Marc Raibert: Boston Dynamics and the Future of Robotics | Lex Fridman Podcast #412

1. 0:00 – 1:43

   Introduction

   1. MRMarc Raibert0:00

      So BigDog became LS3, which is the big load-carrying one.

   2. LFLex Fridman0:03

      Just, just a quick pause. It can carry 400 pounds and-

   3. MRMarc Raibert0:08

      It was designed to carry 400, but we had it carrying about 1,000 pounds, uh, one time. (laughs)

   4. LFLex Fridman0:12

      Of course you did. (laughs) Just to make sure.

   5. MRMarc Raibert0:15

      We had one carrying the other one. We had two of them, so we had one carrying the other one.

   6. LFLex Fridman0:19

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

   7. MRMarc Raibert0:37

      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.

   8. LFLex Fridman0:48

      The following is a conversation with Marc 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 BigDog, Atlas, Spot, and Handle. This was a big honor and pleasure for me. This is the Lex Fridman Podcast. To support it, please check out our sponsors in the description. And now, dear friends, here's Marc Raibert.

2. 1:43 – 6:47

   Early robots

   1. LFLex Fridman1:43

      When did you first fall in love with robotics?

   2. MRMarc Raibert1:47

      Well, I was always a builder from a, from a young age. I wa- I was lucky. My father was a, uh, frustrated, uh, engineer, and by that I mean, uh, he wanted to be an aerospace engineer, but his mom, from the old country, thought that that would be like a grease monkey. (laughs)

   3. LFLex Fridman2:05

      Mm-hmm.

   4. MRMarc Raibert2:05

      And so she said no, so he became an accountant. But the, but the result of that was our basement was always full of, uh, tools and equipment and electronics and, you know, from a young age I would watch him, uh, assembling a kit, an ICO kit or something like that. I still have a couple of his old ICO kits. And, uh, but it was really, uh, during graduate school when, uh, uh, I followed, uh, a professor back, uh, from class. It was, uh, Berthold Horn at MIT, and I was taking a, uh, an interim class. It's IAP, Independent Activities Period.

   5. LFLex Fridman2:42

      Mm-hmm.

   6. MRMarc Raibert2:43

      And I followed him back to his lab, and on the table was a, a Vicarm robot arm taken apart in probably a thousand pieces. And, uh, when I saw that, you know, from that day on, uh, I was a roboticist. (laughs)

   7. LFLex Fridman2:58

      Do you remember the year?

   8. MRMarc Raibert2:59

      1974.

   9. LFLex Fridman3:01

      1974. So there's just this arm in pieces-

   10. MRMarc Raibert3:04

       Yeah.

   11. LFLex Fridman3:04

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

   12. MRMarc Raibert3:12

       Somehow it, uh, it spurred my imagination. I ha- I was in the, uh, Brain and Cognitive Sciences Department as a graduate student doing neurophysiology. I'd been a electrical engineer as an undergrad at Northeastern, and, uh, the neurophysiology wasn't really working for me, you know? I, it wasn't conceptual enough. I couldn't see really how by looking at single neurons you were gonna get to a place where you could understand, you know, control systems or thought or anything like that. And, uh, you know, the AI Lab was always an appealing... This was before CSAIL, right? This was in the '70s. So-

   13. LFLex Fridman3:48

       Mm-hmm.

   14. MRMarc Raibert3:48

       ...the AI Lab was always an appealing idea, and so when I went to the AI Lab with, uh, you know, following him, uh, and I saw the arm, I just thought, you know, "This is it."

   15. LFLex Fridman3:59

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

   16. MRMarc Raibert4:09

       Well, BCS has now morphed a bit, right? They, they have the Center for Brains, Minds, and Machines, which is, uh, 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, uh, 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 '80s, and then recently, uh, you know, the last five or so years there's been a, a stronger connection.

   17. LFLex Fridman4:48

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

   18. MRMarc Raibert5:02

       I mean, we were just doing gadgets when we were kids-

   19. LFLex Fridman5:04

       Yes.

   20. MRMarc Raibert5:04

       ...you know? I had a friend, we were taking, uh, you know the... I don't know if everybody remembers, but fluorescent lights had this, uh, little, m- uh, aluminum cylinder. Uh, and I can't even remember what it's called now, that you needed in a starter I think it was.

   21. LFLex Fridman5:20

       Mm-hmm.

   22. MRMarc Raibert5:20

       Uh, and we would take those apart, fill them with match heads, put a tail on it, and make it into little rockets. (laughs)

   23. LFLex Fridman5:27

       So it wasn't always about function. It was... Well-

   24. MRMarc Raibert5:30

       Well, rocket was pretty-

   25. LFLex Fridman5:31

       (laughs)

   26. MRMarc Raibert5:31

       ...pretty much functional.

   27. LFLex Fridman5:31

       (laughs) I guess that is pretty functional.

   28. MRMarc Raibert5:33

       Yeah.

   29. LFLex Fridman5:33

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

   30. MRMarc Raibert5:40

       I think it's a, it's still a balance between those two.
3. 6:47 – 25:27

   Legged robots

   1. MRMarc Raibert6:47

   2. LFLex Fridman6:47

      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. Uh, a lot of the robots you built, especially in the early days, were very aggressive, uh, under-actuated. They're hopping. They're, (laughs) they're wild, moving quickly. So what, is there a philosophy underlying that?

   3. MRMarc Raibert7:20

      Well, let me tell you about how I got started on legs at all. I, 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. You know, 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 a thousand people there. And I went to a talk. Uh, 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.

   4. LFLex Fridman7:56

      Mm-hmm.

   5. MRMarc Raibert7:56

      And he showed a six-legged, uh, robot that walked very slowly. Um, it always had at least three feet 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." You know, that's not, that's not anything like how people and animals work because we balance and fly, you know, we have to predict what's gonna happen in order to keep our balance when we're taking a running step or something like that. We use the springiness in our, in our legs, you know, in our muscles and our tendons and things like that as part of the story, you know, the energy circulates. We don't just throw it away-

   6. LFLex Fridman8:38

      Mm-hmm.

   7. MRMarc Raibert8:38

      ... every time. So no, I'm not sure I understood all that when I first thought, but I, I definitely got inspired to say, you know, "Let's try the opposite." And I didn't have a clue as to how to make a hopping robot work, not re- you know, not balanced in 3D. Uh, in fact, when I started, it was all just about the energy of bouncing and I was gonna have a springy thing in the leg and some actuator so that you could get an energy, uh, regime going of bouncing. And the idea that balance was an important part of it didn't come until a little later. Uh, and then, you know, I made the, the one-legged, uh, the pogo stick robots.

   8. LFLex Fridman9:16

      Mm-hmm.

   9. MRMarc Raibert9:17

      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, we can, you know, it's come along, but I think it's all too safe, and I, 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. Um, so now, to be fair, uh, being more aggressive is gonna mean things aren't (laughs) gonna work very well for a while. So it's a long, it's a longer term approach to the problem. Um, 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, trying to, uh, encourage our group to, to go at it.

   10. LFLex Fridman10:22

       Well, yeah, I mean, we'll talk about what it means to-

   11. MRMarc Raibert10:25

       (laughs)

   12. LFLex Fridman10:26

       ... what, what is the actual thing we're trying to optimize and, uh, 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 perf- perfect modeling of the object and perfect movement of the arm to gras- grasp that object 'cause, uh, maybe perfection is not supposed to exist in the real world.

   13. MRMarc Raibert10:57

       I don't know if you know my friend Matt Mason who's, uh, who is the, uh, 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 'em was grasping. He would, she would nudge, roll, flatten with her, you know, knife, things like that, and none of them was grasping.

   14. LFLex Fridman11:28

       (laughs) So okay, let's go back to the early days. First of all-

   15. MRMarc Raibert11:30

       Sure.

   16. LFLex Fridman11:31

       ... you've, you've created and led the Leg Lab, the legendary Leg Lab at MIT. So what, what was that first hopping robot? Can you...

   17. MRMarc Raibert11:38

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

   18. LFLex Fridman11:41

       Carnegie Mellon.

   19. MRMarc Raibert11:42

       So I was a professor there starting, uh, in 1980, uh, into about 1986. And, uh, so that's where the first hopping machines were built, uh, 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, uh, we built at the Leg Lab, the first version, was built in about 1984 or '5, and really only got going about '86 or so. And it took years of development to get it to-

   20. LFLex Fridman12:17

       Let's just pause here.

   21. MRMarc Raibert12:18

       (laughs)

   22. LFLex Fridman12:18

       For people who don't know, I'm talking to Marc 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 we- you, like, skipped over this, but, like, go, go to the first hopping robot. There's videos of some of this. I mean, these are incredible robots that you talked about. The fir- uh, th- th- the very first step was to get a thing hopping up and down.

   23. MRMarc Raibert12:46

       Right.

   24. LFLex Fridman12:47

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

   25. MRMarc Raibert12:57

       Sure.

   26. LFLex Fridman12:57

       ... what was involved in creating that robot?

   27. MRMarc Raibert13:00

       Well, I'm gonna start on the- not the technical side-

   28. LFLex Fridman13:03

       Mm-hmm.

   29. MRMarc Raibert13:03

       ... but the, uh, I guess we could call it the motivational side-

   30. LFLex Fridman13:07

       (laughs) Sure.
4. 25:27 – 28:45

   Boston Dynamics

   1. LFLex Fridman25:27

   2. MRMarc Raibert25:27

      Right.

   3. LFLex Fridman25:27

      Uh, what are some fond memories from the early days?

   4. MRMarc Raibert25:31

      Uh, one of the robots that we built wasn't, wasn't actually a robot. It was a surgical simulator, but it had force feedback, so it had all the techniques of robotics. And you look down into this, uh, 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 they were where you were looking, and you had tools in your hands that were connected up to these force feedback devices made by, uh, 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 was in the late '90s, when you had, uh, a silicon graphics computer that was about this big. Uh, you know, it was the heater in the office, basically.

   5. LFLex Fridman26:22

      Nice. Right.

   6. MRMarc Raibert26:23

      And, uh, and we were doing, uh, 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, uh, 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, uh, a surgical trade show, and the surgeons were practically lined up. Well, we, we kept a score and we posted their scores like on a video game. And those guys are so competitive that they really, uh, really love 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 gonna 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, there was no money from the surgeons.

   7. LFLex Fridman27:33

      Yeah.

   8. MRMarc Raibert27:34

      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. Uh, you know, there's no way we could go after a marketing activity. You know, the company was all bootstrapped in those years. We, we never had investors until Google bought us in, 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 gonna 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.

   9. LFLex Fridman28:25

      What was that meeting like? Were you just, like, sitting at a table, "You know what?" (laughs)

   10. MRMarc Raibert28:30

       Probably.

   11. LFLex Fridman28:30

       "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 that came from it."

   12. MRMarc Raibert28:39

       It just always felt right whilst we did it, you know.

   13. LFLex Fridman28:42

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

   14. MRMarc Raibert28:44

       Yeah.

5. 28:45 – 36:52

   BigDog

   1. MRMarc Raibert28:45

   2. LFLex Fridman28:45

      What was the first robot you built under b- the, the flag of Boston Dynamics? BigDog?

   3. MRMarc Raibert28:51

      Well, there was the AIBO, uh, Runner, but it wasn't even a whole robot. It was just legs that we... We took off the legs on AIBOs and attached 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, uh, 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. Uh, you know, English across the boundary with, uh, Japan was a challenge, trying to understand what, what each of us was saying and have meetings every week, uh, for, for several years doing that. And, uh, 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.

   4. LFLex Fridman29:51

      It was a quadruped? The, the legs were four legs-

   5. MRMarc Raibert29:54

      Yeah.

   6. LFLex Fridman29:54

      ... or two legs?

   7. MRMarc Raibert29:55

      Yeah, no, four legs. Yeah.

   8. LFLex Fridman29:56

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

   9. MRMarc Raibert30:03

      Mostly, we learned that something that small, uh, 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, like-

   10. LFLex Fridman30:17

       That's funny.

   11. MRMarc Raibert30:18

       One of my things was to show stuff in video even from the very early days of the hopping machines. Um...... and so I was always focused on how's this gonna look through the viewfinder. And, uh, running AIBO didn't look so cool through the viewfinder.

   12. LFLex Fridman30:32

       So, uh, what- what came next in terms of, uh... What was a big next milestone in terms of a robot you built?

   13. MRMarc Raibert30:40

       I mean, you gotta say that BigDog was... You know, sort of put us on the map and got our heads really pulled together. We scaled up the company. BigDog was the result of, uh, Alan Rudolph at DARPA, uh, starting a biodynotics program. And he put out a, you know, a request for proposals and, uh, I think there were 42 proposals written and three got funded. One was BigDog, one was a climbing robot, RISE... And, you know, that put things in motion. We, we hired, uh, Martin Buehler. He was a, a professor at Mon- in Montreal at, uh, McGill. He was, uh, incredibly important for getting BigDog, uh, out of the lab and into the mud, which is a... You know, it was a key step to really be willing to go out there and, uh, and build it, break it, fix it, which is sort of one of our mottos at the company.

   14. LFLex Fridman31:32

       So testing it in the real world.

   15. MRMarc Raibert31:33

       Testing.

   16. LFLex Fridman31:33

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

   17. MRMarc Raibert31:47

       No.

   18. LFLex Fridman31:47

       ... have built, but a lot.

   19. MRMarc Raibert31:48

       Well, it's 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 BigDog did, but it had, uh, a hydraulic pump sitting in the room with hoses connected to the robot.

   20. LFLex Fridman32:00

       Mm-hmm.

   21. MRMarc Raibert32:01

       It had a VAX computer in the next room. It needed its own room 'cause it was this giant thing with air conditioning, and it had this very complicated, uh, bus connected to the robot. And the robot itself just had the actuators, it had gyroscopes for sensing and other, some other sensors, uh, but all the power and computing was off-board. BigDog had all that stuff integrated, uh, on the platform. It had a gasoline engine for power, which was a very complicated thing to, to undertake. It had to convert the rotation of the engine into hydraulic power, which is how we, uh, actuated, uh, it. So there was a lot of learning just on the, uh, you know, building the physical robot and, and, uh, the system integration, uh, for that. And then there was the controls, uh, of it.

   22. LFLex Fridman32:49

       So for BigDog, you brought it all together onto one platform-

   23. MRMarc Raibert32:52

       Right.

   24. LFLex Fridman32:53

       ... and then so you could, you can-

   25. MRMarc Raibert32:54

       You could take it out in the woods.

   26. LFLex Fridman32:55

       Yeah, you could... And you did. (laughs)

   27. MRMarc Raibert32:57

       We did. We spent a lot of time down at the, uh, Marine Corps base in Quantico where there was a trail called, uh, the Guadalcanal Trail. And our, uh, 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.

   28. LFLex Fridman33:19

       Hi-

   29. MRMarc Raibert33:19

       Those were fun days.

   30. LFLex Fridman33:20

       ... hiking with the robot.
6. 36:52 – 38:44

   Hydraulic actuation

   1. MRMarc Raibert36:52

      did.

   2. LFLex Fridman36:52

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

   3. MRMarc Raibert36:57

      You know, I had been in love with hydraulics and still, uh, love hydraulics. Uh, you know, it's, it's a great technology. It's too bad that the, somehow the, the world out there looks at it like it's old-fashioned or that it's, um, 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. Uh, but if you look at the performance, uh, you know, how strong you can get in a lightweight package... And of course we did a huge amount of innovation. Most of hydraulic, uh, control, that is the valve that controls the flow of oil, had been designed in the '50s for airplanes.

   4. LFLex Fridman37:35

      Mm-hmm.

   5. MRMarc Raibert37:36

      It had been made robust enough, safe enough that you could count on it so that 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, uh, the ones that are in, uh, uh, 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 the whole robot could be smaller and lighter. Uh, 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, uh, kilograms, and it produces five kilowatts of power. Of course, it has to have a battery, uh, 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.

7. 38:44 – 44:31

   Natural movement

   1. MRMarc Raibert38:44

   2. LFLex Fridman38:44

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

   3. MRMarc Raibert39:02

      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. Um, 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 gonna be, uh, 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 gonna, I'm gonna try and adjust for that." But you really need to think about what's coming.

   4. LFLex Fridman39:40

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

   5. MRMarc Raibert39:43

      Uh, it's interesting. I think that the number is only a couple of seconds for Spot, so there's a limited horizon, uh, type approach where you're recalculating, assuming what's gonna happen in the next, uh, 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 gonna 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, uh, 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? If you wanna stick the landing-

   6. LFLex Fridman40:26

      Yeah.

   7. MRMarc Raibert40:26

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

   8. LFLex Fridman40:38

      How hard is it to stick a landing? I mean, that's, uh, very much underactuated. 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? You, first of all, did flips with a hopping robot.

   9. MRMarc Raibert40:57

      If you look at the first time we ever made a robot do a somersault, it was in a planar robot. You know, it had a boom, uh, so it could only, it was, uh, restricted to the surface of a sphere. We call that planar. So it could move fore and aft, 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. Uh, 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, uh, linear momentum. But, you know, that's not too hard to figure out. And we made, you know, back in, uh, about 1985 or '6, 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 planar thing. Roy Murabawa is my graduate student when we were at MIT, which is when we made a, you know, a two-legged robot do a 3D somersault for the first time. Um, there, we, in order to get enough rotation rate, you needed to do tucking also. Uh, you know, we'd draw the legs in order to accelerate it, and he did some really fascinating work on, on how you stabilize more complicated maneuvers. You remember, he was a gymnast, a champion gymnast before he'd come to me.

   10. LFLex Fridman42:25

       (laughs)

   11. MRMarc Raibert42:25

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

   12. LFLex Fridman42:37

       He knew how humans do it, he just-

   13. MRMarc Raibert42:39

       Yeah.

   14. LFLex Fridman42:39

       ... had to get robots-

   15. MRMarc Raibert42:40

       Yeah.

   16. LFLex Fridman42:40

       ... to do the same.

   17. MRMarc Raibert42:41

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

   18. LFLex Fridman42:44

       Yeah.

   19. MRMarc Raibert42:45

       Right? We, we're-

   20. LFLex Fridman42:46

       That's right.

   21. MRMarc Raibert42:46

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

   22. LFLex Fridman42:57

       So in some way-

   23. MRMarc Raibert42:58

       (laughs)

   24. LFLex Fridman42:58

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

   25. MRMarc Raibert43:07

       Right.

   26. LFLex Fridman43:08

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

   27. MRMarc Raibert43:10

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

   28. LFLex Fridman43:23

       That's interesting, right? Uh, 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?

   29. MRMarc Raibert43:37

       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 meaning springiness, and the-

   30. LFLex Fridman43:49

       Yeah, yeah.
8. 44:31 – 51:23

   Leg Lab

   1. LFLex Fridman44:31

      So at the Leg Lab, I believe most of the robots didn't have knees. (laughs) H- 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, 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.

   2. MRMarc Raibert44:58

      Mm-hmm.

   3. LFLex Fridman44:59

      So that seems to be kind of important.

   4. MRMarc Raibert45:01

      Mm-hmm. Mm-hmm.

   5. LFLex Fridman45:01

      So how do you figure out for efficiency, for function, for aesthetics-

   6. MRMarc Raibert45:05

      Mm-hmm.

   7. LFLex Fridman45:06

      ... uh, how many joints to have? How many actuators to have?

   8. MRMarc Raibert45:09

      Well, there's always a balance between wanting to get where you really wanna 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-

   9. LFLex Fridman45:27

      Mm-hmm.

   10. MRMarc Raibert45:27

       ... like a human. I think, uh, 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, an ankle. I'll tell you the first sketch that Ben Brown made, uh, when we were talking about building this thing. It was a very complicated thing with zillions of springs, lots of joints. It looked like, much more like a, uh, 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, uh, say, "Okay, well, let's do something simpler to get started, and maybe we'll get there at some point."

   11. LFLex Fridman46:10

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

   12. MRMarc Raibert46:14

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

   13. LFLex Fridman46:27

       Dumb question. Uh, do ostriches have, like, musc- mus- a lot of musculature on the legs or no?

   14. MRMarc Raibert46:33

       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.

   15. LFLex Fridman46:40

       Mm-hmm.

   16. MRMarc Raibert46:40

       The thing that's halfway down the leg that looks like a backwards knee is actually the ankle.

   17. LFLex Fridman46:46

       Ah.

   18. MRMarc Raibert46:46

       The thing on the ground which looks like the foot is actually the toes. It's an extended toe.

   19. LFLex Fridman46:51

       Fascinating.

   20. MRMarc Raibert46:52

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

   21. LFLex Fridman46:59

       What do you think is, uh, the most beautiful movement of an animal? Like, what animal do you think is the coolest-

   22. MRMarc Raibert47:05

       (laughs)

   23. LFLex Fridman47:06

       ... land animal that's cool? 'Cause fish is pretty cool, like the-

   24. MRMarc Raibert47:09

       Uh-

   25. LFLex Fridman47:09

       ... way a fish moves through water, but like-

   26. MRMarc Raibert47:10

       Yeah.

   27. LFLex Fridman47:10

       ... legged locomotion.

   28. MRMarc Raibert47:11

       You know, the slo-mos of cheetahs running are, are incredible. You know, their, they, there's so much back motion and, uh, you know, grace in their, of course they're moving very fast. Uh, the animals running away from the cheetah are pretty exciting. You know, the pronghorn, uh, which, you know, they, they do this, uh, all four legs at once jump called a prong to kind of confuse the, especially if there's a group of them, to confuse whoever's chasing them.

   29. LFLex Fridman47:39

       So they do like a misdirection type of thing?

   30. MRMarc Raibert47:41

       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 stabilize in the turns, those, that's pretty exciting.
9. 51:23 – 54:41

   AI Institute

   1. MRMarc Raibert51:23

      do.

   2. LFLex Fridman51:23

      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. And 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?

   3. MRMarc Raibert51:57

      You know, I like to talk about intelligence having two parts, an athletic part and a, uh, cognitive part.

   4. LFLex Fridman52:03

      Mm-hmm.

   5. MRMarc Raibert52:04

      And, uh, I think, you know, Boston Dynamics, in my view, has sort of set the standard for, uh, what athletic intelligence can be, and you know, it has to do with all the things we've been talking about. The, the mechanical design, the, the real-time control, the energetics, and that kind of stuff. But obviously, uh, people have another kind of intelligence, and, and animals have another kind of intelligence, you know. We can make a plan. Uh, our meeting started at, at 9:30. I looked up on Google Maps how long it took to walk over here, it was, you know, 20 minutes, so, uh, I decided, okay, I'd leave my house at 9:00, which is what I did.

   6. LFLex Fridman52:41

      Mm-hmm.

   7. MRMarc Raibert52:42

      Um, 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. Um, 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, uh, they need to be smarter. Uh, so the in- AI Institute is designed to combine that physicality of the athletic side with, uh, the cognitive side. So for instance, we're trying to make robots that can watch a human do a task, uh, understand what it's seeing, and then do the task itself. So sort of OJT for robo- on-the-job training for robots.

   8. LFLex Fridman53:34

      Mm-hmm. Mm-hmm.

   9. MRMarc Raibert53:35

      Uh, as a paradigm. Uh, now, you know, that's pretty hard, uh, and it's, it's sort of science fiction, but our idea is to work on a longer timeframe and, and work on, uh, solving those kinds of problems. And I have a whole list of things that are kind of like in that, in that vein.

   10. LFLex Fridman53:53

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

   11. MRMarc Raibert53:58

       Okay.

   12. LFLex Fridman53:59

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

   13. MRMarc Raibert54:10

       And using our hands, you know?

   14. LFLex Fridman54:11

       And using our hands.

   15. MRMarc Raibert54:11

       The mechanics of interacting with all, you know, these parts here.

   16. LFLex Fridman54:15

       Yeah.

   17. MRMarc Raibert54:15

       I'll take these two things.

   18. LFLex Fridman54:15

       Yeah.

   19. MRMarc Raibert54:16

       You know? I'm not-

   20. LFLex Fridman54:16

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

   21. MRMarc Raibert54:17

       I'm not loo- I've never tou- Well, I've touched ones like this.

   22. LFLex Fridman54:19

       Okay. (laughs)

   23. MRMarc Raibert54:20

       But look at all the things I can do, right?

   24. LFLex Fridman54:21

       Like this.

   25. MRMarc Raibert54:21

       I can juggle and I'm rotating it-

   26. LFLex Fridman54:23

       Yeah.

   27. MRMarc Raibert54:23

       ... this way. I can rotate it without looking.

   28. LFLex Fridman54:25

       Mm-hmm.

   29. MRMarc Raibert54:25

       I could fetch these things out of my pocket and figure out which one was which-

   30. LFLex Fridman54:29

       Mm-hmm.
10. 54:41 – 1:02:35

    Athletic intelligence

    1. LFLex Fridman54:41

       What are the big open problems in athletic intelligence? So Boston Dynamics...... all 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 kinda zoom out? What are the big open problems on the athletic intelligence side?

    2. MRMarc Raibert55:01

       I mean, one question you could ask that isn't my question but, you know, are they commercially, uh, viable-

    3. LFLex Fridman55:06

       Mm-hmm.

    4. MRMarc Raibert55:07

       ... uh, could, will they increase productivity?

    5. LFLex Fridman55:10

       Yeah.

    6. MRMarc Raibert55:10

       And I think, you know, we're getting very close to that.

    7. LFLex Fridman55:13

       Mm-hmm.

    8. MRMarc Raibert55:13

       Uh, I don't think we're quite there still. You know, most of the robotics companies it's, it's, uh, 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.

    9. LFLex Fridman55:27

       Interesting.

    10. MRMarc Raibert55:27

        But, uh, you know, your question's a good one. I mean, you can always do a thing that's, uh, more efficient, uh, lighter, more reliable. I'd say reliability. You know, I know that Spot, they've been working very hard, uh, on getting the, the tail of the reliability curve up, and they've made huge progress. So the robots, you know, there, there's, uh, 1,500 of them out there now, uh, many of them being used in, uh, practical applications day in and day out, uh, where, you know, where they have to work reliably.

    11. LFLex Fridman55:59

        Mm-hmm.

    12. MRMarc Raibert56:00

        And, uh, you know, it's very exciting that they've done that. But it takes a huge effort to get that kind of reliability, uh, in the robot. There's cost too. You know, you'd like-

    13. LFLex Fridman56:08

        Uh-huh.

    14. MRMarc Raibert56:08

        ... to get the cost down. Uh, Spots are still pretty expensive, uh, and I don't think that they have to be.

    15. LFLex Fridman56:15

        Mm-hmm.

    16. MRMarc Raibert56:16

        But it takes, you know, a different kind of activity to do that.

    17. LFLex Fridman56:20

        Mm-hmm.

    18. MRMarc Raibert56:21

        Now that, uh, you know, I think, you know, that, uh, Boston Dynamics is owned primarily by, uh, Hyundai now, and I think that the skills of Hyundai in making cars can be brought to bear in, uh, uh, making robots that are less expensive and more reliable and those kinds of things.

    19. LFLex Fridman56:39

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

    20. MRMarc Raibert56:50

        That's a good question. I think we're, we're using the paradigm called stepping-stones to moonshots.

    21. LFLex Fridman56:55

        (laughs)

    22. MRMarc Raibert56:56

        I do- I don't believe any... That, that was in my original proposal for-

    23. LFLex Fridman57:00

        Mm-hmm.

    24. MRMarc Raibert57:00

        ... the AI 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, uh, 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 gonna take several years. Uh, you know, those, those stepping-stone results give you feedback, uh, give motivation because usually there's some success in there. Uh, and so, you know, that's the mantra, uh, we've been working on. And that's pretty much how, uh, you know, I'd, I'd say Boston Dynamics has worked, uh, you know, where the, you make progress, uh, uh, and show it as you go. Show it to yourself if not to the world.

    25. LFLex Fridman57:45

        What does success look like? Like what, what are the, some of the milestones you're, uh, you're chasing?

    26. MRMarc Raibert57:52

        Well, we've, we've, with watch-understand-do, the project I mentioned before, you know, we've broken that down into, uh, getting some progress with what does meaningfully watching something mean, uh, breaking down, uh, an observation of a person doing something into the components. You know, segment- segmenting, you know, you watch me do something. I'm gonna pick up this thing and put it-

    27. LFLex Fridman58:14

        Mm-hmm.

    28. MRMarc Raibert58:14

        ... down here and stack this on it. Well, it's not obvious if you just look at the raw data, uh, uh, what the sequence of acts are. It's, 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, "Okay, what skill do I need to accomplish each of those things?" Uh, 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, uh, 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.

    29. LFLex Fridman58:55

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

    30. MRMarc Raibert59:00

        Mm-hmm. We're starting out with bicycle repair, some simple-
11. 1:02:35 – 1:05:37

    Building a team

    1. MRMarc Raibert1:02:35

       too.

    2. LFLex Fridman1:02:36

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

    3. MRMarc Raibert1:02:58

       Sure. Uh, technical fearlessness means being willing to take on a problem that you don't know how to solve.

    4. LFLex Fridman1:03:03

       Mm-hmm.

    5. MRMarc Raibert1:03:04

       Uh, and, you know, uh, study it, uh, figure out an ac-, 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.

    6. LFLex Fridman1:03:28

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

    7. MRMarc Raibert1:03:32

       Yeah (laughs) , and you don't know how to do it, and, you know, there's easier stuff to do in life. Uh, so, you know ... I mean, I don't know, watch, understand, do. (laughs) It's a, it's a mountain of a, of a challenge.

    8. LFLex Fridman1:03:45

       So that's the b- 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?

    9. MRMarc Raibert1:03:57

       Yeah. I mean, we have others like that. I, th- we have one called Inspect-Diagnose-Fix. Like, uh, you know, you, uh, call up the Maytag, uh, repair man. Okay, he's the one who you don't have to call. But you, 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.

    10. LFLex Fridman1:04:24

        Mm-hmm.

    11. MRMarc Raibert1:04:25

        And, uh, I think robots should be able to do that. Uh, we already, uh, Boston Dynamics already has spot robots collecting data on machines, things like thermal data, reading the gauges, listening to them, getting sounds. And that data are used to determine whether they're healthy or not.

    12. LFLex Fridman1:04:45

        Mm-hmm.

    13. MRMarc Raibert1:04:46

        But the interpretation isn't done by the robots yet. And the, uh, certainly the, the fixing, the diagnosing and the fixing isn't done yet, but I think it could be. And, you know, that's bringing the AI in and combining it with the physical skills to do it.

    14. LFLex Fridman1:05:00

        Yeah, and you're referring to the fixing in the physical world. I can't wait until they can fix the-

    15. MRMarc Raibert1:05:04

        (laughs)

    16. LFLex Fridman1:05:04

        ... psychological problems of humans and show up and just talk.

    17. MRMarc Raibert1:05:07

        Oh. (laughs)

    18. LFLex Fridman1:05:07

        Do therapy.

    19. MRMarc Raibert1:05:08

        No, that's a, that's a different thing.

    20. LFLex Fridman1:05:10

        Yeah, it's a different ... Well-

    21. MRMarc Raibert1:05:11

        But-

    22. LFLex Fridman1:05:11

        ... it's all part of the same thing, again, humanity. (laughs) Maybe, maybe. Uh-

    23. MRMarc Raibert1:05:17

        You mean convincing you it's okay that the dishwasher is broken, just do them for half?

    24. LFLex Fridman1:05:20

        Oh, yeah, that's- (laughs)

    25. MRMarc Raibert1:05:21

        (laughs)

    26. LFLex Fridman1:05:21

        Yeah. Yeah, that's very

    27. NANarrator1:05:22

        (laughs)

    28. MRMarc Raibert1:05:22

        ... the marketing approach?

    29. LFLex Fridman1:05:24

        Yeah, exactly. It's all, yeah, don't smet- uh, don't, don't sweat the small stuff. (laughs) Yeah, as opposed to fixing the dishwasher-

    30. MRMarc Raibert1:05:31

        (laughs)
12. 1:05:37 – 1:13:25

    Videos

    1. LFLex Fridman1:05:37

       diligence, why is diligence important?

    2. MRMarc Raibert1:05:40

       Well, if you want a real robot solution, it can't be, uh, a very narrow solution that's gonna break at the first variation in what the robot does or the environment, if it wasn't exactly as you expected it.

    3. LFLex Fridman1:05:54

       Mm-hmm.

    4. MRMarc Raibert1:05:55

       So how do you get there? I think, uh, having an approach that leaves you unsatisfied until you've embraced the bigger problem is the, is the diligence I'm talking about. And, uh, again, I'll point at Boston Dynamics. I think they've done a ... You know, some of the videos that we had showing the engineer making it hard for the robot to do its task. Uh, 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, on the rope that's attached to the robot, so its navigation has been screwed up.

    5. LFLex Fridman1:06:29

       Mm-hmm.

    6. MRMarc Raibert1:06:30

       Uh, we have one where the robot's climbing stairs, and the, uh, 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 wanna come up with a robust solution is what I meant by diligence.

    7. LFLex Fridman1:06:54

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

    8. MRMarc Raibert1:06:58

       Right.

    9. LFLex Fridman1:06:59

       And as a result, creating some epic videos.

    10. MRMarc Raibert1:07:01

        (laughs)

    11. LFLex Fridman1:07:02

        Uh, the legendary-

    12. MRMarc Raibert1:07:02

        The fun part, the hockey stick.

    13. LFLex Fridman1:07:04

        (laughs) And then, yes, tugging on Spot as it's trying to open the door. I mean, the, it's, it's great testing, but it's also...I don't know, it just s- somehow extremely compelling demonstration of robotics in video form.

    14. MRMarc Raibert1:07:21

        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, uh, interfered with or tested while it's going through the door, it's showing you the scope of the solution.

    15. LFLex Fridman1:07:53

        The, the limits of the system, the, the challenges involved in failure. It's th- it's showing both failure and success makes you appreciate the, the success. Yeah. And then, just the way the videos are done in Boston Dynamics, they're incredible 'cause they're not, there's no flash, there's no extra, like, production. It's just raw testing of the robot.

    16. MRMarc Raibert1:08:13

        Well, you know, I was the final edit for most of the videos up until, uh, until about three years ago or four years ago. And, uh, you know, my theory of the video is no, no explanation. I- 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.

    17. LFLex Fridman1:08:47

        That's brilliant.

    18. MRMarc Raibert1:08:48

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

    19. LFLex Fridman1:08:53

        Yeah, I mean, people always wanna add more stuff. But the simplicity of just-

    20. MRMarc Raibert1:08:58

        Yeah.

    21. LFLex Fridman1:08:58

        ... do something worth showing and show it, that's brilliant. And don't add extra stuff.

    22. MRMarc Raibert1:09:02

        And people, people have criticized, uh, especially the, the BigDog 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.

    23. LFLex Fridman1:09:22

        Mm-hmm.

    24. MRMarc Raibert1:09:22

        The fact that there's a human, so they were thinking about autonomy, whereas we were thinking about the, the mobility.

    25. LFLex Fridman1:09:28

        Yeah.

    26. MRMarc Raibert1:09:28

        Uh, and so, you know, we've, we've adjusted to a lot of things that we see that people care about.

    27. LFLex Fridman1:09:34

        Mm-hmm.

    28. MRMarc Raibert1:09:35

        Uh, trying to be honest. We've always tried to be honest.

    29. LFLex Fridman1:09:38

        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, uh, and dancing with some music. Uh, intrepidness and fun, so in- intrepid.

    30. MRMarc Raibert1:09:57

        I mean, it might be the most important ingredient.
13. 1:13:25 – 1:16:53

    Engineering

    1. MRMarc Raibert1:13:25

    2. LFLex Fridman1:13:25

       Uh, and so fun.

    3. MRMarc Raibert1:13:27

       Fun. Technical fun, I usually say.

    4. LFLex Fridman1:13:30

       Technical.

    5. MRMarc Raibert1:13:30

       Have technical fun. I think that, that life as an engineer is really satisfying.

    6. LFLex Fridman1:13:35

       Mm-hmm.

    7. MRMarc Raibert1:13:36

       I think you get to, uh, you know, to some degree, it, 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, um, you know, in many cases you can have impact, uh, on the world somehow because you've done something that other people admire. Which is dif- different from the own, just the craft fun of, of building a thing. Uh, so that's a second way that, uh, that being engineer is good. I think the third thing is that the, if you're lucky to be working in a team where you're, uh, getting the benefit of other peoples' skills that are helping you do your thing, uh, you know, none of us has all the skills needed to do, um, most of these projects. And, uh, 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.

    8. LFLex Fridman1:14:43

       (laughs)

    9. MRMarc Raibert1:14:43

       And so you-

    10. LFLex Fridman1:14:43

        Yeah.

    11. MRMarc Raibert1:14:43

        ... you kinda get paid four times-

    12. LFLex Fridman1:14:45

        Yeah. (laughs)

    13. MRMarc Raibert1:14:46

        ... uh, in my view of the world.

    14. LFLex Fridman1:14:47

        Yeah.

    15. MRMarc Raibert1:14:48

        So what could be better than that?

    16. LFLex Fridman1:14:49

        Get paid to have fun.

    17. MRMarc Raibert1:14:50

        Yeah.

    18. LFLex Fridman1:14:50

        Uh, I mean, what, what do you love about engineering? What, when you say engineering, what does that, what does that mean to you exactly? What is this kinda big thing that we call engineering?

    19. MRMarc Raibert1:15:00

        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 'cause you're making some... You know, scientists only get to describe, to, to study what's out there, and engineers get, get to make stuff that didn't exist before.

    20. LFLex Fridman1:15:16

        Mm-hmm.

    21. MRMarc Raibert1:15:16

        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.

    22. LFLex Fridman1:15:26

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

    23. MRMarc Raibert1:15:48

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

    24. LFLex Fridman1:16:05

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

    25. MRMarc Raibert1:16:37

        But I think sort of with 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.

    26. LFLex Fridman1:16:48

        That's very satisfying, yeah.

    27. MRMarc Raibert1:16:48

        And, you know, there are people who do that, and it's, it's a creative, a creative act.

14. 1:16:53 – 1:21:40

    Dancing robots

    1. MRMarc Raibert1:16:53

    2. LFLex Fridman1:16:53

       Uh, what, what to you is the most beautiful about robotics?

    3. MRMarc Raibert1:16:57

       (laughs)

    4. LFLex Fridman1:16:59

       Sorry for the big romantic question.

    5. MRMarc Raibert1:17:01

       I think having the robots move in a way that's, uh, uh, evocative of life is, is pretty exciting.

    6. LFLex Fridman1:17:08

       So the elegance of movement?

    7. MRMarc Raibert1:17:09

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

    8. LFLex Fridman1:17:22

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

    9. MRMarc Raibert1:17:27

       Yeah. 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 h- we've been having a ballerina in who's, uh, 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, uh, you know, way of doing a choreography or something like that more accessible, uh, to, uh, to get the robot to do things. And she's starting to produce some interesting stuff.

    10. LFLex Fridman1:17:58

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

    11. MRMarc Raibert1:18:00

        There is.

    12. LFLex Fridman1:18:01

        (laughs) I, I mean, I, I, I guess I saw versions of it, uh, (laughs) which is pretty cool. You can kinda-

    13. MRMarc Raibert1:18:08

        (laughs)

    14. LFLex Fridman1:18:08

        ... at, at slices of time-

    15. MRMarc Raibert1:18:10

        Uh-huh.

    16. LFLex Fridman1:18:10

        ... control different parts at the high level, the movement of the robot and Spot and the others.

    17. MRMarc Raibert1:18:14

        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 do- done a lot, but there's still a lot possible. And I'd like to have, uh, 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-

    18. LFLex Fridman1:18:46

        Mm-hmm.

    19. MRMarc Raibert1:18:46

        ... but I think it should do those things.

    20. LFLex Fridman1:18:49

        I think I would love to see a professional ballerina like alone in a room with a robot slowly teaching the robot.

    21. MRMarc Raibert1:18:56

        (laughs)

    22. LFLex Fridman1:18:56

        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-

    23. MRMarc Raibert1:19:08

        Right.

    24. LFLex Fridman1:19:09

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

    25. MRMarc Raibert1:19:19

        One of the cool things going on, y- you know that there's a class at Brown University called Choreorobotics? Sydney Skybetter-

Episode duration: 1:43:46