01

  And I think trying to break out of that safety thing of static grasping, you know, if you look at the, a lot of work that goes on, it's about the geometry of the part and then, and then you figure out how to move your hands so that you can position it with respect to that. And then you grasp it carefully and then you move it. Well, that's not anything like how people and animals work, you know? We juggle in our hands. We hug multiple objects and can sort them. So now, to be fair, uh, being more aggressive is going to mean things aren't going to work very well for a while. So it's a long, it's a longer term approach to the problem. But that, and that's just theory now, you know? Maybe that won't pay off, but that's sort of how I'm trying to think about it.

• 18:47Speaker 01

  Trying to encourage our group to, to go at it.

• 18:51Lex Fridman?

  Well, yeah, I mean, we'll talk about what it means to, what is the actual thing we're trying to optimize for a robot? You know, sometimes, especially with human robot interaction, maybe flaws is a good thing. Uh, perfection is not necessarily the right thing to be chasing. Just like you said, maybe, maybe being good at fumbling an object, uh, being good at fumbling might be the right thing to optimize versus perfect modeling of the object and perfect movement of the arm to grasp that object. Cause maybe perfection is not supposed to exist in the real world.

• 19:25Speaker 01

  I don't know if you know my friend, Matt Mason, who's, uh, who was the director of the robotics institute at Carnegie Mellon? And we go back to graduate school together. But he analyzed, um, a movie of Julia Childs doing a cooking thing. And she did, I think he said something like there were 40 different ways that she handled a thing and none of them was grasping. He would, she would nudge, roll, flatten with her, you know, knife, things like that. And none of them was grasping.

• 19:56Lex Fridman?

  So, okay, let's go back to the early days. First of all, you've created and led the Leg Lab, the legendary Leg Lab at MIT. So what was that first hopping robot? Can you?

• 20:06Speaker 01

  But first of all, the Leg Lab actually started at Carnegie Mellon.

• 20:09Lex Fridman?

  Carnegie Mellon.

• 20:10Speaker 01

  So I was a professor there, starting in 1980, in about 1986. And so that's where the first hopping machines were built, starting, I guess we got the first one working in about 1982, something like that. That was a simplified one. Then we got a three dimensional one in 1983. The quadruped that we built at the Leg Lab, the first version, was built in about 1984 or '05 and really only got going about '86 or so. And it took years of development to get it to.

• 20:45Lex Fridman?

  Let's just pause here. For people who don't know, I'm talking to Mark Raibert, founder of Boston Dynamics. But before that, you were a professor developing some of the most incredible robots for 15 years. And before that, of course, a grad student and all that. So you've been doing this for a really long time. So you like skipped over this, but like go to the first hopping robot. There's videos of some of this. I mean, these are incredible robots. So you talked about the very first step was to get a thing hopping up and down.

• 21:14Speaker 01

  Right.

• 21:15Lex Fridman?

  And then you realized, well, balancing is a thing you should care about, and it's actually a solvable problem. So can you just go through how to create that robot? What was involved in creating that robot?

• 21:28Speaker 01

  Well, I'm going to start on the, not the technical side, but the, I guess we could call it the motivational side or the funding side. So before Carnegie Mellon, I was actually at JPL, at the Jet Propulsion Lab for three years. And while I was there, I connected up with Ivan Sutherland, who is sometimes regarded as the father of computer graphics because of work he did both at MIT and then University of Utah and at Vincent Sutherland. Anyway, I got to know him. And at one point, he said, he encouraged me to do some kind of project at Caltech, even though I was at JPL. You know, those are kind of related institutions. And.

• 22:14Speaker 01

  So I, I thought about it and I made up a list of three possible projects. And I purposely made the top one and the bottom one really boring sounding. And in the middle, I put a pogo stick robot. And when he looked at it, you know, Ivan is a brilliant, you know, brilliant guy, brilliant engineer and a real cultivator of people. He looked at it and knew right away what the thing that was worth doing. And so he, you know, he had an endowed chair. So he had about $3,000 that he gave me to build the first model, which I went, you know, I went to the shop and with my own hands, kind of made a first model, which, which didn't work and was just, you know, a, a beginning shot at it.

• 22:59Speaker 01

  And Ivan and I took that to Washington. And in those days, you could just walk into DARPA and walk down the hallway and see who's there.

• 23:08Lex Fridman?

  Yeah.

• 23:08Speaker 01

  Ivan, who had been there in his previous life. And so we walked around and we looked in the offices. Of course, I didn't know anything, you know, I was basically a kid, but Ivan knew his way around. And we found Craig Fields in his office. Craig later became the director of DARPA, but in those days, he was a program manager. And so we went in, I had a little Samsonite suitcase, which we opened and it had just the skeleton of this one-legged hopping robot. And we showed it to him and you could almost see the drool going down his chin.

• 23:39Lex Fridman?

  Cause he's excited.

• 23:40Speaker 01

  Of excitement. And he sent me $250,000. He said, okay, I'll, I want to fund this. And I was between institutions. I was just about to leave JPL and I hadn't decided yet where I was going next. And then when I landed at CMU, he sent $250,000, which in 1980 was a lot of, a lot of research money.

• 24:02Lex Fridman?

  Did you see the possibility of where this is going? Why this is an important problem?

• 24:07Speaker 01

  No.

• 24:07Lex Fridman?

  The balance, I mean, it's legged, it has to do with legged locomotion. I mean, it has to do with all these problems that, that, that are, the, the human body solves when we're walking, for example, like all the fundamentals are there.

• 24:19Speaker 01

  Yeah. I mean, I think that was the motivation to try and get more of the fundamentals of how animals work. But the idea that it would result in, you know, machines that were anything like practical, like we're making now, that, that wasn't anywhere in my head, no. You know, as an academic, I was mostly just trying to do the next thing, you know, make some progress, impress my colleagues if I could.

• 24:42Lex Fridman?

  And have fun.

• 24:43Speaker 01

  And have fun.

• 24:44Lex Fridman?

  Pogo stick robot.

• 24:45Speaker 01

  Pogo stick robot.

• 24:46Lex Fridman?

  So what was on the technical side, what are the some of the challenges of getting up, getting to the point where we saw like in the video, the the pogo stick robot that's actually successfully hopping and then eventually doing flips and all this kind of stuff?

• 24:59Speaker 01

  Well, in the very early days, I needed some better engineering than I had than I could do myself. And I hired Ben Brown. We each had our way of contributing to the design. And we came up with a thing that could could start to work. I had some stupid ideas about how the actuation system should work. And we, you know, we sorted that out. It wasn't that hard to make it balanced. Once you get the the physical machine to be working well enough, and have enough control over the degrees of freedom. And then we very quickly, you know, we started out by having it floating on an inclined air table. And then that only gave us like six foot of travel. So once it started working, we

• 25:41Speaker 01

  switched to a thing that could run around the room on a another device. It's hard to explain these without you seeing them, but you probably know what I'm talking about, a planarizer. And and then the next big step was to make it work in 3D, which that was really the scary part with these simple things. You know, people had inverted pendulums at the time for for years, and they could control them by driving a cart back and forth. But could you make it work in three dimensions while it's bouncing and all that? And but it turned out, you know, not to be that hard to do, at least at the level of performance we achieved at the time.

• 26:14Lex Fridman?

  So, okay, you mentioned inverted pendulum, but like, can you explain how a hopping stick in 3D can control, can balance itself?

• 26:26Speaker 01

  Yeah.

• 26:26Lex Fridman?

  What what does the actuation look like?

• 26:29Speaker 01

  You know, the simple story is that there's three things going on. There's something making it bounce. And, you know, we, we had a system that was estimating how high the robot was off the ground and using that, you know, There's energy that can be in three places in a pogo stick. One is in the spring, one is in the altitude, and the other is in the velocity. And so when at the top of the hop, it's all in the height. And so you could just measure how high you're going and thereby thereby have an idea of a lot about the cycle and you could decide whether to put more energy in or less. So that's one element. Then there's a part that you decide where to put the foot.

• 27:11Speaker 01

  And if you think when you're landing on the ground with respect to the center of mass. So if you think of a pole vaulter, the key thing the pole vaulter has to do is get its body to the right place when the pole gets stuck. If they're too far forward, they kind of get thrown backwards. If they're too far back, they go, you know, over. And what they need to do is get it so that they go mostly up to get over the thing. And, you know, high jumpers is the same kind of thing. So there's a calculation about where to put the foot. And we did something relatively simple. And then there's a third part to keep the body at an attitude that's upright. Because if it gets too far, you know, you could hop and just keep rotating around.

• 27:53Speaker 01

  But if it gets too far, then you run out of motion of the joints at the hips. So you have to do that. And we did that by applying a torque between the legs and the body every time the foot's on the ground. You only can do it while the foot's on the ground. In the air, you know, it, it, the physics don't work out.

• 28:11Lex Fridman?

  How far does it have to tilt before it's too late to be able to balance itself? Or it's impossible to balance itself, correct itself?

• 28:18Speaker 01

  Well, you're asking an interesting question because in those days, we didn't actually optimize things. And they probably could have gone much further than we did and then had higher performance. And we just kind of got, you know, a sketch of a solution and worked on that. And then in years since, some people working for us, some people working for others, people came up with all kinds of equations for, or, you know, algorithms for how to do a better job, be able to go faster. One of my students worked on getting things to go faster. Another one worked on climbing over obstacles. Because when you're running, it's one, on the open ground, it's one thing. If you're running like up a stair.

• 29:01Speaker 01

  You have to adjust where you are, otherwise things don't work out right. You land your foot on the edge of the step. So there's other degrees of freedom to control if you're getting to, you know, more realistic, practical situations.

• 29:12Lex Fridman?

  I think it's really interesting to ask about the early days because, you know, believing in yourself, believing that there's something interesting here. And then you mentioned finding somebody else, Ben Brown. What's that like, finding other people with whom you can build this crazy idea and actually make it work?

• 29:28Speaker 01

  Probably the smartest thing I ever did is to find the other people. I mean, when I look at it now, you know, I look at Boston Dynamics and all the really excellent engineering there, you know, people who really make stuff work. You know, I'm, I'm only the, the dreamer.

• 29:44Lex Fridman?

  So when you talk about pogo stick robot or legged robots, whether it's quadrupeds or humanoid robots, did people doubt that this is possible? Did you experience a lot of people around you kind of?

• 29:57Speaker 01

  I don't, I don't know if they doubted whether it was possible, but I think they thought it was a waste of time.

• 30:02Lex Fridman?

  Oh, it's not even an interesting problem.

• 30:04Speaker 01

  I think for a lot of people, you know, people who were, I think it's been, it's been both though. Some people, I think, I felt like they were saying, oh, you know, why are you wasting your time on this stupid problem? And then, but then I've been at many things where people have told me it's been an inspiration to, to go out and, you know, attack these, these harder things. And, and I think it has turned out, I think legged locomotion has turned out to be a useful thing.

• 30:34Lex Fridman?

  Did you ever have like doubt about bringing Atlas to life, for example, or, or with Big Dog? Just every step of the way, did you have doubt like what, this is, this is too hard of a problem?

• 30:47Speaker 01

  I mean, at first I wasn't an enthusiast for the humanoids because again, it goes back to saying, what's the functionality? And the form wasn't as important as the functionality. And I, and also. You know, there's an aspect to humanoid robots that's about, all about the cosmetics, where there isn't really other functionality. And that kind of is off putting for me as a roboticist. I think the functionality really matters. So probably that's why I avoided humanoid robots to start with. But I'll tell you, now, you know, after we started working on them, you could see that the connection and the impact with, with other people, whether

• 31:29Speaker 01

  they're laypeople or even other technical people, there's a, there's a special thing that goes on, even though most of the humanoid robots aren't that much like a person.

• 31:39Lex Fridman?

  But we anthropomorphize and we see the humanity. But also like with, with Spot, you can see, not the humanity, but whatever we find compelling about social interactions, there in Spot as well.

• 31:53Speaker 01

  I'll tell you, you know, I go around giving talks and take Spot to, to a lot of them. And it's amazing. The media likes to say that they're terrifying and, and that people are afraid. And, and YouTube commentators like to say that it's frightening. But when you take a Spot out there, now, maybe it's self-selecting, but you get a crowd of people who want to take pictures, want to pose for selfies, want to operate the robot, want to pet it, want to put clothes on it. It's amazing.

• 32:21Lex Fridman?

  Yeah, I love Spot. So if we move around history a little bit, so you said, I think in the early days of Boston Dynamics, that you quietly worked on making a running version of Ibo.

• 32:32Speaker 01

  Yeah.

• 32:33Lex Fridman?

  Sony's robot dog?

• 32:34Speaker 01

  Yeah.

• 32:34Lex Fridman?

  It's just an interesting little tidbit of history for me. What, what, like, what stands out to you in memory from that task? For people who don't know, that little dog robot moves slowly. How did that become big dog? What was involved there? What was the dance between how do we make this cute little dog versus a thing that can actually carry a lot of payload and move fast and stuff like that?

• 32:57Speaker 01

  What the connection was is that at that point, Boston Dynamics was mostly a physics-based simulation company. So when I left MIT to start Boston Dynamics, you know, there was a few years of overlap. But the concept wasn't to start a robot company. The concept was to use this dynamic simulation tool that we developed to do robotics for other things. But working with Sony, we got back into robotics by doing the iBo Runner. We made some tools for programming Curio, which was a humanoid this big that could do some dancing and other kinds of fun stuff. And I don't think it ever reached the market, even though

• 33:37Speaker 01

  they did show it. You know, when I look back, I say that we got us back where we belonged.

• 33:43Lex Fridman?

  Yeah. You rediscovered the soul of the company.

• 33:46Speaker 01

  That's right.

• 33:47Lex Fridman?

  And so from there, it was always about robots.

• 33:49Speaker 01

  Yep.

• 33:51Lex Fridman?

  So you started Boston Dynamics in 1992.

• 33:55Speaker 01

  Right.

• 33:56Lex Fridman?

  What are some fond memories from the early days?

• 34:00Speaker 01

  One of the robots that we built wasn't actually robotic. It was a surgical simulator, but it had force feedback. So it had all the techniques of robotics. And you looked down into this mirror, it actually was, and it looked like you were looking down onto the body you were working on. Your hands were underneath the mirror, so there were where you were looking. And you had tools in your hands that were connected up to these force feedback devices made by another MIT spinout, Sensible Technologies. So they made the force feedback device. We attached the tools and we wrote all the software and did all the graphics. So we had 3D computer graphics. It was in the old days when, this

• 34:41Speaker 01

  was in the late 90s, when you had a silicon graphics computer that was about this big. You know, it was the heater in the office, basically.

• 34:50Lex Fridman?

  Nice.

• 34:51Speaker 01

  And we were doing surgical operations, anastomosis, which was stitching tubes together, you know, tubes like blood vessels or other things in their body. And you could feel and you could see the tissues move, and it was really exciting. And the idea was to make a trainer to teach surgeons how to do stuff. We built a scoring system because we interviewed surgeons that told us, you know, what you're supposed to do and what you're not supposed to do. You're not supposed to tear the tissue. You're not supposed to touch it in any place except for where you're trying to engage. There were a bunch of rules. So we built this thing and took it to a trade show, a surgical trade show. And the surgeons were practically lined up.

• 35:33Speaker 01

  Well, we, we kept the score and we posted their scores like on a video game. And those guys are so competitive that they really, uh, really loved doing it. And they would come around and they see someone's score was higher there. So they would come back. But we figured out shortly after that we thought surgeons were going to pay us to get trained on these things. And the surgeons thought we should pay them in order to, uh, so they could teach us about the thing. And there was no money from the surgeons. And we looked at it and thought, well, maybe we could sell it to hospitals that would teach, train their surgeons. And then we said, well, we're this, at the time, we were probably a 12 person company or maybe 15 people. I don't remember. There's no way we could go after a marketing activity.

• 36:17Speaker 01

  You know, the company was all bootstrapped in those years. We, we never had investors until Google bought us, which was after 20 years. So we didn't have any resources to, uh, to go after hospitals. So we, at one, sort of at one day, Rob and I were looking at that and we said, uh, we'd built another simulator for knee arthroscopy. And we said, this isn't going to work. And we killed it and we moved on. And that was really a milestone in the company because we, you know, we sort of understood who we were and, uh, and what would work and what wouldn't. Even though technically it was really a fascinating thing.

• 36:53Lex Fridman?

  What was that meeting like? Were you just like sitting at a table, you know what?

• 36:58Speaker 01

  Probably.

• 36:58Lex Fridman?

  We're going to pivot completely. We're going to let go of this thing we put so much hard work into. And then go back to the thing.

• 37:07Speaker 01

  It just always felt right, well, unless we did it, you know?

• 37:10Lex Fridman?

  Just look at each other and said, let's, let's build robots.

• 37:13Speaker 01

  Yeah.

• 37:13Lex Fridman?

  What was the first robot you built under the, the flag of Boston Dynamics? Big Dog?

• 37:19Speaker 01

  Well, there was the Ibo, uh, runner, but it wasn't even a whole robot. It was just legs that we, we took off the legs on Ibo's and attached, uh, legs we'd made. And, um, you know, we got that working and showed it to the Sony people. Uh, we worked pretty closely with Sony in those years. One of the interesting things is that it was before the internet and Zoom and anything like that. So we had six ISDN lines installed and we would have a telecon every week. That worked at very low frame rate, something like 10 hertz. You know, English across the boundary with Japan was a challenge. Trying to understand what, what each of us was saying and have meetings every week.

• 38:04Speaker 01

  For, for several years doing that. And it was a pleasure working with them. They were really supporters. They, they seemed to like us and what we were doing. That was the real transition from us being a simulation company into being a robotics company again.

• 38:20Lex Fridman?

  It was a quadruped, the, the legs were four legs or two legs?

• 38:23Speaker 01

  Yeah, no, four legs, yeah.

• 38:24Lex Fridman?

  And what did you learn from that experience of building basically a fast moving quadruped?

• 38:32Speaker 01

  Mostly we learned that something that's small. Doesn't look very exciting when it's running. It's like it's scampering and you had to, you had to watch a slow mo. For it to look like it was interesting. If you watch it fast, it was just like a.

• 38:46Lex Fridman?

  That's funny.

• 38:46Speaker 01

  One of my things was to show stuff in video, even from the very early days of the hopping machines.

• 38:53Speaker 01

  And so I was always focused on how's this going to look through the viewfinder and. Running IBO didn't look so cool through the viewfinder.

• 39:00Lex Fridman?

  So what, what came next? In terms of, what was a big next milestone in terms of a robot you built?

• 39:08Speaker 01

  I mean, you got to say that Big Dog was, you know, sort of put us on the map and got our heads. Really pulled together. We scaled up the company. Big Dog was the result of Alan Rudolph at DARPA. Starting a biodynamics program. And he put out a, you know, a request for proposals and. I think there were 42 proposals written and three got funded. One was Big Dog, one was a climbing robot, Rise. And you know, that put things in motion. We hired Martin Bueller. He was a professor in Montreal at McGill. He was incredibly important for getting Big Dog out of the lab and into the mud,

• 39:52Speaker 01

  which is a, you know, was a key step to really be willing to go out there and, and build it, break it, fix it, which is sort of one of our mottos at the company.

• 40:00Lex Fridman?

  So testing in the real world.

• 40:02Speaker 01

  Testing.

• 40:02Lex Fridman?

  For people who don't know, Big Dog, maybe you can correct me, but it's a, it's a big quadruped, four leg robot. It looks big, could probably carry a lot of weight. Not the most weight that Boston Dynamics has built, but a lot.

• 40:17Speaker 01

  Well, this is the first thing that worked. So let's see, if we go back to the leg lab, we'd built a quadruped that could do many of the things that Big Dog did, but it had a hydraulic pump sitting in the room with hoses connected to the robot. It had a VAX computer in the next room. It needed its own room because it was this giant thing with air conditioning. And it had this very complicated bus connected to the robot. And the robot itself just had the actuators. It had gyroscopes for sensing and other, some other sensors. But all the power and computing was off board. Big Dog had all that stuff integrated on the platform. It had a gasoline engine for power, which was a very complicated thing to, to undertake.

• 41:00Speaker 01

  It had to convert the rotation of the engine into hydraulic power, which is how we actuated it. So there was a lot of learning just on the, you know, building the physical robot and the system integration for that. And then there was the controls of it.

• 41:17Lex Fridman?

  So for Big Dog, you brought it all together onto one platform.

• 41:21Speaker 01

  Right.

• 41:21Lex Fridman?

  And then so you can.

• 41:23Speaker 01

  You could take it out in the woods.

• 41:24Lex Fridman?

  Yeah, and you did.

• 41:25Speaker 01

  We did. We spent a lot of time down at the Marine Corps base in Quantico, where there was a trail called the Guadalcanal Trail. And our milestone that DARPA had specified was that we could go on this one particular trail that involved, you know, a lot of challenge. And we spent a lot of time, our team spent a lot of time down there. Those were fun days.

• 41:48Lex Fridman?

  Hiking with a robot. So what did you learn about like what it takes to balance a robot like that on a trail? And a hiking trail in the woods, basically forget the woods, just the real world. That's the big leap into testing in the real world.

• 42:03Speaker 01

  Yeah. As challenging as the woods were, working inside of a home or in an office is really harder.

• 42:13Lex Fridman?

  Yeah.

• 42:13Speaker 01

  Because when you're in the woods, you can actually take any path up the, up the hill. All you have to do is avoid the obstacles. You, there's no such thing as damaging the woods, at least, you know, to first order. Whereas if you're in a house, you can't leave scuff marks, you can't bang into the walls. The robots aren't very comfortable bumping into the walls, especially in the early days. So I think those were actually bigger challenges once, once we faced them. It was mostly, you know, getting the systems to work well enough together, the, the hardware systems to work and, and the controls. In those days, we did have a human operator who did all the visual perception going up the Guadalcanal Trail. So there was an operator who was right there, who was very skilled at, even though the

• 42:58Speaker 01

  robot was balancing itself and placing its own feet, if the operator didn't do the right thing, it wouldn't go. But years later, we went back with one of the electric, the precursor to Spot. And we had advanced the controls and everything so much that an amateur, complete amateur could operate the robot the first time, up and down and up and down, whereas it'd taken us years to, to get there in the previous robot.

• 43:24Lex Fridman?

  So if you fast forward, Big Dog eventually became Spot.

• 43:27Speaker 01

  So Big Dog became LS3, which is the big load carrying one.

• 43:31Lex Fridman?

  Just, just a quick pause, it can carry 400 pounds.

• 43:35Speaker 01

  It was designed to carry 400, but we had it carrying about a thousand pounds.

• 43:40Lex Fridman?

  Of course you did. Just to make sure.

• 43:42Speaker 01

  We had, we had one carrying the other one, we had two of them. So we had one carrying the other one. There's a little clip of that, we should put that out somewhere. That's from like 20 years ago, but.

• 43:51Lex Fridman?

  Wow. Wow. And it can go for very long distances, it can travel 20 miles.

• 43:56Speaker 01

  Yeah. Gasoline.

• 43:58Lex Fridman?

  Gasoline, yeah. And that, that eventually just, okay, sorry. So LS3, then what, how did that lead to Spot?

• 44:06Speaker 01

  So Big Dog and LS3 had engine power and hydraulic actuation. Then we made a robot that was.

• 44:19Speaker 01

  Electric power, so there's a battery driving a motor, driving a pump, but still hydraulic actuation.

• 44:27Lex Fridman?

  Yeah.

• 44:27Speaker 01

  Larry sort of asked us, could you make something that weighed 60 pounds? That would not be so intimidating if you had it in a house where there were people. And that was the inspiration behind the spot, pretty much as it exists today. We did a prototype, the same size. That was the first all-electric, non-hydraulic robot.

• 44:47Lex Fridman?

  What was the conversation with Larry Page like about? So here's a guy that kind of is very product focused and can see a vision for like what the future holds. That's just interesting kind of aside. What was the brainstorm about the future robotics with him like?

• 45:03Speaker 01

  I mean, it was almost as simple as what I just said. He, you know, we're having a meeting. He said, yeah, geez, you know, do you think you could make a smaller one? That wouldn't be so intimidating, like a big dog?

• 45:13Lex Fridman?

  Yeah.

• 45:15Speaker 01

  If it was in your house. And I said, yeah, we could do that. And we started and did.

• 45:21Lex Fridman?

  Is there a lot of technical challenges to go from hydraulic to electric?

• 45:25Speaker 01

  You know, I had been in love with hydraulics and still love hydraulics. You know, it's a great technology. It's too bad that somehow the world out there looks at it like it's old fashioned or that it's icky. And it's true that you do, it is very hard to keep it from having some amount of dripping from time to time. But if you look at the performance, you know, how strong you can get in a lightweight package. And of course, we did a huge amount of innovation. Most of hydraulic control, that is the valve that controls the flow of oil, had been designed in the 50s for airplanes. It had been made robust enough, safe enough that you could count on it so that.

• 46:10Speaker 01

  Humans could fly in airplanes and very little innovation had happened. You know, that might not be fair to the people who make the valves. I'm sure that they did innovate, but the basic design had stayed the same. And there was so much more you could do. And so our engineers designed valves, the ones that are in in Atlas, for instance, that had new kinds of circuits. They sort of did some of the computing that could get you much more efficient use. They were much smaller and lighter so that the whole robot could be smaller and lighter. We made a hydraulic power supply that had a bunch of components integrated in this tiny package. It's about this big, you know, the size of a football, weighs five kilograms and it

• 46:54Speaker 01

  produces five kilowatts of power. Of course, it has to have a battery operating, but it's got a motor, a pump, filters, heat exchanger to keep it cool, some valves, all of it, all in this tiny little package. So hydraulics, you know, could still have a ways to go.

• 47:12Lex Fridman?

  One of the things that stands out about the robots Boston and Amazon have created is how beautiful the movement is, how natural the walking is and running is, even flipping is, throwing is. So maybe you can talk about what's involved in making it look natural.

• 47:31Speaker 01

  Well, I think having good hardware is part of the story and people who think you don't need to innovate hardware anymore are wrong, in my opinion. So I think one of the things, certainly in the early years, for me, taking a dynamic approach where you think about what's the evolution of the motion of the thing going to be in the future and having a prediction of that that's used at the time that you're giving signals to it, as opposed to it all being servoing, which is servoing is sort of backward looking. It says, okay, where am I now? I'm going to, I'm going to try and adjust for that. But you really need to think about what's coming.

• 48:08Lex Fridman?

  So how far had you, do you have to look in time?

• 48:12Speaker 01

  It's interesting. I think that the number is only a couple of seconds for spot. So there's a limited horizon type approach where you're recalculating, assuming what's going to happen in the next second or second and a half. And then you keep iterating, you know, the next, even though a tenth of a second later, you'll say, okay, let's do that again and see what's happening. And you're looking at what the obstacles are, where the feet are going to be placed, how to, you know, you have to coordinate a lot of things if you have obstacles and you're balancing at the same time. And it's that limited horizon type calculation that's doing a lot of that. But if you're doing something like a somersault, you're looking out a lot further, right?

• 48:53Speaker 01

  If you want to stick the landing, you have to get the, you have to, at the time of launch, have, you know, momentum and rotation, all those things coordinated so that a landing is within reach.

• 49:06Lex Fridman?

  How hard is it to stick a landing? I mean, it's very much under actuated, like you, once you've in the air, you don't have as much control about anything. So how hard is it to get that to work? First of all, did flips with a hopping robot.

• 49:25Speaker 01

  If you look at the first time we ever made a robot do a somersault, it was in a planer robot, you know, it had a boom, so it could only, it was restricted to the surface of a sphere, we call that planer. So it could move four and a half, it could go up and down, and it could rotate. And so the calculation of what you need to do to get a, to stick a landing, isn't all that complicated. You have to look at, you know, you have to get time to make the rotation. So how hard you jump, how high you jump gives you time. You look at how quickly you can rotate. And so, you know, if you get those two right, then when you land, you have the feet in the right place. And you have to get rid of all that rotational and linear momentum. But, you know, that's not too hard to figure out.

• 50:10Speaker 01

  And we made, you know, back in about 1985 or six, I can't remember, we had a simple robot doing somersaults. To do it in 3D, really the calculation is the same, you just have to be balancing in the other degrees of freedom. If you're just doing a somersault, it's just a, a planer thing. Roy Rob was my graduate student and we were at MIT, which is when we made, you know, a two-legged robot do a 3D somersault for the first time. There, we, in order to get enough rotation rate, you needed to do tucking also. You know, withdraw the legs in order to accelerate it. And he did some really fascinating work on, on how you stabilize more complicated maneuvers. Remember, he was a gymnast, a champion gymnast before he'd come to me.

• 50:53Speaker 01

  So he had, he had the physical abilities and he was a, you know, an engineer. So he could translate some of that into the math and the algorithms that you need to, to do that.

• 51:05Lex Fridman?

  He knew how humans do it. He just had to get robots to do the same.

• 51:09Speaker 01

  Unfortunately, though, when you, humans don't really know how they do it.

• 51:12Lex Fridman?

  Yeah.

• 51:13Speaker 01

  Right? We, we're coached, we, we have ways of learning, but do we really understand in a physical, in a physics way, what we're doing? Probably most gymnasts and athletes don't know.

• 51:26Lex Fridman?

  So in some way, by building robots, you are in part understanding how humans do, like walking. Most of us walk without considering how we walk, really.

• 51:36Speaker 01

  Right.

• 51:36Lex Fridman?

  And how we make it so natural and efficient, all those kinds of things.

• 51:39Speaker 01

  Atlas still doesn't walk like a person and it still doesn't walk quite as gracefully as a person, even though it's been getting closer and closer. The running might be close to a human, but the walking is still a, a challenge.

• 51:51Lex Fridman?

  That's interesting, right? That, that running is closer to a human. It just shows that the more aggressive and kind of the more you leap into the unknown, the more natural it is. I mean, walking is kind of falling always, right?

• 52:05Speaker 01

  And something weird about the knee, that you can kind of do this folding and unfolding and get it to work out just, a human can get it to work out just right. There's compliances. Compliance means springiness in the, in the design that are important to how it all works. Well, we used to have a motto at the Boss Dynamics in the early days, which was, you have to run before you can walk.

• 52:28Lex Fridman?

  And that's a, that's a good motto because you also had Wildcat, which was one of the along the way towards Spot, which is a quadruped that went 19 miles an hour.

• 52:38Speaker 01

  Right.

• 52:38Lex Fridman?

  On flat terrain. Is that the fastest you've ever built?

• 52:42Speaker 01

  Oh, yeah.

• 52:42Lex Fridman?

  Might be the fastest quadruped in the world. I don't know.

• 52:45Speaker 01

  For a quadruped, probably. Of course, it was probably the loudest, too. So we had this little racing go-kart engine on it. And we would get people from, you know, three buildings away, uh, sending us, you know, complaining about how loud it was.

• 53:00Lex Fridman?

  So at the Leg Lab, I believe most of the robots didn't have knees.

• 53:06Lex Fridman?

  How, what's the, how do you figure out what is the right number of actuators? What, what are the joints to have? What do you need to have? Uh, you know, we humans have knees and all kinds of interesting stuff on the feet. The toe is an important part, I guess, for humans. Or maybe it's not. I injured my toe recently and it made running very unpleasant. So that seems to be kind of important. So how do you figure out for efficiency, for function, for aesthetics, uh, how many joints to have, how many actuators to have?

• 53:37Speaker 01

  Well, it's always a balance between wanting to get where you really want to get and what's practical to do based on, uh, your resources or what you know and all that. So, I mean, the whole idea of the, of the pogo stick was to do a simplification. Obviously, it didn't look like a human. I think a technical scientist could appreciate that we were capturing some of the things that are important in human locomotion without it looking, uh, like it, without having a knee and ankle. I'll tell you the first sketch that Ben Brown made, uh, when we were talking about building this thing, was a very complicated thing with zillions of springs, lots of joints.

• 54:19Speaker 01

  It looked like, much more like a, a kangaroo or a, or an ostrich or something like that. Things we were paying a lot of attention to at the time. Um, you know, so my job was to say, okay, well, let's do something simpler to get started. And maybe we'll get there at some point.

• 54:38Lex Fridman?

  I just love the idea that you, you, you two were studying kangaroos and ostriches.

• 54:43Speaker 01

  Oh, yeah, we, we did, uh, we filmed and, and digitized, uh, uh, data from horses. I did, I did a dissection of a ostrich at one point, which has absolutely remarkable legs.

• 54:56Lex Fridman?

  Dumb question, uh, do ostriches have like muscle, a lot of musculature on the legs or no?

• 55:01Speaker 01

  Most of it's up in the feathers, but there's a huge amount going on in the feathers, including a knee joint. The knee joint's way up there. The thing that's halfway down the leg that looks like a backwards knee is actually the ankle. The thing on the ground, which looks like the foot is actually the toes. It's an extended toe.

• 55:20Lex Fridman?

  Fascinating.

• 55:21Speaker 01

  But, you know, the basic morphology is in, is the same in, in, uh, all these animals.

• 55:27Lex Fridman?

  What do you think is, uh, the most beautiful movement of an animal? Like what animal you think is the coolest? Land animal. That's cool. Cause fish is pretty cool. Like the fish moves to water, but like legged locomotion.

• 55:40Speaker 01

  You know, the slow mo's of cheetahs running are, are incredible. You know, they're, they, there's so much back motion and, uh, you know, grace and of course they're moving very fast.

• 55:52Speaker 01

  The animals running away from the cheetah are pretty exciting. You know, the pronghorn, uh, which, you know, they, they do this, all four legs at once jump, called the prong to kind of confuse the, especially if there's a group of them to confuse whoever's chasing them.

• 56:07Lex Fridman?

  So they do like a misdirection type of thing?

• 56:09Speaker 01

  Yep. They do a misdirection thing. The front on views of the cheetahs running fast where the tail is whipping around to help in the turns, to help us stabilize in the turns. That's, that's pretty exciting.

• 56:19Lex Fridman?

  Cause they spend a lot of time in the air, I guess, as they're running that fast.

• 56:23Speaker 01

  But they also turn very fast.

• 56:25Lex Fridman?

  Is that a tail thing or is that, do you have to have contact with ground?

• 56:28Speaker 01

  Everything in the body is probably helping turn, cause they're chasing something that's trying to get away. That's also zigzagging around. But I would be remiss if I didn't say, you know, humans are pretty, pretty good too. You know, you watch gymnasts, especially these days, they're doing just incredible stuff.

• 56:47Lex Fridman?

  Well, like especially like Olympic level gymnasts. See, but there could be, I think there could be cheetahs that are Olympic level. We might be watching the average cheetah versus like, that could be like a really special cheetah that can do like.

• 56:59Speaker 01

  You're right.

• 57:00Lex Fridman?

  When did the knees first come into play in, in you building legged robots?

• 57:05Speaker 01

  Uh, in Big Dog.

• 57:06Lex Fridman?

  Big Dog?

• 57:07Speaker 01

  Yeah, Big Dog came first and then Little Dog was later. And you know, there was a, there's a big compromise there. Human knees have multiple muscles and you could argue that there's, uh, I mean, it's a technical thing about negative work. When you're, when you're contracting a joint, but you're pushing out, that's negative work. And if you don't have a place to store that, it can be very expensive to do negative work. And the most of, and in Big Dog, there was no place to store negative work in the knees. But Big Dog also had pogo stick springs down below. So part of the action was to comply in a bouncing motion.

• 57:52Speaker 01

  You know, later on in SPOT, we, we took that out. As we got further and further away from the leg lab, uh, we had more, uh, you know, energy driven controls.

• 58:03Lex Fridman?

  Is there something to be said about like knees that go forward versus backward?

• 58:09Speaker 01

  Sure. There's this idea called passive dynamics, which says that although, although you can use computers and actuators to make a motion, a mechanical system can make a motion just by itself if it gets stimulated the right way.

• 58:26Speaker 01

  So Tad McGee in the, uh, I think in the mid eighties. Uh, maybe it was in the late eighties, starting to, started to work on that. And he made this legged system that could walk down an inclined plane where the legs folded and unfolded and swung forward, you know, do the whole walk, walking motion where the only, there was no computer. There were some adjustments to the mechanics so that there were dampers and springs in some places that helped the, uh, the mechanical action happen. It was essentially a mechanical computer. And the idea, the interesting idea there is that it's not all about the brain, uh, telling, dictating to the body what the body should do. The body is a participant in the motion.

• 59:10Lex Fridman?

  So a, a great design for a robot has a mechanical component where the movement is efficient even without a brain?

• 59:17Speaker 01

  Yes.

• 59:18Lex Fridman?

  How do you design that?

• 59:20Speaker 01

  I think that these days, most robots aren't doing that. Most robots are, are basically using the computer to, to govern the motion. Now, the, the brain though, is taking into account what the mechanical thing can do, uh, and how it's going to behave. Otherwise it would have to really forcefully move everything around all the time, which probably some solutions do, but I think you end up with a more efficient and more graceful thing if you're taking into account what the, what the machine wants to do.

• 59:51Lex Fridman?

  So this might be a good place to mention that you're now, uh, leading up the, the Boston Dynamics AI Institute, newly formed, which is focused more on designing the robots of the future. I think one of the things, maybe you can tell me the big vision for what's going on, but, uh, one of the things is, uh, this idea that hardware still matters with, with organic design and so on. Maybe before that, can you zoom out and tell me what the vision is for the AI Institute?

• 1:00:25Speaker 01

  You know, I like to talk about intelligence having two parts, an athletic part and a cognitive part. And I think, you know, Boston Dynamics, in my view, has sort of set the standard for what athletic intelligence can be. And, you know, it has to do with all the things we've been talking about, the mechanical design, the real time control, the energetics and that kind of stuff. But obviously, people have another kind of intelligence and animals have another kind of intelligence. You know, we can make a plan. Our meeting started at nine thirty. I looked up on Google Maps how long it took to walk over here. It was, you know, twenty minutes. So I decided, okay, I'd leave my house at nine, which is what

• 1:01:09Speaker 01

  I did. You know, simple intelligence, but we use that kind of stuff all the time. It's sort of what we think of as going on in our heads. And I think that's in short supply for robots. Most robots are pretty dumb. And as a result, it takes a lot of skilled people to program them to do everything they do. And it takes a long time. And if robots are gonna, you know, satisfy our dreams, they need to be smarter. So the AI Institute is designed to combine that physicality of the athletic side with the cognitive side.

• 1:01:50Speaker 01

  So for instance, we're trying to make robots that can watch a human do a task, understand what it's seeing, and then do the task itself. So sort of OJT for on the job training for robots as a paradigm. Now, you know, that's pretty hard. And it's it's sort of science fiction. But our idea is to work on a longer timeframe and work on solving those kinds of problems. And I have a whole list of things that are kind of like in that in that vein.

• 1:02:21Lex Fridman?

  Maybe we can just take many of the things you mentioned, just take it as a tangent.

• 1:02:27Speaker 01

  Okay.

• 1:02:27Lex Fridman?

  First of all, athletic intelligence is a super cool term. And that's, that really is intelligence. We humans kind of take it for granted that we're so good at walking and moving about the world.

• 1:02:38Speaker 01

  And using our hands, you know, the mechanics of interacting with all, you know, these parts, these two things, you know.

• 1:02:44Lex Fridman?

  And you've never touched those things before, right?

• 1:02:46Speaker 01

  Well, I've touched ones like this. Look at all the things I can do, right? I can juggle and I'm rotating it this way. I can rotate it without looking. I could fetch these things out of my pocket and figure out which one was which and all that kind of stuff.

• 1:02:58Lex Fridman?

  Yeah.

• 1:02:58Speaker 01

  And I don't think we have much of a clue how all that works yet.

• 1:03:03Lex Fridman?

  Right. And that's, I really like putting that under the banner of athletic intelligence. What are the big open problems in athletic intelligence? So Boston Dynamics with Spot, with Atlas, just have shown time and time again, like pushed the limits of what we think is possible with robots. But where do we stand actually? If we kind of zoom out, what are the big open problems on the athletic intelligence side?

• 1:03:29Speaker 01

  I mean, one question you could ask, it isn't my question, but you know, are they commercially viable? Could, will they increase productivity?

• 1:03:38Lex Fridman?

  Yeah.

• 1:03:38Speaker 01

  And I think, you know, we're getting very close to that. I don't think we're quite there still, you know, most of the robotics companies, it's, it's a, it's a struggle. You know, it's really the lack of the cognitive side that probably is the biggest barrier at the moment, even for the physically successful robots. But you know, your question's good. I mean, you can always do a thing that's more efficient, lighter, more reliable. I'd say reliability, you know, I know that Spot, they've been working very hard on getting the, the tail of the reliability curve up and they've made huge progress. So the robots, you know, there's 1,500 of them out there now, many of them being used

• 1:04:20Speaker 01

  in practical applications, day in and day out, where, you know, where they have to work reliably. And you know, it's very exciting that they've done that, but it takes a huge effort to get that kind of reliability in the robot. There's cost too, you know, you'd like to get the cost down. Spots are still pretty expensive and I don't think that they have to be, but it takes, you know, a different kind of activity to do that. Now that, you know, I think, you know, that Boston Dynamics is owned primarily by Hyundai now. And I think that the skills of Hyundai in making cars can be brought to bear in

• 1:05:03Speaker 01

  making robots that are less expensive and more reliable and those kinds of things.

• 1:05:08Lex Fridman?

  So on the cognitive side for the I Institute, what's the trade off between moonshot projects for you and maybe incremental progress?

• 1:05:18Speaker 01

  That's a good question. I think we're, we're using the paradigm called stepping stones to moonshots. I don't, I don't believe, that was in my original proposal for the Institute, stepping stones to moonshots. I think if you go more than a year without seeing a tangible status report of where you are, which is the stepping stone, and it could be a simplification, right? You don't necessarily have to solve all the problems of your target goal, even though your target goal is going to take several years. You know, those stepping stone results give you feedback, give motivation, because usually there's some success in there. And so, you know, that's the mantra we've been working on. And that's pretty much how,

• 1:06:03Speaker 01

  you know, I'd say Boston Dynamics has worked, you know, where you make progress and show it as you go. Show it to yourself, if not to the world.

• 1:06:14Lex Fridman?

  What does success look like? What are some of the milestones you're chasing?

• 1:06:20Speaker 01

  Well, we've, with Watch, Understand, Do, the project I mentioned before, you know, we've broken that down into getting some progress with what does meaningfully watching something mean? Breaking down an observation of a person doing something into the components. You know, segmenting, you know, you watch me do something. I'm going to pick up this thing and put it down here and stack this on it. Well, it's not obvious if you just look at the raw data, what the sequence of acts are. It's really a creative intelligent act. For you to to break that down into the pieces and understand them in a way. So you could say, OK, what skill do I need to accomplish each of those things?

• 1:07:02Speaker 01

  So we're working on, you know, the front end of of that kind of a problem where we observe and translate the, if it may be video, it may be live, into a description of what we think is going on. And then trying to map that into skills to accomplish that. And we've been developing skills as well. So, you know, we have kind of multiple stabs at the pieces of of doing that.

• 1:07:23Lex Fridman?

  And this is usually video of humans manipulating objects with their hands kind of thing?

• 1:07:29Speaker 01

  Mm hmm. We're starting out with bicycle repair, some simple bicycle repair.

• 1:07:32Lex Fridman?

  Oh, no. That seems complicated. That seems really complicated.

• 1:07:35Speaker 01

  It is, but but there's some parts of it that aren't, like putting the seat in, you know, into the, you know, you have a tube that goes inside of another tube and there's a latch, you know, that's that should be within range.

• 1:07:48Lex Fridman?

  Is it possible to to observe, to watch a video like this without having an explicit model of what a bicycle looks like?

• 1:07:54Speaker 01

  I think it is. And I think that's the kind of thing that people don't recognize. Now, let me translate it to navigation. You know, I think the basic paradigm for navigating a space is to get some kind of sensor that tells you where an obstacle is and what's open, build a map, and then go through the space. But if I if we were doing on the job training where I was giving you a task, I wouldn't have to say anything about the room, right? We came in here, all we did is adjust the chair, but we didn't say anything about the room and, you know, we could navigate it. So I think there's opportunities to build that kind of navigation skill into robots. And we're, you know, we're hoping to be able to do that.

• 1:08:35Lex Fridman?

  So operate successfully under a lot of uncertainty, like?

• 1:08:38Speaker 01

  Yeah, and and lack of specification.

• 1:08:41Lex Fridman?

  Lack of specification.

• 1:08:43Speaker 01

  I mean, that's what sort of intelligence is, right? Kind of dealing with a situation, understanding a situation, even though it wasn't explained.

• 1:08:50Lex Fridman?

  So how how big of a role does machine learning play in all of this? Is this more and more learning based?

• 1:08:59Speaker 01

  You know, since ChatGPT, which is a year ago, basically, there's a huge interest in that and a huge optimism about it. And I think that there's a lot of things that machine learn, that kind of machine learning. Now, of course, there's lots of different kinds of machine learning. I think there's a, you know, a lot of interest and optimism about it. I think, you know, the facts on the ground are that doing physical things with physical robots is a little bit different than language. And the tokens, you know, the tokens sort of don't exist, you know, pixel, pixel values aren't like words.

• 1:09:37Speaker 01

  But I think that there's a lot that can be done there. We have, uh, we have several people working on machine learning approaches. I don't know if you know, but we, we opened an office in Zurich recently. And Marco Hutter, who's one of the real leaders in reinforcement learning for robots, is the, the director of that office. He's still half time at ETH, the university there where he has an unbelievably fantastic lab. And then he's half time leading, will be leading off efforts in the Zurich office. So we have a healthy learning component, but there's part of me that still says, if

• 1:10:18Speaker 01

  you look out in the world at what the most impressive performances are, they're still pretty much, I hate to use the word traditional, but that's what everybody's calling it, traditional controls, like model predictive control. You know, the thing, the, the Atlas performances that you've seen are mostly model predictive control. They've started to do some learning stuff that's really incredible. I don't know if it's all been shown yet, but you'll see it over, over time.

• 1:10:47Speaker 01

  And then Marco has done some great stuff and, and others.

• 1:10:49Lex Fridman?

  So especially for the athletic intelligent piece. The traditional approach seems to be the one that still performs the best.

• 1:10:57Speaker 01

  I think we're gonna find a mating of the two and we'll have the best of both worlds. And we're working on that at the institute too.

• 1:11:04Lex Fridman?

  If I can talk to you about teams, you've built an incredible team of Boston Dynamics before at MIT and CMU and Boston Dynamics and now at the AI Institute. And you said that there's four components to a great team. Technical fearlessness, diligence, intrepidness and fun, technical fun. Can you explain each? Technical fearlessness, what do you mean by that?

• 1:11:26Speaker 01

  Sure. Technical fearlessness means being willing to take on a problem that you don't know how to solve. Uh, and, you know, uh, study it, uh, figure out an act, an entry point, you know, maybe a simplified version or a simplified solution or something. Learn from the stepping stone and, uh, and go back and, uh, eventually make a solution that meets your goals. And I think that's really important.

• 1:11:56Lex Fridman?

  The fearlessness comes into play because some of it has never been done before?

• 1:12:00Speaker 01

  Yeah, and you don't know how to do it. And, you know, there's easier stuff to do in life.

• 1:12:07Speaker 01

  So, you know, I mean, I don't know, watch, understand, do. It's a, it's a mountain of a, of a challenge.

• 1:12:14Lex Fridman?

  So that's the really big challenge you're, you're tackling now. Can we watch humans at scale and have robots by watching humans become effective actors in the world?

• 1:12:25Speaker 01

  Yeah. I mean, we have others like that. I, we have one called inspect, diagnose, fix. Like, uh, you know, you, uh, call up the Maytag repairman. Okay, he's the one who you don't have to call, but you, you know, you call up the, the dishwasher repair person and they come to your house and they look at your machine. It's already been actually figured out that something doesn't work, but they have to kind of examine it and figure out what, what's wrong and then fix it. And, uh, I think robots should be able to do that. We already, Boston Dynamics already has spot robots. Collecting data on machines, things like thermal data, reading the gauges,

• 1:13:06Speaker 01

  listening to them, getting sounds, and that data are used to determine whether they're healthy or not. But the interpretation isn't done by the robots yet, and the certainly the fixing, the diagnosing and the fixing isn't done yet, but I think it could be. And that's bringing the AI and combining it with the physical skills to do it.

• 1:13:29Lex Fridman?

  And you're referring to the fixing in the physical world. I can't wait until you can fix the psychological problems of humans and show up and talk, do therapy.

• 1:13:37Speaker 01

  Yeah, that's a different thing.

• 1:13:38Lex Fridman?

  Yeah, it's a different, well, that's all part of the same thing. Again, humanity, maybe, maybe.

• 1:13:46Speaker 01

  You mean convincing you it's okay that the dishwasher is broken? Just do the. The marketing approach?

• 1:13:52Lex Fridman?

  Yeah, exactly. It's all, yeah, don't sweat the small stuff.

• 1:13:58Lex Fridman?

  Yeah, as opposed to fixing the dishwasher, it'll convince you that it's okay that the dishwasher is broken. It's a different approach.

• 1:14:05Lex Fridman?

  Diligence, why is diligence important?

• 1:14:08Speaker 01

  Well, if you want a real robot solution, it can't be a very narrow solution that's going to break at the first variation in what the robot does or the environment, if it wasn't exactly as you expected it. So how do you get there? I think having an approach that leaves you unsatisfied until you've embraced the bigger problem is the diligence I'm talking about. And again, I'll point at Boss Dynamics. I think they've done, you know, some of the videos that we had showing the engineer making it hard for the robot to do its task.

• 1:14:47Speaker 01

  Spot opening a door, and then the guy gets there and pushes on the door so it doesn't open the way it's supposed to, pulling on the rope that's attached to the robot. So its navigation has been screwed up. We have one where the robot's climbing stairs and an engineer is tugging on a rope that's pulling it back down the stairs. You know, that's totally different than just the robot seeing the stairs, making a model, putting its feet carefully on each step. But that's what probably robotics needs to succeed and having that broader. That broader idea that you want to come up with a robust solution is what I meant by diligence.

• 1:15:23Lex Fridman?

  So really testing it in all conditions, perturbating the system in all kinds of ways.

• 1:15:27Speaker 01

  Right.

• 1:15:27Lex Fridman?

  And as a result, creating some epic videos. The legendary.

• 1:15:31Speaker 01

  The fun part, the hockey stick.

• 1:15:33Lex Fridman?

  And then, yes, tugging on spot as it's trying to open the door. I mean, it's, it's great testing, but it's also.

• 1:15:42Lex Fridman?

  I don't know, it's just somehow extremely compelling demonstration of robotics in video form.

• 1:15:49Speaker 01

  I learned something very early on with the first three dimensional hopping machine. If you just show a video of it hopping. It's a so what? If you show it falling over a couple of times and you can see how easily and fast it falls over. Then you appreciate what the robot's doing when it's, when it's doing its thing. So I think, you know, you're the reaction you just gave to the door, the robot getting kind of interfered with or tested while it's going through the door. It's showing you the scope of the solution.

• 1:16:21Lex Fridman?

  The, the limits of the system, the, the challenges involved and failure. If it's shown both failure and success makes you appreciate the, the success. Yeah. And then just the way the videos are done in both dynamics are incredible because they're not, there's no flash. There's no extra like production. It's just raw testing of the robot.

• 1:16:41Speaker 01

  Well, you know, I was the final edit for most of the videos up until, until about three years ago or four years ago. And, uh, you know, my theory of the video is no, no explanation. If they can't see it, then it's not the right thing. And if you do something worth showing, then let them see it. Don't, don't interfere with. Uh, you know, a bunch of titles that slow you down or a bunch of distraction. Just, you know, do something worth showing and then show it.

• 1:17:16Lex Fridman?

  That's brilliant.

• 1:17:17Speaker 01

  It's hard. It's hard, though, for, for people to buy into that.

• 1:17:21Lex Fridman?

  Yeah, I mean, people always want to add more stuff, but the simplicity of just do something worth showing and show it. That's brilliant and don't add extra stuff.

• 1:17:30Speaker 01

  And people, people have criticized, uh, especially the, the big dog videos where there's a human, uh, driving the robot. And, and I understand the criticism now. At the time, we wanted to just show, look, this thing's using its legs to get up the hill. So we focused on showing that, which was, we thought the, the story. The fact that there's a human, so they were thinking about autonomy, whereas we were thinking about the, the mobility. Uh, and so, you know, we've, we've adjusted to a lot of things that we see that people care about. Trying to be honest. We've always tried to be honest.

• 1:18:06Lex Fridman?

  But also just show cool stuff in its raw form, the limits of the system. The see the system be perturbed and be robust and resilient and all that kind of stuff. And, and dancing with some music.

• 1:18:23Lex Fridman?

  Intrepidness and fun. So intrepid.

• 1:18:26Speaker 01

  I mean, it might be the most important ingredient and that is, you know, robotics is hard. It's not going to work right, right away. So don't be discouraged is all it really means. So usually when I talk about these things, I show videos and I show a long string of outtakes, you know, and you know, you have to have courage to be, to be intrepid when, you know, you work so hard, you built your machine. You know, and then you're trying and it just doesn't do what you thought it would do, what you want it to do. And, uh, you know, you have to. Stick to it and keep trying.

• 1:19:03Lex Fridman?

  How long, I mean, we don't often see that, the story behind Spot and Atlas. How long, how many failures was there along the way to get, you know, a working Atlas, a working Spot? In their early days, even working big dog?

• 1:19:17Speaker 01

  There's a video of Atlas climbing three big steps and it's very dynamic and it's, you know, it's really exciting, real accomplishment. It took a hundred and nine tries and we have video of every one of them. You know, we shoot everything. Again, we, this is at Boston Dynamics.

• 1:19:36Speaker 01

  So it took a hundred and nine tries, but once it did it, it had a high percentage of success. So it's not like we're cheating by just showing the best one, but we do show the evolved, you know, performance, not everything along the way. But the everything along the way is informative and, you know, it shows sort of, uh, there's, you know, stupid things that go wrong, like, like the robot, just when you say go and it collapses right there on the start, that doesn't have to do with the steps.

• 1:20:06Speaker 01

  Or the perception didn't work right. So you missed the target when you jump or something breaks and there's oil flying everywhere.

• 1:20:14Speaker 01

  But that's fun.

• 1:20:15Lex Fridman?

  Yeah. So the hardware failures and then maybe some software.

• 1:20:18Speaker 01

  Lots of control of evolution during that time. I think it took six weeks to get that, those a hundred and nine trials, you know, cause there was, there was a programming going on, you know? It was, it was actually robot learning, but there were human in the loop helping with the learning. So all data driven, but.

• 1:20:37Lex Fridman?

  Okay. And so, and you always are learning from that failure. So.

• 1:20:41Speaker 01

  Right.

• 1:20:43Lex Fridman?

  And how do you, how do you protect Atlas from not getting damaged from a hundred and nine, uh, attempts?

• 1:20:52Speaker 01

  It was, it's remarkable. One of the accomplishments of Atlas is that the engineers have made a machine that's robust enough that it can take that kind of testing where it's falling and stuff and it doesn't break every time. It still breaks and we had, you know, part of the paradigm is to have people to repair stuff. You gotta figure that in if you're gonna do this kind of work. You know, I sometimes criticize the people who have their gold plated thing and they keep it on the shelf and everybody and they're afraid to kind of use it. I don't think you can make progress if you're working that way. You know, you need to be ready to have it break and, and go in there and fix it. It's part of the thing. You know, plan your budget so you have spare parts and a crew and all that stuff.

• 1:21:36Lex Fridman?

  Yeah, if it falls 109 times, it's okay. Wow. So intrepid, truly. And that applies to Spot, that applies to all the other.

• 1:21:45Speaker 01

  Applies to everything. I think it applies to everything anybody tries to do that's worth doing.

• 1:21:49Lex Fridman?

  Yeah. And especially with systems in the real world, right?

• 1:21:53Speaker 01

  Yeah.

• 1:21:53Lex Fridman?

  And so fun.

• 1:21:55Speaker 01

  Fun. Technical fun, I usually say. Have technical fun. I think that life as an engineer is really satisfying. I think you get to, you know, to some degree, it can be like crafts work where you get to do things with your own hands or your own design or whatever your, you know, your media is. And it's very satisfying to be able to just do the work. Unlike, you know, a lot of people who have to do something that they don't like doing. I think engineers typically get to do something that they, that they like and there's a lot of satisfaction from that. Then there's, you know, in many cases, you can have impact on the world somehow because you've done something that other

• 1:22:36Speaker 01

  people admire, which is different from the own, just the craft fun of, of building a thing. So that's a second. Way that that being engineer is good. I think the third thing is that if you're lucky to be working in a team where you're getting the benefit of other people's skills that are helping you do your thing. You know, none of us has all the skills needed to do most of these projects. And if you have a team where you're working well with the others, that can be very satisfying. And then if you're an engineer, you also usually get paid. And so you kind of get paid four times.

• 1:23:13Lex Fridman?

  Yeah.

• 1:23:14Speaker 01

  In my view of the world.

• 1:23:16Lex Fridman?

  Yeah.

• 1:23:16Speaker 01

  So what could be better than that?

• 1:23:17Lex Fridman?

  Get paid to have fun. I mean, what do you love about engineering? What, when you say engineering, what does that, what does that mean to you exactly? What is this kind of big thing that we call engineering?

• 1:23:28Speaker 01

  I think it's both being a scientist or getting to use science at the same time as being kind of an artist or a creator because you're making some, you know, scientists only get to describe, to study what's out there. And engineers get to make stuff that didn't exist before. And so it's really, I think, a higher calling, even though I think most, you know, the public out there thinks science is top and engineering is somehow secondary. But I think it's the other way around.

• 1:23:55Lex Fridman?

  And at the cutting edge, I think when you, when you talk about robotics, there is a possibility to do art in that you do like the first of its kind thing. So then there's the production at scale, which is a so beautiful thing. But when you do the first new robot or the first new thing, that's a possibility to create something totally new.

• 1:24:15Speaker 01

  I mean, bringing metal to life or a machine to life is kind of, is fun. And, you know, it was fun doing the dancing videos where got a huge, you know, public response and we're going to do more. We're going to do some, doing some at the institute and we'll, we'll do more.

• 1:24:33Lex Fridman?

  Well, that metal to life moment, I mean, to me, that's still magical. Like when an animate object comes to life, that's still like to me, to this day is still an incredible moment. The human intelligence can create systems that instill life or whatever that is into inanimate objects. It's really, it's truly magical, especially when it's at the scale that humans can perceive and appreciate like directly.

• 1:25:05Speaker 01

  But I think sort of with it going, going back to the pieces of that, you know, you design a linkage that turns out to be half the weight and just as strong. That's, that's very satisfying. And you know, there are people who do that and it's, it's a creative, a creative act.

• 1:25:21Lex Fridman?

  Uh, what, what do you use the most beautiful about robotics? Sorry for the big romantic question.

• 1:25:29Speaker 01

  I think having the robots move in a way that's uh,

• 1:25:34Speaker 01

  evocative of life is, is pretty exciting.

• 1:25:36Lex Fridman?

  So the elegance of movement?

• 1:25:37Speaker 01

  Yeah. Or, or if it's a high performance act where it's doing it, you know, faster, bigger than, than other robots. Usually we're not doing it bigger, faster than people, but we, you know, we're getting there in a few narrow dimensions.

• 1:25:50Lex Fridman?

  So faster, bigger, smoother, more elegant, more graceful.

• 1:25:56Speaker 01

  I mean, I'd like to do dancing that, that starts, you know, we're nowhere near the, uh, the dancing capabilities of a human. We, we've been having a ballerina in who's a kind of a well-known ballerina and she's been programming, uh, the robot. We've been working on the tools that can make it so that she can use her way of talking, you know, way of doing a choreography or something like that, more accessible.

• 1:26:21Speaker 01

  To, uh, to get the robot to do things and starting to produce some interesting stuff.

• 1:26:26Lex Fridman?

  Well, we should mention that there is a choreography tool.

• 1:26:29Speaker 01

  There is.

• 1:26:30Lex Fridman?

  I, I mean, I, I guess I saw versions of it. Which is pretty cool. You can kind of at, at slices of time, control different parts at the high level, the movement of the robot.

• 1:26:43Speaker 01

  We hope to take that forward and make it, you know, more tuned to how, uh, the dance world wants to talk, wants to communicate and, and get better performances. I mean, we've done, done a lot, but there's still a lot possible. And I'd like to have performances where the robots are dancing with people. So right now, almost everything that we've done on dancing, uh, is to a fixed time base. So once you press go, the robot does its thing and plays out its thing. It's not listening, it's not watching, but I think it should do those things.

• 1:27:17Lex Fridman?

  I think I would love to see a professional ballerina. Like alone in a room with a robot, slowly teaching the robot. Just actually the, the process of a clueless robot trying to figure out a, a small little piece of a dance. So it's not like, uh, cause right now Atlas and Spot have done like perfect dancing.

• 1:27:37Speaker 01

  Right.

• 1:27:37Lex Fridman?

  Uh, to a beat and so on, one, so, you know, uh, to a degree, but like, uh, the learning process of interacting with a human would be like incredible to watch.

• 1:27:47Speaker 01

  One of the cool things going on, you know, that there's a class at Brown University called Choreo Robotics. Sidney Skybetter is a dancer, uh, choreographer and he, he teamed up with, uh, Stephanie Telax, who's a computer science professor, and they taught this class, and I think they have some, uh, graduate students helping teach it. Where they have two spots and people come in, most, uh, I think it's 50 50 of, uh, computer science people and dance people. And, uh, they do, they program performances that are, that are very interesting. I show some of them sometimes when I give a talk.

• 1:28:21Lex Fridman?

  And making that process of a human teaching the robot more efficient, more intuitive, maybe partial language, part movement. That'd be fascinating. That'd be really fascinating because, uh, I mean, one of the things I've kind of realized is, um, humans communicate with movement a lot. It's not just language. There's a lot of this body language. There's so many intricate little things and.

• 1:28:44Speaker 01

  Totally.

• 1:28:46Lex Fridman?

  And like that, you know, to, to watch a human and Spot communicate back and forth with movement is, uh, I mean, there's just so many wonderful possibilities there.

• 1:28:57Speaker 01

  But it's also a challenge, you know, we, we get asked to have our robots perform with, uh, with famous dancers. And they can, you know, they have 200 degrees of freedom or something, right? Every little ripple and thing, and they have all this head and neck and shoulders and stuff. And the robots mostly don't have all that stuff and it's, it's, uh, it's a daunting challenge to not look stupid. You know, physically stupid next to them. So we've, we've pretty much avoided that kind of performance, but we'll, we'll get to it.

• 1:29:32Lex Fridman?

  I think even with the limited degrees of freedom, we could still have some sass and flavor and so on. You can figure out your own thing, even if you can't.

• 1:29:40Speaker 01

  And we can reverse things. Like if you watch a human do robot animation, which is a dance style where, you know, you jerk around sort of and you pop and pop and lock and all that stuff. I think the robots could show up the humans by, you know, doing unstable oscillations and things that are faster than a person could. So that's sort of on my, you know, my plan, but we haven't quite gotten there yet.

• 1:30:07Lex Fridman?

  You, you mentioned about building teams and robotics teams and so on. How do you, how do you find great engineers? How do you hire great engineers?

• 1:30:14Speaker 01

  I think you even need to have an environment where interesting engineer, well, you know, it's a chicken and egg. If you have an environment where interesting engineering is going on, then engineers want to work there. And, uh, you know, I think it took a long time to develop that at Boston Dynamics. In fact, when we started, although, you know, I had the experience of building things in the, in the leg lab, both at CMU and at MIT, we weren't that sophisticated of an engineering thing compared to what Boston Dynamics is now. But it was our ambition to do that. And, you know, Sarkos was another robot company. So I always thought of us as

• 1:30:53Speaker 01

  being this much on the computing side and this much on the hardware side. And they were like this. And then over the years, we, you know, I think we achieved the same or better levels of engineering. Meanwhile, you know, Sarkos got acquired and then they went through all kinds of changes. And I don't know exactly what their current status is, but, uh, so it took, it took many years is, is part of the answer. Uh, I think you get, you got to find people who love it. In the early days, we would, we paid a little less. So we only got people who were doing it because, because they really loved it. We also hired people who might not have professional degrees, you know, people who were building

• 1:31:36Speaker 01

  bicycles and building kayaks. We have some people who come from that kind of the maker world. And that's really important.

• 1:31:45Speaker 01

  Uh, for the kind of work we do to have that be part of the mix.

• 1:31:48Lex Fridman?

  Whatever that is, the whatever the magic ingredient that makes a great builder maker. Yeah, that that's the big part of it.

• 1:31:55Speaker 01

  People who took who repaired the car, the cars or built or motorcycles or whatever in their garages when they were kids.

• 1:32:02Lex Fridman?

  There's a kind of like the robotics students, grad students and just roboticists that I know and hang out with. There's a kind of endless energy and like there's just, they're just happy. Like say, uh, I compare it and other group of people that are like that are people that skydive professionally. There's just like a excitement and general energy that I think probably has to do with the fact that they're just constantly. First of all, fail a lot and then the the joy of building a thing that you eventually works.

• 1:32:35Speaker 01

  Talking about being happy, there used to be a time when when I was doing the machine shop work myself back in those JPL and Caltech days. When if I came home smelling like the machine shop, you know, because it's an oily place. My wife would say, oh, you you had a good day today, because you could tell that that's where I'd been.

• 1:32:52Lex Fridman?

  You've done, yeah, you've actually built something, you've done something in the physical world. Yeah, and probably the videos help, right? The videos help show off what robotics is.

• 1:33:03Speaker 01

  Oh, you know, at Boston Dynamics, it put us on the map. We, I remember interviewing some sales guy and he was from a company. And he said, well, no one's ever heard of my company, but we have products. You know, really good products. You guys, everybody knows who you are, but you don't have any, any products at all, which was true. So it was, and you know, we thank YouTube for that. YouTube came, we caught the YouTube wave and it had a huge impact on on our company.

• 1:33:35Lex Fridman?

  I mean, this, that's, it's, it's a big impact on not just on your company, but on robotics in general and helping people understand, inspire what is possible.

• 1:33:45Speaker 01

  Yeah.

• 1:33:45Lex Fridman?

  With robots.

• 1:33:46Speaker 01

  Yeah.

• 1:33:46Lex Fridman?

  They inspire imagination, fear and everything. The full spectrum of human emotion was aroused, which is, which is great, which is, is great for the entirety of humanity. And also is probably inspiring for young people that want to get into AI and robotics.

• 1:34:00Speaker 01

  Yeah.

• 1:34:01Lex Fridman?

  Uh, let me ask you about some competitors.

• 1:34:03Speaker 01

  Sure.

• 1:34:04Lex Fridman?

  You've been a complimentary of Elon and Tesla's work on Optimus Robot. With this, their humanoid robot, what do you think of their efforts there with the humanoid robot?

• 1:34:15Speaker 01

  You know, I really admire Elon as a technologist. I think that what he did with Tesla is just totally mind boggling that he could go from this totally niche area that, you know, less than 1% of anybody seemed to be interested to making it. So that essentially every car company in the world is trying to do what, what he's done. So you got to give it to him. Then look at SpaceX, you know, he's basically replaced NASA, if you could, that might be a little exaggeration, but not by much. So, you know, you got to admire the guy and, you know, I wouldn't count him out for, for anything. You know, I don't think

• 1:35:00Speaker 01

  Optimus today is, is where Atlas is, for instance. I don't know, it's a little hard to compare them to the other companies. You know, I, I visited Figure, I think they're doing well and they have a good team. I've visited Epcronic and I think they're, they have a good team and they're doing well. But Elon has a lot of resources. He has a lot of ambition. I like to take some credit for his ambition. I think, I think if I read between the lines, it's hard not to think that him seeing what Atlas is doing is a little bit of an inspiration. I, I hope so.

• 1:35:41Lex Fridman?

  Do you think Atlas and Optimus will, will hang out at some point?

• 1:35:44Speaker 01

  I would love to host that, you know, now that I'm not at Boston Dynamics, you know, I'm not officially connected. I am on the board, but I'm not officially connected. I would love to host a

• 1:35:55Lex Fridman?

  Robot meetups?

• 1:35:56Speaker 01

  A robot meetup, yeah.

• 1:35:58Lex Fridman?

  Does the AI Institute work with Spots and Atlas is, is it focused on Spots mostly right now as a platform?

• 1:36:06Speaker 01

  We have a bunch of different robots. We bought everything we could buy. So we have Spots. I think we have a good sized fleet of them. I don't know how many it is, but a good sized fleet. We have a couple of animal robots. You know, animal is a company founded by Marco Hutter, even though he's not that involved anymore, but we have a couple of those. We have a bunch of arms like, you know, Frank is and US Robotics. Cause you know, even though we have ambitions to build stuff and we are building, starting to build stuff.

• 1:36:37Speaker 01

  You know, day one getting off the ground, we just, you know, just bought stuff.

• 1:36:42Lex Fridman?

  I love this like robot playground you've built.

• 1:36:45Speaker 01

  You can come over and take a look if you want.

• 1:36:47Lex Fridman?

  That's great. So it's like all these kinds of robots, legged, arms.

• 1:36:51Speaker 01

  It doesn't feel that much like, well, there's some areas that feel like a playground, but it's not like they're all frolick together.

• 1:36:59Lex Fridman?

  Hey, again, maybe you'll arrange it, a robot meetup.

• 1:37:05Lex Fridman?

  But in general, what's your view on competition in this space for especially like humanoid and legged robots? Are you, are you excited by the competition or the friendly competition?

• 1:37:16Speaker 01

  I think that it doesn't, you know, I don't think, I don't think about competition that much. You know, I'm not a commercial guy. I think for many years at Boston, you know, the many years I was at Boston Dynamics, we didn't, we didn't think about competition. We were just kind of doing our thing. There wasn't, it wasn't like there were products out there that we were competing with. You know, maybe there was some competition for DARPA funding, which we got, you know, got a lot of, got very good at, at getting. But even there, in, in a couple of cases where we might've competed, we ended up just being the robot provider. That is for the little dog program, you know, we, we just made the robots. We didn't participate as developers,

• 1:37:59Speaker 01

  except for developing the robot. And in the DARPA robotics challenge, we didn't compete, we provided the robot. So,

• 1:38:09Speaker 01

  you know, in the AI world now, now that we're working on cognitive stuff, it feels much more like a, a competition. You know, the, the entry requirements in terms of computing hardware and, and the skills of the team are, and hiring talent, it's, it's a much tougher place. So I think much more about competition. Now, on the cognitive side, on the physical side, it doesn't feel like it's that much about competition yet. Obviously with 10 humanoid companies out there, 10 or 12, I mean, there's probably others that I don't know about.

• 1:38:46Speaker 01

  They're definitely in competition, will be in competition.

• 1:38:50Lex Fridman?

  How much room is there for a quadruped and especially a humanoid robot to become cheaper? So like cutting costs and like how low can you go?

• 1:39:05Lex Fridman?

  And how much of it is just mass production? So questions of, you know, Hyundai, like how to produce versus like engineering innovation? How to simplify?

• 1:39:15Speaker 01

  Um, I think there's a huge way to go. I don't think we've seen the bottom of it or the bottom in terms of lower prices.

• 1:39:24Speaker 01

  You know, I think you should be totally optimistic that at asymptote, things don't have to be anything like as expensive as they are now. Back to competition, I wanted to say one thing. I think in the quadruped space, having other people selling quadrupeds is a great thing for Boston Dynamics. Because the question, I believe the question in the users' minds is which quadruped do I want? It's not, oh, can a quadruped, do I want a quadruped? Can a quadruped do my job? It's much more like that, which is a great place for it to be.

• 1:39:53Lex Fridman?

  Yeah.

• 1:39:54Speaker 01

  Then, then you're just, you know, doing, doing the things you normally do to make your product better and compete and selling and all that stuff. And that'll be the way it is with humanoids at some point.

• 1:40:05Lex Fridman?

  Well, there's a lot of humanoids and you're just not even, it's like a iPhone versus Android and people are just buying both. And it's kind of just.

• 1:40:15Speaker 01

  Yeah.

• 1:40:16Lex Fridman?

  You're not really.

• 1:40:17Speaker 01

  You're creating the category or the category is happening. I mean, right now, the use cases, you know, that, that's the, the key thing. Having realistic use cases that are money-making in robotics is, is a big challenge. You know, there's the warehouse use case. That's probably the only thing that makes anybody any money in robotics at this point.

• 1:40:38Lex Fridman?

  There's gotta be a moment.

• 1:40:39Speaker 01

  There's old fashioned, I mean, there's aren't fixed arms doing manufacturing. I don't want to say that they're not making money.

• 1:40:45Lex Fridman?

  Industrial robotics, yes, but I, there's gotta be a moment when social robotics starts making real money. Meaning like a spa type robot in the home and there's tens of millions of them in the home. And they're like, you know, I don't know how many dogs there are in the United States as pets. But this feels many, it feels like there's something we love about having a intelligent companion with us that remembers us, that's excited to see us, all that kind of stuff.

• 1:41:12Speaker 01

  But it's also true that the company's making those things. There've been a lot of failures in recent times, right? There's that one year when I think three of them went under. So it's, it's not that easy to do that, right? Getting, you know, getting performance, safety and cost, all to be where they need to be at the same time is, uh, that's, that's hard.

• 1:41:35Lex Fridman?

  But also some of it is, like you said, you can have a product, but

• 1:41:40Lex Fridman?

  people might not be aware of it. So like also part of it is the videos or however you connect with the public, the, the culture and like create the category that make, make people realize this is the thing you want. Cause from a, you know, there's a lot of negative perceptions you can have. Do you really want a system with a camera in your home, walking around, right?

• 1:42:01Lex Fridman?

  If, if it's presented correctly and if there's like the right kind of boundaries around it, that you understand how it works and so on, that a lot of people would want to. And if they don't, they might be suspicious of it. So that, that's important. Like we, we all use smartphones and that has a camera that's looking at us.

• 1:42:17Speaker 01

  Yeah, it has two or three or four.

• 1:42:19Lex Fridman?

  And it's listening. Isn't a very few people are, uh, you know, suspicious about it. They kind of take it for granted and so on. And I think robots would be the same kind of way.

• 1:42:28Speaker 01

  I, I agree.

• 1:42:30Lex Fridman?

  So as you work on the cognitive aspect of, uh, of these robots, do you think we'll ever get to human level or superhuman level intelligence? There's been a lot of conversations about this recently, given the rapid development in large language models.

• 1:42:49Speaker 01

  Um, I, I think that intelligence is a lot of different things. And I think some things, computers are already smarter than people. And some things, they're not even close. And, you know, I think you'd need a menu of, of detailed categories to come up with, uh, with that. But I also think that the, you know, the, the, the conversation that seems to be happening about AGI is puzzles me. It's sort of. So I ask you a question. Do you think there's anybody smarter than you in the world?

• 1:43:23Lex Fridman?

  Absolutely, yes.

• 1:43:25Speaker 01

  Do you find that threatening?

• 1:43:26Lex Fridman?

  No.

• 1:43:27Speaker 01

  So I don't understand, even if computers were smarter than people, why we should assume that that's a threat. Especially since they could easily be smarter, but still available to us or under our control, which is basically how computers generally are.

• 1:43:45Lex Fridman?

  I think the fears that they would be 10x, 100x smarter and

• 1:43:53Lex Fridman?

  operating under different morals and ethical codes than humans like naturally do. And so almost become misaligned in unintended ways and therefore harm humans in ways we just can't predict. And even if we program them to do a thing as on the way of doing that thing, that would cause a lot of harm. And when they're 100 times, 1,000 times, 10,000 times smarter than us, we won't be able to stop it or we won't be able to even see the harm as it's happening until it's too late. That kind of stuff. So you can construct all kinds of like possible trajectories of how the world ends because of superintelligent systems.

• 1:44:33Speaker 01

  It's a little bit like that line in the Oppenheimer movie where they contemplate whether the first time they set off a reaction, all matter on earth is gonna, you know, go, go up. I don't remember what the verb they used was for, for the chain reaction, right?

• 1:44:55Speaker 01

  Yeah, I guess it's possible, but I don't, I don't think, I personally don't think it's worth worrying about that. I think that the, you know, it's an opportunity, balancing opportunities and risk. I think if you take any technology, there's opportunity and risk and, you know, it's easy to, I'll point at the car. They pollute and they, about what, 1.25 million people get killed every year around the world because of them. Despite that, I think they're a boon to humankind, very useful. We all love, many of us love them. And those technical problems can be solved. I think they are becoming safer.

• 1:45:38Speaker 01

  I think they're becoming less polluting, at least some of them are.

• 1:45:43Speaker 01

  And every technology you can name has a story like that, in my opinion.

• 1:45:48Lex Fridman?

  What's the story behind the Hawaiian shirt? Is it a fashion statement, a philosophical statement? Is it just a statement of rebellion?

• 1:45:58Lex Fridman?

  Engineering statement?

• 1:45:59Speaker 01

  It was born of me being a contrarian.

• 1:46:02Lex Fridman?

  Yes.

• 1:46:03Speaker 01

  Someone

• 1:46:03Lex Fridman?

  It's a symbol.

• 1:46:04Speaker 01

  Someone told me once that I was wearing one, when I only had one or two. And they said, oh, those things are so old fashioned, you can't wear that mark. And I stopped wearing them for about a week. And then I said, I'm not gonna let them tell me what to do. And so every day since, pretty much.

• 1:46:24Lex Fridman?

  So it's like a

• 1:46:24Speaker 01

  That was years ago. That was 20 years ago, 15, 15 years ago, probably.

• 1:46:28Lex Fridman?

  Ah, that says something about your personality. That's great. That's your nut.

• 1:46:32Speaker 01

  It took me a while to realize I was a contrarian, but you know, it can be a useful tool.

• 1:46:38Lex Fridman?

  Have you had people tell you about on the robotic side that like, I don't think you could do this? The kind of negative motivation?

• 1:46:49Speaker 01

  I'd rather talk about, there's a guy, when we were doing a lot of DARPA work, there was a marine, Ed Tovar, who's still around, who his, his, what he would always say is when someone would say, oh, you can't do that. He'd say, why not?

• 1:47:04Lex Fridman?

  Yeah.

• 1:47:05Speaker 01

  And it's a great question. I ask all the time when I'm thinking, oh, that's gonna, we're not gonna do that. And I think, why not? And I give him credit for opening my eyes to, to to resisting that.

• 1:47:19Lex Fridman?

  So, yeah, yeah, the Hawaiian shirt is almost like a symbol of why not? Okay.

• 1:47:25Lex Fridman?

  What advice would you give to young folks that are trying to figure out what they want to do with their life? How to have a life they can be proud of, how they can have a career they can be proud of?

• 1:47:34Speaker 01

  When I was teaching at MIT, I, for a while I had undergraduate advisees where, you know, people would have to meet with me once a semester or something. And they frequently would ask, you know, what they should do. And I think the advice I used to give was something like, well, if you had no constraints on you. Uh, no resource constraints, no opportunity constraints and no still skill constraints. What would you, could you imagine doing? And I said, well, start there and see how close you can get. You know, what's realistic for how close you can get. You know, the other version of that is, you know, try and figure out what you want to do and, and do that. Cause I don't, I don't think, a lot of people think that they're in a channel, right?

• 1:48:18Speaker 01

  And there's only limited opportunities, but it's usually wider than they think.

• 1:48:25Lex Fridman?

  Yeah, the opportunities really are limitless, but like, at the same time, you want to

• 1:48:31Lex Fridman?

  pick a thing, right? And it's the, the diligence and really, really pursue it, right? Like really pursue it.

• 1:48:39Speaker 01

  Yeah.

• 1:48:41Lex Fridman?

  Uh, cause sometimes like the really special stuff happens after years of pursuit.

• 1:48:46Speaker 01

  Yeah. Oh, absolutely. It can take, it can take a while.

• 1:48:50Lex Fridman?

  I mean, you've been doing this for forty plus years.

• 1:48:52Speaker 01

  People, some people think I'm in a rut, right? Why don't I do? And in fact, the, some of the inspiration for the AI institute is to say, okay, I've been working locomotion for however many years it was. Let's do something else. And uh, it's, it's a really fascinating and interesting challenge and.

• 1:49:13Lex Fridman?

  And you, uh, you're hoping to show it off also in the same way as.

• 1:49:16Speaker 01

  Just about to start showing some stuff off, yeah? I hope we have YouTube channel. I mean, one of the challenges is it's one thing to show athletic skills on YouTube. Showing cognitive function is a lot harder and I haven't quite figured out yet how that's going to work.

• 1:49:35Lex Fridman?

  There might be a way.

• 1:49:36Speaker 01

  There's a way.

• 1:49:37Lex Fridman?

  There's a way.

• 1:49:37Speaker 01

  Why not?

• 1:49:38Lex Fridman?

  I also do think

• 1:49:42Lex Fridman?

  sucking at a task is also compelling, like the incremental improvement. A robot being like really terrible at a task and then slowly becoming better. Even in athletic intelligence, honestly, like learning to walk and falling and slowly figuring that out. I think there's something extremely compelling about that. Like we like flaws, especially with the cognitive task. It's okay to be clumsy. It's okay to be confused and a little silly and all that kind of stuff.

• 1:50:10Lex Fridman?

  It feels like in that space is where we can.

• 1:50:13Speaker 01

  There's charm.

• 1:50:14Lex Fridman?

  There's charm. The charm and there's something inspiring about a robot sucking and then becoming less terrible slowly. At a test.

• 1:50:25Speaker 01

  I think you're right.

• 1:50:26Lex Fridman?

  That kind of reveals something about ourselves. And ultimately, that's what's one of the coolest things about robots is it's kind of a mirror about what makes humans special, just by watching how hard it is to make a robot do the things that humans do. You realize how special we are.

• 1:50:46Lex Fridman?

  What do you think is the meaning of this whole thing? Why are we here? Mark. You ever ask about the big questions as you try to create these humanoid, human-like intelligence systems?

• 1:51:00Speaker 01

  I don't know. I think you have to have fun while you're here. That's about all I know. It would be a waste. It would be a waste not to, right?

• 1:51:08Lex Fridman?

  The ride is pretty short, so might as well have fun.

• 1:51:13Lex Fridman?

  Mark, I'm a huge fan of yours. It's a huge honor that you would talk with me. This is really amazing and your work for many decades has been amazing. And I can't wait to see what you do at the AI Institute. I'm going to be waiting impatiently for the videos and the demos and the next robot meetup for maybe Atlas and Optimus to hang out.

• 1:51:35Speaker 01

  I would love to do that. That would be fun.

• 1:51:37Lex Fridman?

  Thank you so much for talking to me.

• 1:51:39Speaker 01

  Thank you. It was fun talking to you.

• 1:51:41Lex Fridman?

  Thanks for listening to this conversation with Mark Riber. To support this podcast, please check out our sponsors in the description. And now let me leave you with some words from Arthur C. Clarke. Whether we're based on carbon or on silicon makes no fundamental difference. We should each be treated with appropriate respect. Thank you for listening and hope to see you next time.