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

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 LEGO 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.

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When did you first fall in love with robotics? Well, I was always a builder from a from a young age. 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 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 and I co kid or something like that. I still have a couple of his old I co kids. And, but it was really during graduate school when I followed a professor back from class, it was a Bertolt Horn at MIT. And I was taking an interim class at its IAP independent activities period.

And I followed him back to his lab and on the table was a a vicar robot arm taken apart in probably a thousand pieces. And when I saw that, you know, from that day on, I was a roboticist. Do you remember the year 1974 1974. So there's just this arm and pieces. Yeah. And you saw the pieces and you saw in your in your vision, the arm when it's put back together and the possibilities that holds 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 undergraduate in Northeastern. And the neurophysiology wasn't really working for me. You know, I 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 70s. 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.

It's so interesting. The tension between the BCS brain cognitive science approach, understanding intelligence and the robotics approach to understanding intelligence. 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 Mar was in the AI lab. David Mar 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 80s. And then recently, you know, the last five or so years, there's been a stronger connection.

You said you were always kind of a builder. What stands out to you in memory of a thing you built, maybe a trivial thing that just kind of like inspired, inspired you in the possibilities that this direction of work might hold. I mean, we were just doing gadgets when we were kids, you know, I have 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. So it wasn't always about function. It was well, rocket was pretty, pretty, pretty functional. But I guess that is a question. How much was it about function versus just creating something cool?

I think 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 pilot junk, which a lot of my robots look like in those days. But then when it started moving, you'd get the idea that it, you know, it had some kind of life or some kind of interest in its movement. And I think it would be purposely even design the machines, not worrying about the aesthetics of the structure itself.

But then, you know, it turns out that the aesthetics of the thing itself, add and combine with the life like things that the robots can do. But the heart of it is, you know, making them do things that are interesting. So one of the things that underlies a lot of your work is that the robots who create the systems you have created 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, undirectuated. They're hopping. They're wild, moving quickly. So what is there a philosophy underline that? 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 and was a biological legged locomotion conference.

So 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, 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 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 anything like how people of animals work.

Because we balance and fly, you know, 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, you know, our muscles in our tendons and things like that. As part of the story, you know, the energy circulates. We don't just throw it away every time. So, and I'm not sure I understood all that when I first thought, but 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, you know, not balance in 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, you know, might have made the one-legged, the pogostic robots.

Now, I think that we need to do that in manipulation. If you look at robot manipulation, we've been working a community, it's 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 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 hand 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, being more aggressive is going to mean things are going to work very well for a while. So it's a long, it's a longer term approach to the problem. 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 encourage our group to go at it.

Well, yeah, I mean, we'll talk about what it means to what is the actual thing we're trying to optimize for robot, you know, sometimes especially human robot interaction, maybe flaws is a good thing. Perfection is not necessary. The right thing to be chasing, just like you said, maybe, maybe being good at fumbling an object, being good at fumbling, maybe the right thing to optimize versus perfect modeling of the object and perfect movement of the arm to craft.

Grass grass that object is maybe perfection is not supposed to exist in the real world. I don't know if you know my friend, Matt Mason, who's who is the director of the robotics Institute at Carnegie Mellon and we go back to graduate school together, but he analyzed a movie of Julia Childs doing cooking thing.

And she did, I think he said something like there were 40 different ways that she handled the 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. 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? But first of all, the leg lab actually started at Carnegie Mellon. So, I was a professor there starting in 1980 to 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, 5, and really only got going about 86 or so. Took years of development to get it to. Let's just pause here. For people who don't know, I'm talking to Mark Weber, 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, all that. So, you've been doing this for a really long time. So, you're like skipped over this, but they go, go to the first hopping robot. There's videos of some of this. And these are incredible robots. So, you talked about the very first step was to get a thing hopping up and down. And then, you realized, well, balancing is the 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? 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 Evans and 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, those are kind of related institutions.

And so, 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, brilliant guy, brilliant engineer and a really 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 a first model, which I went, you know, I went to the shop and with my own hands kind of made a first model, which didn't work. And was just, you know, a beginning shot at it. 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. Yeah.

Ivan, who had been there in his previous life. And so, we walked around and we looked in offices, because I didn't know anything. You know, I was basically a kid, but Ivan moves away 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, we opened and it had just the skeleton of this one like a topping robot. And we showed it to him.

And you can almost see the drool going down his chin. So, you said 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.

Did you see the possibility of worth this is going? Why this is an important problem? No. The balance, I mean, it's, it has to do with leg look emotion. I mean, it has to do with all these problems that, that the human body solves when we're walking, for example, like all the fundamentals are there.

Yeah, I mean, I think that was the motivation to try and get more at 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. 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. And have fun. And have fun.

Pogo stick robot. 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 eventually doing flips and all this kind of stuff.

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 started that out. It wasn't that hard to make it balanced once you get the physical machine to be working well enough and have enough control over the degrees of the

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 switched to a thing that could run around the room on another device. It's hard to explain these without you seeing them, but you probably know and talking about 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. So, okay, you mentioned inverted pendulum, but like can you explain how a hopping stick in 3D.

Can control. Can balance itself. Yes. What are what what is the actuation look like? 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 in a pogostick. 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. 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, you know, 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 happen just keep rotating around. But if it gets too far, then you've right right 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. The physics don't work out how far does it have to tell before it's too late to be able to balance itself or some possible to balance itself correct itself. Well, you're asking a just in question because in those days, we didn't actually optimize things. And they probably could have gotten 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 your sense, 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 is one thing. If you're running like up a stair, 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.

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?

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 dreamer. So when you talk about pogos that robot or legged robots, whether it's squadron PES or humanoid robots, did people doubt that this is possible? Did you experience a lot of people around you kind of?

I don't know if they doubt whether it was possible, but I think they thought it was a waste of time. Oh, it's not even an interesting problem. 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 go out and, you know, attack these, these harder things. And I think it has turned out, I think like it locomotion has turned out to be a useful thing.

Did you ever have like doubt about bringing atlas to life, for example, or, or, or with big dog just every step of the way. Did you have doubt like what this is, this is too hard for a problem? I mean, at first I wasn't an enthusiast for the human rights, 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-punning for me as a robot assist. I think the functionality really matters.

So, probably that's why I avoided human robots, humanoid robots to start with. But I'll tell you, now, you know, after we start working on them, you could see that the, the connection and the impact with, with other people, whether they're lay people 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.

But we answer for more flies than we see that's humanity. But also like with the, with spot, you can see not the humanity, but whatever we find compelling about social interactions there in spot as well. I'll tell you, you know, I go around giving talks and take spot 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 commenters like to say that it's frightening.

But when you take a spot out there, you, 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. Yeah, 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 I-Bone.

Yeah. So, and he's robot dog. Yeah. It's just an interesting little tidbit of history for me. What, what, like what stands out to you memory from that task? For people 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 care a lot of payload and move fast and stuff like that?

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 I-Bone runner, by we programmed, we made some tools for programming Curio, which was a small, 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 they did show it. You know, when I look back I say that we got us back where we belonged. Yeah, you rediscovered the soul of the company. That's right. And so from there, it was always about robots.

Yeah. So, you started Boston Dynamics in 1992. Right. What are some fond memories from the early days? One of the robots that we built wasn't actually robotic. It was a surgical simulator, but it had forced 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 forced feedback devices made by another MIT spin-out sensible technologies. So, they made the forced feedback device. We attached the tools and we brought 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 the silicon graphics computer that was about this big, you know, it was the heater in the office basically.

And we were doing surgical operations, an asthmosis, 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. Well, we kept the score and we posted their scores like on a video game. And those guys are so competitive that they really, 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 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 to 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. You know, the company was all bootstrapped in those years. We never had investors until Google bought us, which was after 20 years.

So we didn't have any resources to go after hospitals. So we at one sort of at one day Rob and I were looking at that and we said, we 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 what would work and what wouldn't even though technically it was really a fascinating thing.

What was that meeting like where you just like sitting at it? You know what? Probably 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. It just always felt right when we did it. You know, just look at each other and said, let's build robots. Yeah. What was the first robot you built under the flag of Boston dynamics? Big dog.

Well, there was the IBO runner, but it wasn't even a whole robot. It was just legs that we took off the legs on IBOs and attached legs we made and, you know, we got that working and showed it to the Sony people. 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 for several years doing that. And it was a pleasure working with them. They were really supporters. They they seem 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. It was a quadruped the legs were four legs or two legs, no four legs.

And what did you learn from that experience of building it basically a fast moving quadruped? Mostly we learned that something that small doesn't look very exciting when it's running. It's like it's scampering and you had 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 funny one of my things was to show stuff in video from the very early days of the hopping machines.

And so I was always focused on how is this going to look through the viewfinder and running IBO didn't look so cool through the viewfinder. So what came next in terms of what was a big next milestone in terms of the robot you built? I mean you got to say that big dog was 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 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 at Montreal at McGill. He was incredibly important for getting big dog out of the lab and into the mud, which is a key step to really be willing to do.

And he was really willing to go out there and build it break and fix it, which is sort of one of our models at the company. So testing it in the real world. For people who don't know big dog, maybe you can correct me. It's a big quadruped four leg robot that it looks big. Probably carry a lot of weight. Not the most weight that Boston and Amsterdam built, but a lot.

Well, it's the first thing that work. So let's see if we go back to the leg lab, we 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 undertake. 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 building the physical robot and the system integration for that. And then there was the controls of it. So for big dog, we brought it all together onto one platform. And then so you can take it out in the woods. Yeah, and you did. 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 a lot of challenge. And we spent a lot of time, our team spent a lot of time down there. Those were fun days hiking with the robot. What did you learn about what it takes to balance the robot like that on a trail on a hiking trail on the woods? I forgot the woods, just the real world. That's the big leap into testing in the real world.

As challenging as the woods were, working inside of a home or in an office is really harder. Because when you're in the woods, you can actually take any path up the hill. All you have to do is avoid the obstacles. There's no such thing as damaging the woods, at least, 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 we faced them. It was mostly getting the systems to work well enough to gather the hardware systems to work. The controls, in those days, we did have a human operator who did all the visual perception going up the Guadalcanal trail. There was an operator who was right there who was very skilled at even though the robot was balancing itself and placing its own feet. If the operator didn't do the right thing, it wouldn't go.

Years later, we went back with one of the electric precursor to Spot. 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's taken us years to get there in the previous robot. So if you fast forward, Big Dog eventually became Spot. So Big Dog became LS3, which is the big load carrying one. Just a quick pause. It can carry 400 pounds.

It was designed to carry 400, but we had it carrying about a thousand pounds. Of course you did. 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. It can go for very long distances. You can travel 20 miles. Gasoline. That eventually just... Okay, sorry. LS3, then how did that lead to Spot?

So Big Dog and LS3 had engine power and hydraulic actuation. Then we made a robot that was electric power. So there's a battery driving a motor, driving a pump, but still hydraulic actuation. 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. 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. What was the conversational Larry page like about? Here's a guy that is very product focused and can see a vision of what the future holds. That's just interesting kind of a side. What was the brainstorm about the future robotics with him? It was almost as simple as when I just said, you know, we had a meeting. He said, yeah, geez, do you think you could make a smaller one that wouldn't be so intimidating?

It'll be like a Big Dog if it was in your house. And I said, yeah, we could do that. And we started and did. Is there a lot of technical challenges to go from hydraulic to electric? I had been in love with hydraulics and still love hydraulics. 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, how strong you can get in a lightweight package. 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 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 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. 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 produces five kilowatts of power. Of course, it has that battery operating, but it's got a motor, a pump, filters, heat exchanger to keep it cool, some valves, all in this tiny little package. So hydraulics, you know, could still have a ways to go. One of the things that stands out about the robots, Boston and Amos have created is how beautiful the movement is.

How natural the walking is and running is, even flipping was throwing is so maybe you can talk about what's involved in making it look natural. 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.

So how far ahead do you do you have to look in time? 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 the limited horizon type calculation that's doing a lot of that.

But if you're doing something like a summer song, you're looking out a lot further, right? If you want to stick to landing, you have to get the right, 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. How hard is it to stick a landing? I mean, it's very much under-exuated 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, it did flips with a hopping robot. If you look at the first time we ever made a robot do a summer song, it was in a planar robot, you know, it had a boom. So it could only, it was restricted to the surface of a sphere, we call that planar. So it could move for an 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. 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.

And we made, you know, back in about 1985 or 6, I can't remember. We had a simple robot doing summer songs. To do it in 3D, really the calculations is the same. You just have to be balancing in the other degrees of freedom. If you're just doing a summer song, it's just a planar thing. Roy Marabo is my graduate student and we ran MIT, which is when we made, you know, a two-legged robot do a 3D summer song for the first time.

There, we, in order to get enough rotation rate, you needed to do tucking also. You know, we draw the legs in order to accelerate it. And he did some really fascinating work on how you stabilize more complicated maneuvers. Remember, he was a gymnast, a champion gymnast before he'd come to me. So he had the physical abilities. And he was, you know, an engineer so he could translate some of that into the math and algorithms that you need to do that.

He knew how humans do it. He just had to get robots to do the same. Unfortunately, though, when humans don't really know how they do it. Yeah, right. We were coached. 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. So in some way, by building robots, you are in part understanding how humans do it, like walking. Most of us walk without considering how we walk really.

Right. And how we make it so natural and efficient, all those kinds of athletes 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 challenge. That's interesting, right? The 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? 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 compliance is compliance means springiness and the design that are important to us. Well, we used to have a motto at Boston Dynamics in the early days, which is the half the run before you can walk.

That's a good motto. You also had Wildcat, which was one of the among the way towards spot, which is a quadripetent 19 miles an hour on flat terrain. Is that the fastest you've ever built? Oh, yeah. It might be the fastest quadripet in the world. I don't know. For a quadripet, 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 three buildings away sending us complaining about how loud it was.

So at the leg lab, I believe most of the robots didn't have knees. What's the, how do you figure out what is the right number of actuators? What are the joints to have? What do you need to have? 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. 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, how many joints to have, how many actuators to have? Well, it's always a balance between wanting to get where you really want to get and what's practical to do based on your resources or what you know and all that.

So, I mean, the whole idea of the, of the pogostick was to do 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 like it without having a knee and ankle. I'll tell you the first sketch that Ben Brown made when we were talking about building this thing was a very complicated thing with zillions of springs, lots of joints.

It looked like much more like a kangaroo or an ostrich or something like that, things we were paying a lot of attention to at the time. 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. I just love the idea that you two were studying kangaroos and ostriches. Oh yeah, we did, we filmed and digitized data from horses. I did a dissection of ostrich at one point, which has absolutely remarkable legs.

It's dumb question. Do ostriches have like musk a lot of musk literature on the legs or no? 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 joints 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. But the basic morphology is the same in all these animals.

What do you think is the most beautiful movement of an animal? What animal do you think is the coolest land animal? It's cool because fish is pretty cool. The fish must have water, but the leg is a locomotion. The slow mobs of cheetahs running are incredible. There's so much back motion and grace, and of course they're moving very fast.

The animals running away from the cheetah are pretty exciting. The pronghorn, which they do this all four legs at once jump, call the prong to confuse the, especially if there's a group of them to confuse whoever's chasing them. So did you like a misdirection type of thing? Yeah, 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 pretty exciting.

Because they spent a lot of time in the air, I guess, that they're running that fast. But they also turn very fast. Is that a tail thing or do you have to have contact with the ground? Everything in the body is probably helping turn. Because 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 good to you. You know, you watch gymnasts, especially these days, they're doing just incredible stuff.

Well, like especially like Olympic level gymnasts. See, but there could be, there could be cheetahs, their Olympic club. We might be watching the average cheetah versus like, there could be like a really special cheetah that can do like, they're right. When do the knees first come into play in you building legged robot? I in big dog. Big dog. Yeah, big dog came first and then little dog was later. And you know, there was a big compromise there. Human knees have multiple muscles.

And you could argue that there's, I mean, it's a technical thing about negative work 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 pogostic springs down below. So part of the action was to comply in a bouncing motion.

You know, later on in spot, we took that out. So we got further and further away from the leg lab. We had more energy driven controls. Is there something to be said about like knees that go forward versus backward? Sure. There's this idea called passive dynamics, which says that 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.

So Tad McGear in the, I think in the mid 80s, maybe within the late 80s, started to work on that. And he made this legged system that could walk down and then climb plane where the legs folded and unfolded and swung forward. You know, do the whole walking motion where there was no computer. There were some adjustments to the mechanics so that there were dampers and springs in some places that help 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, or the talent dictating to the body what the body should do. The body is a participant in the motion. So a great design for a robot has a mechanical component where the movement is efficient even without a brain. Yes. How do you design that? I think that these days most robots aren't doing that. Most robots are basically using the computer to govern the motion.

The brain, though, is taking into account what the mechanical thing can do 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 machine wants to do.

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

I like to talk about intelligence having two parts, an athletic part and a cognitive part. I think Boston Dynamics, in my view, has set the standard for what athletic intelligence can be. 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.

We can make a plan. Our meeting started at 9.30. I looked up on Google Maps how long it took to walk over here. It was 20 minutes. So I decided, okay, I'd leave my house at 9, which is what 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 going to, 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. So, for instance, we're trying to make robots that can watch a human do a task, understand what that's seeing, and then do the task itself.

So sort of OJT for robots, on the job training for robots, as a paradigm. Now, you know, that's pretty hard. And it's sort of science fiction. But our idea is to work on a longer time frame and work on solving those kinds of problems. And I have a whole list of things that are kind of like in that vein. Maybe we can just take many of the things you mentioned, just take it as a tangent. Okay. First of all, athletic intelligence is a super cool term.

And that really is intelligence. We humans kind of take it for granted that we're so good at walking, moving about the world. And using our hands, you know, the mechanics of interacting with all these two things. You know, I'm not I'm not looking for it. Well, I've touched ones like this. Look at all the things I can do right. I can juggling. I'm rotating this way. I can rotate it without looking.

I could fetch these things out of my pocket and figure out which one was switch and all that kind of stuff. And I don't think we have much of a clue how all that works yet. 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 push 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? I mean, one question you could ask that isn't my question, but you know, are they commercially viable? Could will they increase productivity? Yeah. 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 physical least successful robots. But you know, your question is to go 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 tail of the reliability curve up. And they've made a huge progress. So the robots, you know, there's a 1500 of them out there now.

Many of them being used in practical applications, stay 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 costs 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 boss dynamics is owned primarily by Hyundai now. And I think that the skills of Hyundai in making cars can be brought to bear in making robots that are less expensive and more reliable and those kinds of things. So on the cognizide for the I institute, what's the tradeoff between moonshot projects for you and maybe incremental progress?

That's a good question. I think we're using the paradigm called stepping stones to moonshots. I don't believe that that was in my original proposal for the institute stepping stones to my shots. 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, you know, I'd say boss dynamics has worked, you know, where you make progress and show it as you go. Show it to yourself if not to the world. What does success look like? What are some of the milestones you're chasing?

Well, we've, we've, we've, watch, understand through the project I mentioned before, you know, we've broken that down into getting some progress with what is meaningfully watching something mean breaking down an observation of a person doing something into the components. You know, segment, 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 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? So we're working on you to the front end 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 may have been developing skills as well. So, you know, we have kind of multiple stabs at the pieces of doing that. And this is usually video of humans manipulating objects with their hands kind of thing. So we're starting out with bicycle repair, some simple bicycle repair. Oh, no. That seems complicated. That seems really complicated.

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 should be within range. Is it possible to observe, to watch a video like this without having an explicit model of what a bicycle looks like? I think it is. And I think that's the kind of thing that people don't recognize. 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. 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. So operate successfully under a lot of uncertainty. Yeah. And lack of specification. I mean, that's what sort of intelligence is, right? Kind of dealing with understanding a situation even though it wasn't explained. So how big of a role does machine learning play in all of this? Is this more and more learning based?

You know, since chat GBT, 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 learning, that kind of machine learning. Now, Chris, 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. But I think that there's a lot that can be done there. We have several people working on machine learning approaches. I don't know if you know, but we opened an office in Zurich recently. And Marco Hunter, who's one of the real leaders in reinforcement learning for robots, is the director of that office.

He's still half time at the ETH, the university there, where he has a 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 you look out in the world at what the most impressive performances are, there's 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 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 time. And then Marco has done some great stuff and others. So especially for the athletic intelligence piece, the traditional approach seems to be the one that still performs the bus.

I think we're going to find a mating in the tube and we'll have the best of both worlds. And we're working on that at the Institute too. If I can talk to you about teams, you've built an incredible team of boss dynamics before it on my TNC, Mu, and boss dynamics and out the AI Institute. And you said that there's four components to a great team, technical fearlessness, diligence, and trappiness and fun, technical fun. Can you explain each technical fearlessness? What do you mean by that?

Sure. Technical fearlessness means being willing to take on a problem that you don't know how to solve. And study it, figure out an entry point, maybe a simplified version or a simplified solution or something, learn from the stepping stone and go back and eventually make a solution that meets your goals. And I think that's really important. The fearlessness comes into play because some of it has never been done before.

Yeah, and you don't know how to do it. And you know, just the easier stuff to do in life. So, you know, I mean, I don't know. Watch, understand, do. It's a mountain of a challenge. So that's the really big challenge you're tackling now. Can we watch humans at scale and have robots by watching humans become effective actors in the world? I mean, we have others like that. We have one called inspect diagnosed fix.

Like, you know, you call up the Maytag repairman. He's the one who you don't have to call. But you know, you call up 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's wrong and then fix it. And 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, 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 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.

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. Yeah, that's a different thing. Yes, different. Well, but it's all part of the same thing again, humanity. Maybe, maybe you may convincing you it's okay that the dishwasher is broken just to the. Yeah, exactly. It's all. Yeah, don't smoke. Don't sweat the small stuff.

Yeah, supposed to fixing the dishwasher will convince you that it's okay that the dishwasher is broken to different approach. Diligence. Why is diligence important? 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 is the diligence I'm talking about. And again, I'll point it boss dynamics. I think they've done it. Some of the videos that we had showing the engineer making it hard for the robot to do its task. 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 it's navigation has been screwed up. We have one where the robot's climbing stairs and engineers 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.

So really testing in all conditions, preserving the system in all kinds of ways. And as a result creating some epic videos, the legendary fun part. And then yes, tugging on spot is trying to open the door. I mean, it's great testing, but it's also. I don't know. It's just somehow extremely compelling demonstration of robotics in video form. 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 doing its thing. So I think, you know, the reaction you just gave to 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. The limits of the system, the challenge is involved in failure. If you're showing both failure and success, makes you appreciate the success.

Yeah. And then just the way the videos are done and both at an end makes it incredible because there's no flash, there's no extra like production is just raw testing of the robot. Well, you know, I was the final edit for most of the videos up until about three years ago or four years ago. And you know, my theory of the video is 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 interfere with a bunch of titles that slow you down or a bunch of distraction. And then just do something worth showing and then show it. It's hard though for people to buy into that. 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.

Now people have criticized, especially the big dog videos where there's a human driving the robot. And I understand the criticism now at the time we wanted to just show look this things using its legs to get up the hill. So we focused on showing that, which was we thought the story, the fact that there's a human.

And so they were thinking about autonomy, whereas we were thinking about the mobility. And so, you know, 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. But also just show cool stuff in this 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 dancing with some music. Intrepidness and fun. So intrepid.

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. This 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 have to have courage to be intrepid when you work so hard to build your machine.

And then you're trying and it just doesn't do what you thought it would do what you want it to do. And you have to stick to it and keep trying. How long? I mean, we don't often see that the story behind spot and Atlas. How long, how many failures was there a long way to get it, you know, a working Atlas, a working spot in the early days, even working big dog? 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 109 tries and we have video of every one of them. You know, we shoot everything. Again, we, this is a boss dynamics. So it took 109 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 everything along the way is informative. And, you know, it shows sort of, 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. Or the perception didn't work right. So you missed the target when you jump or something breaks and there's oil flying everywhere. But that's fun. Yeah. So the hardware failures and then maybe some software lots of control of evolution during that time. I think it took six weeks to get that those 109 trials.

You know, because there was there was a programming going on, you know, it was it was actually robot learning, but there were human and the loop helping with the learning. So all data driven. Okay. And so, and you always are learning from that failure. So, right. And how do you, how do you protect Atlas from not getting damaged from 109 attempts?

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 got to figure that in if you're going to 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 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.

Yeah, if it falls 109 times, it's okay. Wow. So, in trap it truly, and that applies to spot that applies to all the other places. Everything. I think it applies to everything anybody tries to do that's worth doing. Yeah. And especially with systems in the real world, right? And so fun.

Technical fun. I usually 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 craft's 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 people admire, which is different from the own just the craft fun of building a thing.

So that's the second way that being engineers 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 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. So you get paid four times in my view of the world.

So what could be better than that? Get paid to have fun. I mean, what do you love about engineering? What, when you say engineering, what does that mean to you exactly? What is this kind of big thing that we call engineering? I think it's both being a scientist or getting to use science at the same time as being kind of an artist or 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. And at the cutting edge, I think 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 production at scale, which is it's so beautiful thing. But when you do the first new robot or the first new thing, that's the possibility to create something totally new. And 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 public response. We're going to do more. We're doing some at the institute and we'll do more.

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

But I think sort of with it 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 very satisfying. And you know, the people who do that and it's a creative, creative act. What what do you use the most beautiful bot robotics? Sorry for the big romantic question. I think having the robots moving away that's.

If I could live of life is is pretty exciting. So the elegance of movement. Yeah, or if it's a high performance act, we're doing it, you know, faster bigger than then other robots. We're not doing it bigger faster than people, but we know we're getting there in a few narrow dimensions. So faster, bigger, smoother, elegant, more graceful. I mean, I'd like to do dancing that that starts, you know, we're nowhere near the the dancing capabilities of a human.

We've been having a ballerina and who's kind of a well known ballerina and she's been programming 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 to get the robot through things and starting to produce some interesting stuff. Well, we should mention that there is a choreography tool. There is. I mean, I guess I saw versions of it, which is pretty cool.

You can kind of at it slices of time control different parts at the high level, the movement of the robot. We hope to take that forward and make it, you know, more tuned to how the dance world wants to talk, wants to communicate and get better performances. I mean, that 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 is to a fixed time base.

So once you press go, the robot does its thing and plays that thing. It's not listening, it's not watching, but I think it should do those things. I think I would love to see a professional ballerina, like a lone in a room with a robot slowly teaching the robot. Just actually the process of a clueless robot trying to figure out a small little piece of a dance. So it's not like, because right now Atlas and Spot have done like perfect dancing to a beat and so on.

To a degree. But like the learning process of interacting with the human would be like incredible to watch. One of the cool things going on, you know that there's a class at Brown University called Corior Robotics. Sydney Skybetter is a dancer, a choreographer. And he teamed up with Stephanie Telax, who's a computer science professor. And they taught this class, and I think they have some graduate students help and teach it, where they have two spots.

And people come in, I think it's 50-50 of computer science people and dance people. And they do program performances that are very interesting. I show some of them sometimes when I give a talk. And making that process of a human teaching the robot more efficient, more intuitive, maybe a language part movement. That'd be fascinating. That'd be really fascinating.

I mean, one of the things I've kind of realized is 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. Totally. And like that, you know, to watch a human and spot communicate back and forth with movement. I mean, there's so many wonderful possibilities there. But it's also a challenge. You know, we get asked to have our robots perform with famous dancers.

And they can, you know, they have 200 degrees of freedom or something, right? Every little ripple 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 a daunting challenge to not look stupid, you know, physically stupid next to them. So we've pretty much avoided that kind of performance. But we'll get to it. I think even with a 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. 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 day the humans by, you know, doing unstable oscillations and things that are faster than a person. So that's sort of on my, you know, my plan, but we haven't got quite gotten there.

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? I think you even need to have an environment where interesting engineer that well, you know, it's a chicken neck. If you have an environment where interested in engineering is going on, then engineers want to work there.

And, you know, I think it took a long time to develop that at Boston, I know. 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, Sarcos was another robot company.

So I always thought of us as 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, Sarcos got acquired and then they went through all kinds of changes. And I don't know exactly what their current status is, but. So it took, it took many years as part of the answer.

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 they really loved it. We also hired people who might not have professional degrees, you know, people who were building bicycles and building kayaks. So we have some people who come from that kind of the maker world. And that's really important for the kind of work we do to have that be part of the mix.

Whatever that is, whatever the magic agree and that makes a great builder maker. That's the big part of it. People who took, who repaired the car, the cars or built or motorcycles or whatever in their garages when there were kids. There's a kind of like the robotics students, grad students and just robotics 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, I compare it to another group of people that are like that are people that skydive professionally. There's just like an 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 joy of building a thing that you eventually works.

I'm talking about being happy. There used to be a time 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 had a good day today. You had a good tell. That's where I've been. You've done. You've done something. You've done something in the physical world. And probably the videos help, right? The videos help show off what robotics is.

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 products at all, which was true. And you know, we thank YouTube for that. YouTube came. We caught the YouTube wave and it had a huge impact on our company.

I mean, it's a big impact on not just the new company, but on robotics in general and helping people understand, inspire what is possible with robots. Inspire imagination, fear and everything. The full spectrum of human emotion was a rose, which is great, which is great for the entirety of humanity. And also, it's probably inspiring for young people that want to get into AI robotics. Let me ask you about some competitors.

Sure. You've been a complimentary of Elon and Tesla's work on Optimus Robot. What do you think of their efforts there with the human robot? 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 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 anything. You know, I don't think Optimus today is where Atlas is, for instance. I don't know. It's a little hard to compare him to the other companies. You know, I've visited a figure. I think they're doing well and they have a good team.

I've visited an electronic and I think they're having a good team and they're doing well. But Elon has a lot of resources. He has a lot of ambition. I'd like to take some credit for his ambition. 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 hope so. Do you think Atlas and Optimus will hang out at some point? I would love to host that.

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 robot meetups. I wrote up meetup. Does the AI Institute work with spots and Atlas is a focus on spots mostly right now as a platform? We have a bunch of different robots. We bought everything we could buy. So, we have spots. I think we have a good size fleet in them. I don't know how many it is, but a good size fleet.

We have a couple of animal robots. You know, animals, a company founded by Marco Harder, 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 in the US robotics. Because, you know, even though we have ambitions to build stuff and we are starting to build stuff, you know, they won't get off the ground. We just, you know, just bought stuff. I love this robot playground you've built.

You can come over and take a look at you, huh? That's great. So, it's like all these kinds of robots, legged arms. 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 frolic to get there. Hey, again, maybe you'll arrange it a robot meetup. But in general, what's your view on competition in the space for especially like humanoid and like robots? Are you excited by the competition or the friendly competition?

I think that it doesn't, you know, 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've said Boston Dynamics, we didn't think about competition. We were just kind of doing our thing. There wasn't, there 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 getting.

But even there, in a couple of cases where we might have competed, we ended up just being the robot provider. That is for the little dog program, you know, we just made the robots. We didn't participate as developers, except for developing the robot. And in the DARPA Robotics Challenge, we did compete, we provided the robots. So, you know, in the AI world now, now that we're working on cognitive stuff, it feels much more like a competition.

You know, the entry requirements in terms of computing hardware and the skills of the team are, and hiring talent, 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. They're definitely in competition, will be in competition.

How much room is there for quadruped and especially humanoid robot to become cheaper? So, like, cutting costs. And, like, how low can you go? 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? I think there's a huge way to go. I don't think we've seen the bottom up, or the bottom in terms of lower prices.

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 user's minds is, which quadruped do I want?

It's not, oh, 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. Then you're just, you know, doing the things you normally do to make your product better and compete, selling and all that stuff. And that'll be the way it is with humanoid at some point. Well, there's a lot of humanoid, and you're just not even, it's like, this phone versus Android and people just buying both and it's kind of just, you're not really...

You're creating the category, or the category is happening. I mean, right now, the use cases, you know, that's the key thing. Having realistic use cases that are money-making in robotics is a big challenge. You know, this is the warehouse use case. That's probably the only thing that makes anybody any money in robotics at this point. There's got to be a moment. There's old-fashioned robots. I mean, there's fixed arms doing manufacturing. I don't want to say that they're not making money.

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

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 not that easy to do that, right? Getting performance, safety, and cost, all to be where they need to be at the same time, is... That's hard. But also some of it is, like you said, you can have a product, but people might not be aware of it.

So also part of it is the videos are however you connect with the public, the culture, and create the category. And maybe people realize this is the thing you want. Because there's a lot of negative perceptions you can have. Do you really want a system with the camera in your home walking around? If it's presented correctly, and if there's a bunch of boundaries around it, they understand how it works, and so on, that a lot of people would want to.

And if they don't, that might be suspicious of it. So that's important. Like we all use smartphones, and that has a camera that's looking at us. Yeah, it has two or three or four. And it's listening. Isn't it a very few people are suspicious about it. They kind of take it for granted, and so on. I think robots would be the same kind of way. I agree. So as you work on the cognitive aspect 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. 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 need a menu of detailed categories to come up with with that. But I also think that the, you know, 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? Absolutely. Yes. Does it do you find that threatening? No. 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.

I think the fears that they would be 10X 100 X smarter and operating under different morals and ethical codes that humans like naturally do. And so almost become misaligned in unintended ways. And therefore harm humans in ways we can't predict. And even if we program them to do a thing as on the way of doing that thing, they would cause a lot of harm.

And when there are a hundred times, a thousand times, 10,000 times smarter than us, we won't be able to stop it or 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 super entire systems. 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 going to go up.

I don't remember what the verb they used was for the chain reaction, right? Yeah, I guess it's possible, but 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 in risk. I think if you take any technology, there's opportunity in risk. And you know, it's easy to put 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. I think they're becoming less polluting, at least some of them are. And every technology you can name has a story like that in my opinion. What's the story behind the Hawaiian shirt? Is it a fashion statement, a philosophical statement? Is it just a statement of rebellion?

It was born of me being a contrarian. 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 going to let them tell me what to do. And so every day since pretty much. That was 20 years ago. That was 15 years ago, probably. That says something while you're a personality. That's great. That's your nut.

It took me a while to realize I was a contrarian. But you know, it can be a useful tool. Have you had people tell you about on the robotics side that like, I don't think you could do this. The kind of negative motivation. I'd rather talk about this 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? Yeah.

And it's a great question. I asked all the time when I'm thinking, oh, that's going to, we're not going to do that. And I think, why not? And I give him credit for opening my eyes to to to resisting that. So yeah, yeah, the Hawaiian shirt is almost like a simple, why not? Okay. 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 that can be proud of how they can have a career that can be proud of?

When I was a 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, no resource constraints, no opportunity constraints, no skill constraints, what would you, could you imagine doing? And I said, well, start there and see how close you can get.

What's realistic for how close you can get? The other version of that is, you know, try and figure out what you want to do and do that. Because I don't think a lot of people think that they're in a channel, right? And there's only limited opportunities. Yeah. It's usually wider than they think. Yeah, the opportunities really are limitless. At the same time, you want to pick a thing, right? And it's the diligence. Yeah. And really, really pursue it, right? Yeah. Really pursue it.

Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Oh, absolutely. It can take a while. I mean, you've been doing this for 40 plus years. And I think people, some people think I'm in a rut, right? Why don't I do it? And in fact, some of the inspiration for the AI Institute is to say, okay, I've been working on a locomotion for however many years it was. Let's do something else. And it's a really fascinating and interesting challenge.

And you're hoping to show it off also in the same way as just about to start showing some stuff off. 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. There might be a way. There's a way. There's a way. Why not? I also do think sucking at a task is also compelling. Like the incremental improvement.

A robot being like really terrible at a task and slowly becoming better. Even in a athletic intelligence, honestly, like learning to walk and falling and slowly figuring that out. I think there's something extremely compelling about that. 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. It feels like in that space is where we can... There's charm. There's charm. There's charm.

There's something inspiring about a robot sucking and then becoming less terrible, slowly at a task. I think you're right. That kind of reveals something about ourselves. Ultimately, that's one of the coolest things about robots. It's kind of a mirror about what makes humans special. Just by watching a hearted system to make a robot do the things that humans do. You realize how special we are. What do you think is the meaning of this whole thing? Why are we here?

Mark, you have to ask about the big questions. As you try to create these humanoid, human-like intelligence systems. 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 not to, right? The ride is pretty short. Somebody as well have fun. Mark, I'm a huge fan of yours. It's a huge honor that you would talk with me. This was really amazing. And your work for many decades has been amazing. 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. I would love to do that. That would be fun. Thank you so much for talking to it. Thank you. It was fun talking to you. Thanks for listening to this conversation with Mark Reiber. 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 a propria respect. Thank you for listening. And hope to see you next time.