Russ Tedrake: Underactuated Robotics, Control, Dynamics and Touch | Lex Fridman Podcast #114

Lex Fridman 0:00 The following is a conversation with Russ Tedrake, a roboticist and professor at MIT, and vice president of robotics research at Toyota Research Institute, or TRI. He works on control of robots in interesting, complicated, underactuated, stochastic, difficult to model situations. He's a great teacher and a great person. One of my favorites at MIT. We'll get into a lot of topics in this conversation, from his time leading MIT's DARPA Robotics Challenge Team, to the awesome fact that he often runs close to a marathon a day to and from work barefoot. For a world-class roboticist interested in elegant efficient control of underactuated dynamical systems like the human body, this fact makes Russ one of the most fascinating people I know. Quick summary of the ads. Three sponsors. Magic Spoon Cereal, BetterHelp, and Express VPN. Please consider supporting this podcast by going to magic spoon.com/lex and using code Lex at checkout, going to better help.com/lex and signing up at express vpn.com/lexpod. Click the links in the description, buy the stuff, get the discount. It really is the best way to support this podcast. If you enjoy this thing, subscribe on YouTube, review it with five stars on Apple Podcast, support it on Patreon, or connect with me on Twitter @lexfridman. As usual, I'll do a few minutes of ads now and never any ads in the middle that can break the flow of the conversation. This episode is supported by Magic Spoon, low-carb, keto-friendly cereal. I've been on a mix of keto/carnivore diet for a very long time now. That means eating very little carbs. I used to love cereal. Obviously most have crazy amounts of sugar, which is terrible for you, so I quit years ago. But Magic Spoon is a totally new thing. Zero sugar, 11 grams of protein, and only three net grams of carbs. It tastes delicious. It has a bunch of flavors. They're all good, but if you know what's good for you, you'll go with cocoa. My favorite flavor and the flavor of champions. Click the magic spoon.com/lex link in the description, use code Lex at checkout to get the discount and to let them know I sent you. So buy all of their cereal. It's delicious and good for you. You won't regret it. This show is also sponsored by Better Help, spelled H-E-L-P help. Check it out at better help.com/lex. They figure out what you need and match you with a licensed professional therapist in under 48 hours. It's not a crisis line, it's not self-help, it is professional counseling done securely online. As you may know, I'm a bit from the David Goggins line of creatures and so have some demons to contend with, usually on long runs or all-nighters full of self-doubt. I think suffering is essential for creation, but you can suffer beautifully in a way that doesn't destroy you. For most people, I think a good therapist can help in this, so it's at least worth a try. Check out the reviews. They're all good. It's easy, private, affordable, available worldwide. You can communicate by text anytime and schedule weekly audio and video sessions. Check it out at better help.com/lex. This show is also sponsored by Express VPN. Get it at express vpn.com/lexpod to get a discount and to support this podcast. Have you ever watched The Office? If you have, you probably know it's based on a UK series also called The Office. Not to stir up trouble, but I personally think the British version is actually more brilliant than the American one, but both are amazing. Anyway, there are actually nine other countries with their own version of The Office. You can get access to them with no geo restriction when you use Express VPN. It lets you control where you want sites to think you're located. You can choose from nearly 100 different countries, getting you access to content that isn't available in your region. So again, get it on any device at express vpn.com/lexpod to get an extra three months free and to support this podcast. And now here's my conversation with Russ Tedrake.

Lex Fridman 4:29 What is the most beautiful motion of a animal or robot that you've ever seen?

Russ Tedrake 4:36 I think the most beautiful motion of a robot has to be the passive dynamic walkers. I think there's just something fundamentally beautiful. The ones in particular that Steve Collins built with Andy Ruina at Cornell. A 3D walking machine, so it was not confined to a boom or a plane, that you put it on top of a small ramp, give it a little push. It's powered only by gravity. No controllers, no batteries whatsoever. It just falls down the ramp, and at the time it looked more natural, more graceful, more human-like than any robot we'd seen to date. Powered only by gravity.

Lex Fridman 5:15 How does it work?

Russ Tedrake 5:17 Well, okay, the simplest model, it's kind of like a Slinky. It's like an elaborate Slinky. Um, one of the simplest models we use to think about it is actually a rimless wheel. So imagine taking, uh, a bike- bicycle wheel, but take the rim off, so it's now just got a bunch of spokes. If you give that a push, it still wants to roll down the ramp, but every time its foot, its spoke comes around and hits the ground, it loses a little energy. Every time it takes a step forward, it gains a little energy. Those things can come into perfect balance, and actually they, they want to. It's a stable phenomenon. If it's going too slow, it'll speed up. If it's going too fast, it'll slow down, and it comes into a stable periodic motion. Now you can take that...... rimless wheel, which doesn't look very much like a human walking. Take all the extra spokes away, put a hinge in the middle, now it's two legs. That's, uh, called our compass gait walker. That can still, you give it a little push, starts falling down a ramp, looks a little bit more like walking, at least it's a biped. But what Steve and Andy and, Tad McGeer started the whole exercise, but what Steve and Andy did was they took it to this beautiful conclusion where they built something that had knees, arms, a torso, the arms swung naturally, uh, give it a little push, and that looked like a stroll through the park.

Lex Fridman 6:38 How do you design something like that? I mean, is that art or science?

Russ Tedrake 6:42 It's on the boundary. I think there's a science to getting close to the solution. I think there's certainly art in the way that they, they made, they made a beautiful robot. But, um, but then the finesse, because, because this was work- they were working with a system that wasn't perfectly modeled, wasn't perfectly controlled, there's all these little tricks that you have to tune the suction cups at the knees, for instance, so that they stick, but then they release at just the right time. Or there's all these little tricks of the trade which really are art. But it was a point, I mean, it made the point. And we were, at, at that time, the walking robot, the best walking robot in the world was Honda's ASIMO. Absolutely marvel of modern engineering. It-

Lex Fridman 7:24 Is this '90s?

Russ Tedrake 7:25 This was in '97 when they first released, it sort of announced P2 and then it went through, it was ASIMO by then in 2004, um...

Lex Fridman 7:34 (laughs) And it, it looks like this, uh, very cautious walking, like you're walking on, on hot coals or something like that.

Russ Tedrake 7:41 It's, uh, uh, I think it gets a bad rap. ASIMO is a beautiful machine. It does walk with its knees bent. Our Atlas walking had its knees bent. But actually ASIMO was pretty fantastic. But it wasn't energy efficient, neither was Atlas when we worked on Atlas. Um, none of our robots have, that have been that complicated have been very energy efficient. But there was a, there's a thing that happens when you do control, when you try to control a system of that complexity. You try to use your motors to basically counteract gravity. Take whatever the world's doing to you and push back, erase the dynamics of the world, and impose the dynamics you want because you can make them simple and analyzable, mathematically simple. And this was a very s- sort of beautiful example that you don't have to do that. You can just let go. Let physics do most of the work, right? And you just have to give it a little bit of energy. This one only walked down a ramp. It would never walk on the flat. To walk on the flat, you have to give a little energy at some point. But maybe instead of trying to take the forces imparted to you by the world and replacing them, what we should be doing is letting the world push us around and we go with the flow. Very zen, very zen robot.

Lex Fridman 9:01 Yeah, but, okay, so that, that sounds very zen. But you can, I can also imagine how many, like, failed versions they had to go through. Like how many f- like I would say it's prob- would you say it's in the thousands that they've had to have the system fall down before they figured out how to get it?

Russ Tedrake 9:19 I don't know if it's thousands, but, uh, it's a lot. It takes some patience. There's no question.

Lex Fridman 9:25 So in that sense, control might help a little bit.

Russ Tedrake 9:28 Oh, the abs- I think everybody, even at the time, said that the answer is to do with that with control. But it was just pointing out that maybe the way we're doing control right now isn't the way we

Russ Tedrake 9:40 should.

Lex Fridman 9:41 Got it. So what about on the animal side? The ones that, uh, figured out how to move efficiently. Is there anything you find inspiring or beautiful in the movement of any particular animal?

Russ Tedrake 9:50 I do have a favorite example.

Lex Fridman 9:52 Okay. (laughs)

Russ Tedrake 9:54 So it sort of goes with the passive walking idea. So is there, you know, how, how energy efficient are animals? Okay, there's a great series of experiments by George Lauder at Harvard and Mike Tranquillo at MIT. They were studying fish swimming in a water tunnel.

Lex Fridman 10:10 Mm-hmm.

Russ Tedrake 10:10 Okay? And one of these, the type of fish they were studying were these rainbow trout, because they, there was an phenomenon well-understood that rainbow trout, when they're swimming upstream at mating season, they kind of hang out behind the rocks. And looks like, I mean, that's tiring work swimming upstream. They're hanging out behind the rocks. Maybe there's something energetically interesting there. So they tried to recreate that. They put, in this water tunnel, a rock basically, a cylinder that had the same sort of vortex stream.

Lex Fridman 10:40 Mm-hmm.

Russ Tedrake 10:40 The eddies coming off the back of the rock that you would see in a stream. And they put a real fish behind this and watched how it swims. And the amazing thing is that if you watch from above, what the fish swims when it's not behind a rock, it has a particular gait. You can identify the fish the same way you look at a human looking th- walking down the street. You sort of have a sense of how a human walks. A fish has a characteristic gait. You put that fish behind the rock, its gait changes, and what y- they saw was that it was actually resonating and kind of s- surfing between the vortices.

Lex Fridman 11:15 Yeah.

Russ Tedrake 11:16 Now here was the experiment that really was the clincher 'cause there was still, it wasn't clear how much of that was mechanics of the fish, how much of that is control, the brain. So the clincher experiment, and maybe one of my favorites to date, although there are many good experiments, they took, they, this was now a dead fish, um, they took a dead fish-

Lex Fridman 11:40 (laughs)

Russ Tedrake 11:40 They put a string that went, that tied the mouth of the fish to the rock, so it couldn't go back and get caught in the grates. Uh, and then they asked, "What would that dead fish do when it was hanging out behind the rock?" And so what you'd expect, it sort of flopped around like a dead fish in the, in the vortex wake, until something sort of amazing happens. And this video is worth putting in.

Lex Fridman 12:01 (laughs) Yeah.

Russ Tedrake 12:02 Right?

Lex Fridman 12:02 What happens?

Russ Tedrake 12:03 Uh, the dead fish basically starts swimming upstream, right? It's completely dead.... no brain, no motors, no control, but it somehow the mechanics of the fish resonate with the vortex street and it starts swimming upstream. It's one of the best examples ever.

Lex Fridman 12:20 What, who do you credit for that too? Is that just evolution constantly just figuring out by killing a lot of generations of animals, uh, like, the most efficient motion? Is that, uh, or maybe the physics of our world completely, like... It's like if evolution applied not only to animals, but just the entirety of it somehow drives the efficiency. Like, nature likes efficiency. I don't know if that question even makes any sense.

Russ Tedrake 12:50 I understand the question. That's re- I mean, (laughs) do they co-evolve? Um-

Lex Fridman 12:54 Yeah, somehow co- yeah, like, I, I don't know if an environment can evolve but, um...

Russ Tedrake 13:00 I mean, there are experiments that people do, careful experiments that show that, um, animals can adapt to unusual situations and recover efficiency, so there seems like at least in one direction, I think there is reason to believe that the animal's motor system, um, and probably its mechanics, uh, adapt in order to be more efficient. But efficiency isn't the only goal, of course. Um, sometimes it's too easy to think about only efficiency, but we have to do a lot of other things first, not get eaten, and then all other things being equal, try to save energy.

Lex Fridman 13:34 By the way, let's, uh, draw a distinction between control and mechanics. Like, how, how can, how would you define each?

Russ Tedrake 13:40 Yeah. I mean, I, I think part of the point is that we shouldn't draw a line-

Lex Fridman 13:45 Right.

Russ Tedrake 13:45 ... as, as, as clearly as we tend to. But the, you know, on a robot, we have motors and we have the links of the robot, let's say. If the motors are turned off, the robot has some passive dynamics, okay? Gravity does the work, you can put springs, I would call that mechanics, right? If we have springs and dampers, which our muscles are springs and dampers and tendons, um, but then you have something that's doing active work, putting energy in, which are your motors on the robot. The controller's job is to send commands to the motor that add new energy into the system, right? So, the mechanics and control interplay, somewhere the divide is around, you know, did you decide to, uh, send some commands to your motor or did you just leave the motors off and let them do their work?

Lex Fridman 14:30 Uh, would you say, is most, most of nature on the, uh, dynamic side or the control side? So like, if you look at biological systems or f- you know, we're living in a pandemic now, like, do you think a virus is a... (laughs) do you think is a dynamic system or, um, or is there a lot of control, intelligence?

Russ Tedrake 14:54 I think it's both, but I think we maybe have underestimated how important the dynamics are, right? Um, I mean, even our bodies, the mechanics of our bodies, certainly with exercise, they evolve, but... Uh, so I actually, I lost a finger in early 2000s, and, um, it's my fifth metacarpal. And it turns out you use that a lot, um, in ways you don't expect, when you're opening jars. Even when I'm just walking around, if I bump it on something, there's a bone there that was used to taking, uh, contact, my fourth metacarpal wasn't used to taking contact, it used to hurt. It still does a little bit, but actually my bone has remodeled, right?

Lex Fridman 15:33 Mm-hmm.

Russ Tedrake 15:34 The, the, over the la- over a couple years, the geometry, the mechanics of that bone changed to address the new circumstances. So the idea that somehow it's only our brain that's adapting or evolving is, is not right.

Lex Fridman 15:50 Maybe sticking on evolution for a bit, 'cause, um, it's tended to create some interesting things, uh, bipedal walking, do you, um... Why the heck did evolution give us... I think we're, are we the only mammals that walk on two feet?

Russ Tedrake 16:06 No. I mean, there's a bunch of animals that do it a bit.

Lex Fridman 16:10 A bit.

Russ Tedrake 16:10 There's a, there, I think we are the most successful bipeds.

Lex Fridman 16:13 I, I think some, uh, I think I read somewhere that, um, the reason the, you know, evolution made us walk on two feet is because, uh, there's an advantage to being able to carry food back to the tribe or something like that, so like you can carry, it's kind of this communal, uh, cooperative thing, so like to carry stuff back to, um, to a place of shelter and so on to share with others. Um, do you understand at all the value of, uh, walking on two feet from a, both a robotics and a human perspective?

Russ Tedrake 16:49 Yeah. There are some great books written about evolution of walking, evolution of the human body. I think it's easy though to make bad evolutionary arguments.

Lex Fridman 17:00 Sure. (laughs)

Russ Tedrake 17:02 Um...

Lex Fridman 17:02 Most of them are probably bad, but what, what else can we do?

Russ Tedrake 17:06 I mean, I think, um, a lot of what dominated our evolution probably was not the things that worked well sort of in the steady state, um, uh, you know, when things are, when things are good, but, but, uh, for instance, people talk about what we should eat now because our ancestors were meat eaters or, or whatever.

Lex Fridman 17:28 Oh, yeah, I love that. Yeah.

Russ Tedrake 17:30 But probably, you know, the reason that one pre-, uh, pre-Homo sapiens species versus another survived was not because of whether they ate well, uh, when there was lots of food, but when the Ice Age came, you know, probably one of them happened to be in the wrong place, one of them happened to, uh, forage a food that was okay even, even when the, the glaciers came or something like that. I mean, the point is-

Lex Fridman 17:58 There's a million variables that contributed and we can't... And our, actually the amount of information we're working with in telling these stories, uh, these evolutionary stories is, um, is very little. So just, yeah, just like you said, it, it seems like if we, if we study history, it seems like history turns on like these little events...... that, um, that otherwise would seem meaningless. But in, in a grand, like when you, in retrospect, were, um, turning points.

Russ Tedrake 18:27 Absolutely.

Lex Fridman 18:28 And that, that's probably how, like somebody got hit in the head with a rock because somebody slept with the wrong person back in the (laughs) K days, and somebody get, uh, get angry and that turned, uh, you know, warring tribes, com- combined with the environment, all those millions of things, and the meat-eating. Which I get a lot of criticism because I, I don't know, um, I don't know what your dietary processes are like, but these days I been eating only meat, which is, um ... There's, there's a large community of people who say, yeah, probably make evolutionary arguments and say, "You do a great job." There's probably an even larger community of people, including my mom, who says it's, uh, deeply unhealthy, it's wrong, but I just feel good doing it. But, you're right, I, uh, these evolutionary arguments can be flawed. But is there anything interesting to pull out for, um, for walking?

Russ Tedrake 19:17 There's a great book, by the way, um, well a series of books by Nicholas Taylor about Fooled by Randomness and Black Swan. Um, highly recommend them, but yeah, they make the point nicely that probably it was a few random events that, yes, maybe it was, uh, someone getting hit by a rock as you say.

Lex Fridman 19:38 Uh, that said, do you think, uh, I don't know how to ask this question or how to talk about this, but there's something elegant and beautiful about moving on two feet. Obviously biased because I'm human, but, uh, from a robotics perspective too, you work with robots on two feet, is it, um, is it at all useful to build robots that are on two feet as opposed to four? Is there something useful about it?

Russ Tedrake 20:02 I think the most, um, I mean the reason I spent a long time working on wa- bipedal walking was because it was hard, and it was, um, it challenged control theory in ways that I thought were important. Um, I wouldn't have ever tried to convince you that, um, you should start a company around bipeds or something like this. There are people that make pretty compelling arguments, right? I think the most compelling one is that the world is built for the human form, and if you want a robot to work in the world we have today, then, you know, having a human form is a pretty good way to go. Um, there are, there are places that a biped can go that would be hard for, uh, other form factors to go, even natural places. But, um, you know, at some point in the long run, we'll be building our environments for our robots probably, and so maybe that argument falls aside.

Lex Fridman 20:56 So, you famously run barefoot. Uh, do you still run barefoot?

Russ Tedrake 21:02 I still run barefoot.

Lex Fridman 21:03 That's so awesome.

Russ Tedrake 21:04 Much to my wife's chagrin. (laughs)

Lex Fridman 21:07 (laughs) Do you wanna make an evolutionary argument for why running barefoot (laughs) is advantageous? Um, (laughs) , what have you learned about, um, human and robot movement in general from running barefoot? Human or robot, and/or?

Russ Tedrake 21:23 Well, you know, it happened the other way, right? So, I was studying walking robots and, uh, I was ... There's a great conference called the Dynamic Walking Conference, uh, where it brings together both the biomechanics community and the walking robots community. And so I had been going to this for years and hearing talks by people who study barefoot running and other, the mechanics of running. So I, I did eventually read Born to Run. Most people read Born to Run and then-

Lex Fridman 21:51 First then.

Russ Tedrake 21:52 ... read ... Right? Um, the other thing I had going for me is actually that I, um, I wasn't, I wasn't a runner before, and I learned to run after I had learned about barefoot running, or I mean, started running longer distances. So I didn't have to unlearn. And I'm definitely, um, I'm a big fan of it for me, but I'm not gonna ... I tend to not try to convince other people. There's people who run beautifully with shoes on (laughs) , and that's good. Um, but here's why it makes sense for me. Um, it's all about the long-term game, right? So I, I think it's just too easy to run 10 miles, feel pretty good, and then you get home at night and you realize, uh, "My knees hurt. I did something wrong." Right? Um, if you take your shoes off, then if, if you hit hard with your foot at all, um, then it hurts. (laughs) You don't like run 10 miles, uh, and then, and then realize you've done something, some damage. You have immediate feedback-

Lex Fridman 22:52 Mm-hmm.

Russ Tedrake 22:52 ... telling you that you've done something that's, that's maybe suboptimal, and you change your gait. I mean, it's even subconscious. If I, right now, having run many miles barefoot, if I put a shoe on, my gait changes in a way that I think is not as good. Um, so, so it, it makes me land softer, and I think my, my goals for running are to do it for as long as I can into old age, um, not to win any races. And so for me, this is a, uh, you know, a way to protect myself.

Lex Fridman 23:23 Yeah, I think, um, first of all, I've tried running barefoot (laughs) many years ago, uh, probably the other way, just, just, just, uh, reading Born to Run, but just to understand because I felt like I couldn't put in the miles that I wanted to, uh, and it feels like, um ... Running for me, and I think for a lot of people, was one of those activities that we do often and we never really try to learn to do correctly. (laughs) Like, it's funny, there's so many activities we do every day, like brushing our teeth, right? Uh, I think a lot of us, at least me, probably have never deeply studied how to properly brush my teeth, right? Or wash, as now with the pandemic, or how to properly wash our hands. We do it every day, but we haven't really studied, like, "Am I doing this correctly?" But running felt like one of those things that it was absurd not to study how to do correctly 'cause it's the source of so much pain and suffering. Like, I hate running, but I do it (laughs) - (laughs)

Russ Tedrake 24:25 ... I do it because I hate it, but it- I- I feel good afterwards. But I think it feels like you need to learn how to do it properly. So, that's where barefoot running came in, and then I quickly realized that my gait was completely wrong. I was taking huge, like, steps and, um, landing hard on the heel, all those elements. And so, yeah, from that I actually learned to take really small steps. Look, uh, I already forgot the number, but I feel like it was 180 a minute or something like that. And I- I remember I was, uh, I actually just took songs that are 180 beats per minute and then, like, tried to run at that beat, uh, just to teach myself. It took, it took a long time and I feel like, uh, after a while you, you learn to, to run, you adjust it properly without going all the way to barefoot. But I feel like barefoot is the legit way to do it. I, I mean, I think a lot of people would be really curious about it. Can you, if they're interested in trying, what would you ... how would you recommend they start or try or explore? Slowly. (laughs)

Lex Fridman 25:32 (laughs)

Russ Tedrake 25:32 That's the biggest thing people do is they are excellent runners and they're used to running long distances or running fast and they take their shoes off and they hurt themselves instantly trying to do something that they were used to doing. I, I think I lucked out in the sense that I, I couldn't run very far when I first started trying. And I run with minimal shoes too. I mean, I will, uh, you know, bring along a pair of actually, like, aqua socks or something like this I can just slip on, or running sandals. I've tried all of them.

Lex Fridman 26:00 What's the difference between a minimal shoe and nothing at all? What's, like feeling-wise, what does it feel like?

Russ Tedrake 26:07 There is a ... I mean, I, I noticed my gait changing, right? So, um, I mean, your, your foot has as many muscles and sensors as your hand does, right?

Lex Fridman 26:17 Sensors. Ooh, okay.

Russ Tedrake 26:20 And, uh, we do amazing things with our hands, and we stick our foot in a big solid shoe, right? So there's, I think, you know, when you're barefoot, you're l- you're just giving yourself more proprioception, and that's why you're more aware of some of the gait flaws and stuff like this. Now, you have less protection too. So, um-

Lex Fridman 26:40 Rocks and stuff. That-

Russ Tedrake 26:42 I mean, yeah. So, so I think people are, who are afraid of barefoot running, they're worried about getting cuts or getting, stepping on rocks. Um, first of all, even if that was a concern, I think those are all, like, uh, very short term. You know, if I get a scratch or something, it'll heal in a week. If I blow out my knees, I'm done running forever. So I will trade the short term for the long term anytime. But even then, (laughs) you know, uh, and this, again, to my wife's chagrin, um, your feet get tough, right? And, uh, uh-

Lex Fridman 27:08 A callous. Okay.

Russ Tedrake 27:09 Yeah. I can run over almost anything now. (laughs)

Lex Fridman 27:13 (laughs) I mean, what, um ... Maybe c- can you talk about is there hint, like is there tips or tricks that you have, uh, suggestions about ... Like if I wanted to try it.

Russ Tedrake 27:26 You know, there, there is a good book actually. Uh, there's probably more good books since I read them. But, uh, Ken Bob, Barefoot Ken Bob Saxton.

Lex Fridman 27:35 Mm-hmm. (laughs)

Russ Tedrake 27:37 Um, he's an interesting guy, but I think his book captured, uh, the right way to describe running, barefoot running to somebody better than any other I've seen.

Lex Fridman 27:48 So, you run pretty good distances and you bike. And is, is there, um, you know, if we talk about bucket list items, i- is there something crazy on your bucket list athletically that you hope to do one day?

Russ Tedrake 28:04 I mean, my commute is already a little crazy. Um-

Lex Fridman 28:07 What are we talking about here? What, what, uh, what distance are we talking about?

Russ Tedrake 28:11 Well, I live about 12 miles from MIT, but you can find lots of different ways to get there. So, I mean, I've run there for a long, many years. I've biked there. Um-

Lex Fridman 28:20 Both ways?

Russ Tedrake 28:21 Yeah. But normally I would try to run in and then bike home, bike in, run home.

Lex Fridman 28:26 But you have run there and back before-

Russ Tedrake 28:28 Sure.

Lex Fridman 28:28 ... barefoot?

Russ Tedrake 28:29 Yeah. Uh, yeah, or with minimal shoes or whatever that-

Lex Fridman 28:32 12, 12 times two?

Russ Tedrake 28:34 Yeah.

Lex Fridman 28:34 Okay.

Russ Tedrake 28:35 It's, it became kind of a game of how can I get to work. I've rollerbladed, I've done all kinds of weird stuff. But, um, my favorite one these days is I've been taking the Charles River to work. So, um, I can put in, uh, the rowboat not so far from my house, but the Charles River takes a long way to get to MIT so, um, I can spend a long time getting there. And it's, you know, it's not about ... I don't know. It's just about, uh ... I've had people ask me, "How can you justify taking that time?" Uh, but for me it's just a magical time to think, to compress, decompress. Um, you know, especially I'll, I'll wake up, do a lot of work in the morning and then I kinda have to just let that settle before I s- I'm ready for all my meetings. And then on the way home it's a great time to lo- sort of let that settle.

Lex Fridman 29:24 So you, you lead a, like a h- a large group of people. I mean, you're ... Is there days where you're like, "Oh shit, I gotta get to work in an hour." (laughs) Like, uh, I mean, uh, is, is there, is there a tension there where ... And, like, if we look at the grand scheme of things, just like you said, long term, that meeting probably doesn't matter. Like you can always say, "I'll just, I'll run and let the meeting happen how it happens." Like what, uh, how do you, uh, that zen, how do you, uh, what do you do with that tension between the real world saying, "Urgently, you need to be there. This is important. Everything is melting down. How are we gonna fix this robot? There's this, uh, critical meeting." And then there's this de- the zen beauty of just running, the simplicity of it, you alone with nature. What do you do with that?

Russ Tedrake 30:22 I would say I'm not a fast runner particularly.

Lex Fridman 30:24 Mm-hmm.

Russ Tedrake 30:25 Probably my fastest splits ever was when-... I had to get to daycare on time because they were gonna charge me, uh, you know, some, some dollar per minute that I was late.

Lex Fridman 30:33 (laughs)

Russ Tedrake 30:33 Uh, I, I've run some fast splits to daycare.

Lex Fridman 30:36 (laughs)

Russ Tedrake 30:37 Uh, but that, those times are past now. Um, I think work, uh, you can find a work/life balance in that way. I think you just have to. Um, I, I think I am better at work because I take time to think on the way in. So, I plan my day around it, um, and I, I, I rarely feel that those are really in, at odds.

Lex Fridman 31:00 So, what ... The bucket list item. If we're talking 12 times two, we're approaching a marathon, uh, what, uh ... Have you run an ultra-marathon before? Do you do races? Is there ... What's, uh-

Russ Tedrake 31:17 Never to win. (laughs) If-

Lex Fridman 31:19 Not to ... (laughs)

Russ Tedrake 31:20 Uh, I'm not gonna, like, take a dinghy across the Atlantic or something, if that's what you want, but, uh, uh-

Lex Fridman 31:26 (laughs)

Russ Tedrake 31:26 But if someone does and wants to write a book, I would totally read it 'cause I, I'm a sucker for that kinda thing.

Lex Fridman 31:30 Yeah.

Russ Tedrake 31:31 No, I, I do have some fun things that I will try.

Lex Fridman 31:33 Right.

Russ Tedrake 31:33 You know, I like to, when I travel, I almost always bike to the Logan Airport and fold up a little folding bike and, and then-

Lex Fridman 31:39 Right.

Russ Tedrake 31:39 ... take it with me and bike to wherever I'm going, and I've, it's taken me, or I've take, I'll take a stand-up paddleboard these days on, on the airplane, and then I'll try to paddle around where I'm going or whatever. And I've done some crazy things, but, um-

Lex Fridman 31:50 But not for the ... You know, I've, I, I now talk ... I don't know if you know who David Goggins is, by any chance?

Russ Tedrake 31:57 Not well, but yeah.

Lex Fridman 31:58 But I, I talk to him now every day, so he's the person who made me, uh, do this stupid challenge. So, he (laughs) , he's insane, and he does things for the purpose, in, in the best kind of way. He does things y- like, f- for the explicit purpose of suffering. Like, he picks the thing that, like, whatever he thinks he can do, he does more. Uh, so is that ... Do you have that thing in you? Or are you, uh-

Russ Tedrake 32:27 I think it's become the opposite.

Lex Fridman 32:28 (laughs)

Russ Tedrake 32:29 It's, uh-

Lex Fridman 32:30 So you're like that dynamical system that, uh, the walker, the efficient, uh-

Russ Tedrake 32:34 Yeah, it's, uh, leave no pain. Right? Uh, uh, g- you should end feeling better than you started.

Lex Fridman 32:40 Okay.

Russ Tedrake 32:41 But, um, it's mostly, I think, and COVID has tested this 'cause I've lost my commute, I think I'm perfectly happy walking around, uh, around town with my wife and, uh, kids if they could get them to go, uh, a- and it's more about just getting outside and getting away from the keyboard for some time just to let things compress.

Lex Fridman 33:02 Let's go into robotics a little bit. What, to you, is the most beautiful idea in robotics? Whether we're talking about control, or whether we're talking about optimization and the math side of things, or the engineering side of things, or the philosophical side of things.

Russ Tedrake 33:18 Mm-hmm. I think I've been lucky to experience something that not so many roboticists have experienced, which is to hang out with some really amazing control theorists (laughs) and, um, the clarity of thought that some of the more mathematical control theory can bring to even very complex, messy-looking problems is really, it really had a big impact on me, and, and, uh, I had a day even ac-, just a couple weeks ago where I'd spent the day on a Zoom robotics conference having great conversations with lots of people, felt really good, um, about the ideas that were flowing and, and the like. And then I had a, you know, late afternoon meeting with, uh, one of my favorite control theorists, and, um, and we went from these, from these abstract discussions about maybes and what-ifs and, and what a great idea, to these super precise statements about systems that aren't that much f- more simple or, or abstract than the ones I care about deeply. And the contrast of that is, um ... I don't know, it, it really gets me. I think people underestimate, um, maybe the power of clear thinking. Uh, uh, and so for instance, deep learning is amazing. Um, I use it heavily in our work. I think it's changed the world, unquestionable. It makes it easy to get things to work without thinking as critically about it. So I think one of the challenges as an educator is to think about, um, how do we make sure people get a taste of the more rigorous thinking that I think goes along, uh, with, with some different approaches.

Lex Fridman 35:24 Yeah, so that's really interesting. So, understanding, like, the fundamentals, the first principles of the, of the, the, the pro-, the problem, or in this case, it's mechanics. It's, like, how a thing moves, how a thing behaves, like, all the forces involved, like, really getting a deep understanding of that. I mean, it, the, from physics, the first principle thing come from physics, and here it's literally physics. Uh, yeah, and this applies, in deep learning, this applies to, um, not just, I mean, it applies so cleanly in, in robotics, but it also applies to just in any data set. I find this true, I mean, driving as well. There's a lot of folks in the t-, uh, that work on autonomous vehicles that don't study driving (laughs) like, deeply. I, I, I might be coming a little bit from the psychology side, but, um, I remember I spent a ridiculous number of hours o- at lunch-... a, (laughs) a- a- this, like, lawn chair, and I would sit somewhere, um, somewhere on MIT's campus, there's a few interesting intersections, and we'd just watch people cross. So we were studying, um, uh, pedestrian behavior, and I felt like... And to record a lot of video, to try it, and then there's the computer vision extracts their movement, how they move their head, and so on. But, like, every time, I felt like I didn't understand enough. I, I just, I, I felt like I wasn't understanding what, how are people signaling to each other? What are they thinking? How cognizant are they of their fear of death? (laughs) Like, what are we, like, what's the game th- what's the underlying game theory here? What are, what are the, the, the incentives? And then I finally found a livestream, uh, of an intersection that's, like, high def that I just, I would watch so I wouldn't have to sit out there. But that's interesting, so, like, I feel, I get-

Russ Tedrake 37:23 But that's tough. That's a tough example because, I mean, the learning-

Lex Fridman 37:27 Humans are involved. (laughs)

Russ Tedrake 37:28 Not just because human, but I, I think, um, the learning mantra is the basically the statistics of the data will tell me things I need to know, right? And, uh, you know, for the example you gave of all the nuances of, um, you know, eye contact, or hand gestures, or whatever that are happening for these subtle interactions between pedestrians and traffic, right? Maybe the data will tell this- tell- tell that story. Uh, maybe even, I, I, uh, one level more meta (laughs) than, than what you're saying. Um, for a particular problem, I think it might be the case that data should tell us the story. But I think there's a rigorous thinking that is just an essential skill for a mathematician or an engineer that, um, I just don't wanna lose it.

Lex Fridman 38:18 Yes.

Russ Tedrake 38:18 There are, there are certainly super rigorous, um, rigorous control, or sorry, um, machine learning people. I just think deep learning makes it so easy-

Lex Fridman 38:27 Right.

Russ Tedrake 38:28 ... to do some things that, um, our next generation are, um, not immediately rewarded for going through some of the more rigorous approaches, and then I wonder where that takes us. I j- well, I'm, I'm actually optimistic about it. I just want to, um, do my part to try to steer that rigorous thinking.

Lex Fridman 38:48 So, there's, like, two questions I wanna ask. D- do you have sort of a, a good example of rigorous thinking where it's easy to get lazy and not do the rigorous thinking? And the other question I have is, like, do you have advice of, um, how to practice rigorous thinking in, um, in, uh, in all the, uh, computer science disciplines that we've mentioned?

Russ Tedrake 39:16 Yeah, I mean, uh, there are times where problems that can be solved with well-known mature methods, um, could also be solved with, uh, with a deep learning approach. And, um, there's an argument that you must use learning even for the parts we already think we know because if the human has touched it, then you've, you've, d- you've biased the system and you've su- suddenly put a bottleneck in there that, uh, is your own mental model. But something like inverting a matrix, you know, I, I think we know how to do that pretty well, even if it's a pretty big matrix, and we understand that pretty well. And you could train a deep network to do it, but you shouldn't, probably.

Lex Fridman 39:57 So r- so in that sense, rigorous thinking is, uh, understanding the, the scope and the limitations of the meth- of the methods that we have. Like, how to use the tools of mathematics properly?

Russ Tedrake 40:10 Yeah. I think, you know, uh, i- taking a class on analysis is all I'm still, or sort of-

Lex Fridman 40:16 Right.

Russ Tedrake 40:16 ... arguing is to take, take a chance to stop and, and force yourself to think rigorously about even, you know, the rational numbers or something, you know, it doesn't have to be the end-all problem. But that exercise of clear thinking, I think, uh, goes a long way, and I just wanna make sure we, we keep preaching it.

Lex Fridman 40:35 We don't lose it.

Russ Tedrake 40:36 Yeah.

Lex Fridman 40:36 But do you think, uh, when you're doing, like, rigorous thinking or, like, maybe, uh, trying to write down equations or sort of explicitly, like, formally describe a system, do you think we naturally simplify things too much? Is that a danger you run into? Like, uh, in order to be able to understand something about the system mathematically, we, uh, make it too much of a toy example?

Russ Tedrake 41:01 But I think that's the good stuff, right? Um...

Lex Fridman 41:04 That's how you understand the fundamentals?

Russ Tedrake 41:07 I think so. I think, uh, uh, maybe even that's a key to intelligence or something. But, I mean, if, okay, what if Newton and Galileo had deep learning?

Lex Fridman 41:15 (laughs)

Russ Tedrake 41:15 And, and, and they had done a bunch of experiments and they told the world, "Here's your weights-

Lex Fridman 41:21 Yes.

Russ Tedrake 41:21 ... of your neural network. I've, we've solved the problem."

Lex Fridman 41:23 Yeah.

Russ Tedrake 41:24 You know, where would we be today? I don't, I don't think we'd be as far as we, as we are. There's something to be said about having a, the simplest explanation for a phenomenon. So I don't doubt that we can train neural networks to predict even physical, um, you know, uh, F equals MA type equations. But, um, I maybe I, I want another Newton to come along 'cause I think there's more to do in terms of coming up with the simple models for more complicated tasks.

Lex Fridman 41:59 Yeah. Uh, let's not offend the AI systems from 50 years from now that are listening to this that are probably better at, might be better coming up with F equals MA equations themselves. So-

Russ Tedrake 42:13 Oh, sorry. I actually think, um, learning is probably a route to, to achieving this. Um, but the representation matters, right? And I think, uh, having a function that takes my inputs to outputs, uh, that is arbitrarily complex may not be the end goal. I think, um, there's still-... you know, the most simple or parsimonious explanation for the data. Um, simple doesn't mean low-dimensional. That's one thing I think that we've... a lesson that we've learned. So, well, you know, a standard way to do, um, model reduction or system identification and controls is to, the typical formulation is that you try to find the minimal state dimension realization of a system that hits some error bounds or something like that. And that's maybe not... I think we're- we're l- we're learning that that was... that the me- state dimension is not the right metric.

Lex Fridman 43:06 Of complexity.

Russ Tedrake 43:06 Of complexity. But for me, I think a lot about contact, m- the mechanics of contact, if a robot hand is picking up an object or something. And when I write down the equations of motion for that, they are... they look incredibly complex, not because... Um, actually, not so much because of the dynamics of the hand when it's moving, but it's just the interactions and when they turn on and off, right? So having a high-dimensional, you know, but s- simple description of what's happening out here is fine, but if, when I actually start touching, if I write down a different dynamical system for every polygon on my robot hand and every polygon on the object, whether it's in contact or not, with all the combinatorics that explodes there, then that's too complex. So I need to somehow summarize that with a more intuitive physics way of- of thinking. And, uh, yeah, I'm very optimistic that machine learning will get us there.

Lex Fridman 44:05 First of all, I mean, I'll probably do it in the introduction, but you're, uh, one of the great robotics people at MIT. You're a professor at MIT. You've... Teach in a lot of amazing courses. You run a large group. Uh, and you have a important history for MIT, I think, as, uh, being a part of the DARPA Robotics Challenge. Can you maybe first say what is the DARPA Robotics Challenge and then tell your story around it, your journey with it?

Russ Tedrake 44:36 Yeah, sure. Um, so the DARPA Robotics Challenge, it came on the tails of the DARPA Grand Challenge and DARPA Urban Challenge, which were the challenges that brought us... um, put a spotlight on self-driving cars. Um, Gill Pratt was at DARPA and pitched a new challenge that involved disaster response. Um, it didn't explicitly require humanoids, although humanoids came into the picture. This happened shortly after the Fukushima disaster in Japan, and our challenge was motivated roughly by that because that was a case where if we had had robots that were ready to be sent in, there's a chance that we could have, um, averted a disaster. And certainly, after the, um... in the disaster response, there were times where we'd love- we would have loved to have sent robots in. So in practice, what we ended up with was a- a grand challenge, uh, DARPA Robotics Challenge, um, where Boston Dynamics was, uh, was to make humanoid robots. People like me and the- the amazing team at MIT, um, were competing first in a simulation challenge to try to be one of the ones that wins the right to work on one of the, uh, the Boston Dynamics humanoids in order to compete in the- the final challenge, which was a physical challenge.

Lex Fridman 46:08 And at that point, it was already... So it was decided as humanoid robots early on?

Russ Tedrake 46:13 So there were... There were two tracks. There w- you could enter as a hardware team where you brought your own robot, or you could enter through the virtual robotics challenge as a software team that would try to win the right to use one of the Boston Dynamics robots.

Lex Fridman 46:25 Which are called Atlas-

Russ Tedrake 46:27 Atlas.

Lex Fridman 46:27 ... humanoid robots.

Russ Tedrake 46:28 Yeah.

Lex Fridman 46:28 Yeah.

Russ Tedrake 46:28 It was a 400-pound marvel, but, uh, you know, pretty big, scary-looking robot.

Lex Fridman 46:35 Expensive, too-

Russ Tedrake 46:36 Expensive, yeah.

Lex Fridman 46:36 ... at least at the time. Yeah.

Russ Tedrake 46:37 Mm-hmm.

Lex Fridman 46:38 Okay, so, um, I mean, how did you feel at the prospect of this kind of challenge? I mean, it seems... You know, autonomous vehicles, yeah, I guess that sounds hard, but, uh, not really from a robotics perspective. It's like, "Didn't they do it in the '80s?" is the kind of feeling I would have. (laughs) Uh, like when you first look at the problem, it's on wheels, but, like, humanoid robots, that sounds really hard. Uh, so what... Like, how, what- what are the, psychologically speaking, what were you feeling? Excited? Scared? Why the heck did you get yourself involved in this kind of messy challenge?

Russ Tedrake 47:19 We didn't really know for sure what we were signing up for.

Lex Fridman 47:23 Of course.

Russ Tedrake 47:24 Um, in the sense that you could have something that, as it was described in the call for participation, um, that could have put a huge emphasis on the dynamics of walking and not falling down and walking over rough terrain or the same description, 'cause the robot had to go into this disaster area and turn valves and- and, uh, pick up a drill or cut a hole through a wall. It had to do some interesting things. The challenge could have really highlighted perception and autonomous planning, or it- it ended up that, you know, locomoting over a complex, uh, terrain played a pretty big role in the competition. So, um-

Lex Fridman 48:05 And the degree of autonomy wasn't clear.

Russ Tedrake 48:08 The degree of autonomy was always a central part of the discussion. So, um, what wasn't clear was how we would be able... how far we'd be able to get with it. So the idea was always, uh, that you want semi-autonomy, that you want the robot to have enough compute that you can have a degraded network link to a human. And so the same way you... we had degraded networks at a- at a many natural disasters, you'd send your robot in.... you'd be able to get a few bits back and forth, but you don't get to have enough, potentially, to fully, uh, operate the robot, every joint of the robot. So, and then the question was, and the gamesmanship of the organizers was to figure out what we're capable of, push us as far as we could, so that, um, it would differentiate the teams that put more autonomy on the robot and had a few clicks and just said, "Go there, do this. Go there, do this," versus someone who's picking every footstep or something like that.

Lex Fridman 49:05 So, what were some, uh, memories, painful, triumphant from the experience? Like, what was that journey? Maybe c- if you can dig in a little deeper, maybe even on the technical side, on the team side, that, that whole process of, um, from the e- early idea stages to actually competing to find the stage.

Russ Tedrake 49:28 I mean, this was a defining experience for me. I, I, it came at the right time for me in my career. I had gotten tenure before I was due a sabbatical (laughs) and most people do something, you know, relaxing and restorative for a sabbatical.

Lex Fridman 49:41 So, you got tenure before the, the, before this?

Russ Tedrake 49:44 Yeah. Yeah, yeah. It was a good time for me. I had, I had, we had a bunch of algorithms that we were very happy with. We wanted to see how far we could push them, and this was a chance to really test our mettle, to do more proper software engineering. Um, the team, we all just worked our butts off. We, you know, were in that lab almost all the time. Um, okay, so there, I mean, there were some, of course, high highs and low lows throughout that, uh, anytime you're, you know, not sleeping and devoting your life to a 400-pound humanoid. Um, I r- I remember actually one funny moment where we're all super tired, and so Atlas had to walk across cinder blocks. That was one of the obstacles. And I remember Atlas was powered down and hanging limp, you know, on the, on its harness. And the, the humans were there, like, laying, you know, picking up and laying the brick down so that the robot could walk over it, and I thought, "What is wrong with this?" (laughs) You know?

Lex Fridman 50:38 (laughs)

Russ Tedrake 50:40 We got, we've got a robot just watching us do all the manual labor so that it can take its little, um-

Lex Fridman 50:44 (laughs)

Russ Tedrake 50:45 ... stroll across the terrain.

Lex Fridman 50:46 Yeah.

Russ Tedrake 50:47 But, uh...

Lex Fridman 50:48 (laughs)

Russ Tedrake 50:49 I mean, even the v- even the virtual robotics challenge was, was super nerve-wracking and dramatic. I, I remember, um, so, so we were using Gazebo as a simulator, uh, on the cloud, and there was all these interesting challenges. I think, um, the investment that, that OSRF's, um, FC, whatever they were called at that time, Brian Gerkey's team at Open Source Robotics, um, they were pushing on the capabilities of Gazebo in order to scale it to the complexity of these challenges. So, um, you know, up to the virtual competition, so the virtual competition was you will sign on at a certain time, and we'll have a network connection to another machine on the cloud that is running the simulator of your robot. And your controller will run on this comp- this controller, this computer, and, and the physics will run on the other, and you have to, to c- connect. Now, um, the physics, they wanted it to run at real-time rates because there was an element of human interaction, um, and humans could... If you do on a telly op, it works way better if it's at frame rate.

Lex Fridman 51:56 Oh, cool. Mm-hmm.

Russ Tedrake 51:57 But it was very hard to simulate these comp- these complex scenes at real-time rate. So, right up to, like, days before the competition, the, the simulator wasn't quite s- uh, at real-time rate. And that was great for me because my controller was solving a big, pretty big optimization problem-

Lex Fridman 52:15 (laughs)

Russ Tedrake 52:16 ... and it wasn't quite at real-time rate. So I was fine, I was keeping up with the simulator. We were both running at about 0.7, and I remember getting this email, and by the way, the perception folks on our team hated that I, that... They knew that if my c- controller was too slow, the robot was gonna fall down and, and, you know, no matter how good their perception system was, if I can't make my controller fast en- Anyways, we get this email, like, three days before the virtual competition. Oh, you know, it's for all the marbles. We're gonna either get a humanoid robot or we're not.

Lex Fridman 52:43 Mm-hmm.

Russ Tedrake 52:44 And we get an email saying, "Good news, we made the robot, the, the simulator faster. It's now one point..." And, uh-

Lex Fridman 52:51 (laughs)

Russ Tedrake 52:51 ... I, we were, I was just like, "Oh, man, what are we gonna do here?" So-

Lex Fridman 52:55 Yeah.

Russ Tedrake 52:55 ... um, that came in late at night for me. Um-

Lex Fridman 52:59 A few days ahead.

Russ Tedrake 53:00 ... a few days ahead. I went over, there was, it happened that Frank Permenter, who's a, uh, very, very sharp, he was a st- he was a student at the time, um, working on optimization, was, he was still in lab. Uh, "Frank, we need to make-"

Lex Fridman 53:15 (laughs)

Russ Tedrake 53:15 "... this quadratic programming solver faster. Not like a little faster, it's actually, you know." Um, and we wrote a new solver for that QP together that night. (laughs)

Lex Fridman 53:28 A new solver?

Russ Tedrake 53:28 It was terrifying.

Lex Fridman 53:29 So, there's a really hard optimization problem that you're constantly solving. You didn't make the optimization problem simpler? You, you wrote a new solver? (laughs)

Russ Tedrake 53:38 So, um, I mean, your observation is almost spot on. What, what we did was what everybody, I mean, people know how to do this, but we had not yet done this idea of warm starting. So, we are solving a big optimization problem at every time step, but if you're running fast enough, the optimization problem you're solving on the last time step is pretty similar to the optimization you're gonna solve on the next.

Lex Fridman 53:59 Got it.

Russ Tedrake 54:00 We had, of course, had told our commercial solver to use warm starting, but even the interface to that commercial solver was causing us these delays. So, what we did was we basically wrote, we called it Fast QP at the time, um, we wrote a very lightweight, very fast layer, which would basically check if nearby solutions to the quadratic program were, which were very easily checked, uh, could stabilize the robot. And if they couldn't, we would fall back to the solver.

Lex Fridman 54:30 You couldn't really test this well, right?

Russ Tedrake 54:32 Um-

Lex Fridman 54:33 Or they... (laughs)

Russ Tedrake 54:33 ... I mean, uh, so-... we always knew that if we fell back to, if we, it got to the point where if for some reason things slowed down and we fell back to the original solver, the robot would actually literally fall down. Um, so it was, it was a harrowing sort of edge we were, ledge we were sort of on. But I mean, it, it actually, like, the r- the 400-pound humanoid could come crashing to the ground if you, if you, if your solver's not fast enough. But, you know, the, we have lots of good experiences. I- I'll-

Lex Fridman 55:02 So can I ask you a, a, a weird question I, I get, um, about idea of hard work? So, um, actually, people, like students of yours that I've interacted with, and just, and robotics people in general, but they, uh, they have moments, at moments have worked harder than, uh, most people I know in terms of if you look at different disciplines of how hard people work. But they're also, like, the happiest. (laughs) Like, just, like, I don't know. Um, it's the same thing with, like, running, people that push themselves to, like, the limit, they all also seem to be, like, the most, like, full of life somehow. Uh, and I get often criticized, like, "Y- you're not getting enough sleep. What are you doing to your body?" Blah, blah, blah. Like, this kinda stuff. And I usually just kinda respond like, "Um, I'm doing what I love, I'm passionate about. I, I love it. I feel like it's, um, it's invigorating." I actually think, I don't think the lack of sleep is what hurts you. I think what hurts you is, uh, stress and lack of doing things that you're passionate about. But in this world, yeah. I mean, can you comment about, uh, why the heck robotics people are, are (laughs) , uh, willing to push themselves to that degree? Is there value in that? And, uh, why are they so happy?

Russ Tedrake 56:30 I think, I think you got it right. I mean, I think the causality is not that we work hard, and I think other disciplines work very hard too. But it's, I don't think it's that we work hard and therefore we are happy. I think we found something that we're truly passionate about. It makes us very happy. And then, we get a little involved with it and spend a lotta time on it. Um, what a luxury to have something that you wanna spend all your time on, right?

Lex Fridman 56:59 We could talk about this for many hours, but maybe if we could pick, is there something on the technical side on the approach that you took that's interesting that turned out to be a terrible failure or a, a success that you carry into your work today about all the different i- ideas that were involved in, um, making, whether in the, in the simulation or in the, in the real world, making a semi-autonomous system work?

Russ Tedrake 57:26 I mean, it really did teach me something fundamental about what it's gonna take to get robustness out of a system of this complexity. I would say the DARPA Challenge really, um, was foundational in my thinking. I think the autonomous driving community thinks about this. I think lots of people thinking about safety-critical systems that might have machine learning in the loop are thinking about these questions. For me, the DARPA Challenge was the moment where I realized, uh, you know, we've spent every waking minute running this robot, and a- again, the, in, for the physical competition, days before the competition, we saw the robot fall down in a way it had never fallen down before. I thought, you know, "H- how could we have found that? You know, w- we only have one robot. It's running almost all the time. We just didn't have enough hours in the day to test that robot. Something has to change," right? And, and then, I think that, I mean, I would say that the team that won, uh, was, uh, was from KAIST, was the team that had two robots and was able to do not only incredible engineering, just absolutely top-rate engineering, but also they were able to test at a rate and, um, discipline that we didn't keep up with.

Lex Fridman 58:39 What does testing look like? What are we talking about here? Like, what's, what's a, a loop of test, like, a, from start to finish, what is a loop of testing look like?

Russ Tedrake 58:48 Yeah, I mean, I think there's a whole philosophy to testing. There's the unit tests, and you can do that on a hardware, you can do that in a, a small piece of code. You write one function, you should write a test that, that checks that function's input/outputs. You should also write a integration test at the other extreme of, of running the whole system together, you know, where that, that try to turn on all of the different functions that you've, you think are correct. It's much harder to write the specifications for a system-level test, especially if that system is as complicated as a humanoid robot. But the philosophy is sort of the same. On the real robot, um, it's, it's no different, but on a real robot, it's impossible to run the same experiment twice. So if you, if you see a failure, you hope you caught something in the logs that tell you what happened, but you'd probably never be able to run exactly that experiment again. And right now, I think our philosophy is just basically Monte Carlo, uh, estimation, is just, uh, run as many experiments as we can, maybe try to set up the environment to, uh, to make the things we are worried about happen as often as possible. But really, we're relying on somewhat random search in order to test. Maybe that's all we'll ever be able to, but I think, uh, you know, 'cause there's an argument that the things that'll get you are the, the, the things that are really nuanced in the world and would be very hard to, for instance, put back in a simulation.

Lex Fridman 1:00:16 Yeah, the, I guess the edge cases. What, what was the, the hardest thing? Like, uh, so you said walking over rough terrain, like, the, just taking footsteps. I mean, people, there's, it's so dramatic and painful in a certain kinda way to watch these videos from the DRC of robots falling.

Russ Tedrake 1:00:37 ... yep.

Lex Fridman 1:00:38 It's just so heartbreaking. I don't know. Maybe it's because, uh, for me at least, we anthropomorphize the robot. Um, of course, it's-

Russ Tedrake 1:00:46 Every one of those-

Lex Fridman 1:00:47 ... also funny for some reason. Like-

Russ Tedrake 1:00:48 Oh, yeah.

Lex Fridman 1:00:48 ... humans falling is funny, uh, for, I don't, it's some dark reason, I'm not sure why it, it is so, but it's also like tragic and painful. And, uh, so s- speaking of which, I mean, what, what made the robots fall and fail, uh, in, in your view?

Russ Tedrake 1:01:05 So, I can tell you exactly what happened on a... We, I contributed one of those. Our team contributed to one of those spectacular falls. Um, every one of those falls, the, has a complicated story. I mean, at one time, the power effectively went out on the robot, because it had been sitting at the door waiting for the green light to be able to proceed and its batteries are, you know, and therefore, it just fell backwards and smashed its head aground, against the ground, and it was hilarious. But it wasn't because of bad software. Right? Um, but for ours... So the hardest part of the challenge, the hardest task, in my view, was getting it out of the Polaris. It was actually relatively easy to drive the Polaris. We had-

Lex Fridman 1:01:43 Can you s- can you tell the story, sorry to interrupt-

Russ Tedrake 1:01:45 No, of course.

Lex Fridman 1:01:45 ... but the story of the car (laughs) wha- people should watch this video. I mean, the, the, the thing you've come up with is just brilliant. But, uh, anyway, sorry. What's, um...

Russ Tedrake 1:01:55 Yeah. We, we kind of joke. We call it the big robot, little car problem, because, um, somehow the race organizers decided to give us a 400-pound humanoid, and then they also provided the vehicle, which was a little Polaris. And the robot didn't really fit in the car, so you couldn't drive the car with your feet under the steering column. You, we actually had to straddle the, the main column of the, uh, and have basically one foot in the passenger seat, one foot (laughs) in, in the driver's seat, and then drive with our left hand. But the hard part was we had to then park the car, get out of the car, um, it didn't have a door, that was okay, but it's just, um, getting up from crouched, from sitting, when you're in this very constrained environment.

Lex Fridman 1:02:41 Uh, first of all, I remember after watching those videos, I was much more cognizant of how, how hard is it, it is for me to get in and out of the car, and out of the car especially.

Russ Tedrake 1:02:51 Yeah.

Lex Fridman 1:02:51 Like, it's actually a really difficult control problem.

Russ Tedrake 1:02:54 Yeah.

Lex Fridman 1:02:55 And I, I'm very cognizant of it when I'm like injured for whatever reason.

Russ Tedrake 1:02:59 No, that's really hard.

Lex Fridman 1:03:00 Yeah.

Russ Tedrake 1:03:01 Uh-

Lex Fridman 1:03:01 So, so how did you (laughs) how did you approach this problem?

Russ Tedrake 1:03:03 Well, so, so we had, uh, you know, you think of NASA's operations and they have these checklists, you know, pre-launch checklists and the like. We weren't far off from that. We had this big checklist and on the first day of the competition, we were running down our checklist, and one of the things we had to do, we had to turn off the controller, the piece of software that was running that would drive the left foot of the robot in order to accelerate on the gas, and then we turned on our balancing controller. And the nerves, jitters of the first day of the competition, someone forgot to check that box and turn that controller off. So, um, we used a lot of motion planning, uh, to figure out a, uh, a sort of configuration of the robot that we could get up and, and over. We relied heavily on our balancing controller and (laughs) and basically there were, when the robot was in one of its most precarious, you know, sort of configurations, trying to sneak its big leg out of the, out of the side, the other controller that we thought it was still driving told its left foot to go like this. And, uh, and that wasn't good. Um...

Lex Fridman 1:04:10 (laughs)

Russ Tedrake 1:04:11 ... but, but it turned disastrous-

Lex Fridman 1:04:12 I-

Russ Tedrake 1:04:12 ... for us because, um, what happened was a little bit of push here... Actually, if you, we have videos of us, you know, running into the robot with a 10-foot pole and it kind of will recover, but this is a case where there's no space to recover. So, a lot of our secondary balancing mechanisms about, like take a step to recover, they were all disabled because we were in the car, you know, there was no place to step. So, we were relying on just our lowest level reflexes. And even then, I think just hitting the foot on the seat, or on the, on the floor, we probably could have recovered from it. But the thing that was bad that happened was when we did that and we jostled a little bit, the tailbone of our robot had, was only a little off the seat, it hit the seat, and the other foot came off the ground just a little bit.

Lex Fridman 1:04:58 Ah.

Russ Tedrake 1:04:58 And nothing in our plans had ever told us what to do if your butt's on the seat and your feet are in the air.

Lex Fridman 1:05:05 Feet in the air.

Russ Tedrake 1:05:06 And then-

Lex Fridman 1:05:06 (laughs)

Russ Tedrake 1:05:07 ... the thing is once you get off the script, things-

Lex Fridman 1:05:10 Yeah.

Russ Tedrake 1:05:10 ... can go very wrong, because even our state estimation, our system that was trying to collect all the data from the sensors and understand what's happening with the robot, it didn't know about this situation. So, it was predicting things that were just wrong. And then we did a violent shake and fell off and our, uh, face first down onto the robot.

Lex Fridman 1:05:29 But like into the destination?

Russ Tedrake 1:05:32 Into... That's true. We fell in and we got our point for egress-

Lex Fridman 1:05:35 We got the point. (laughs)

Russ Tedrake 1:05:36 ... um-

Lex Fridman 1:05:36 But, so-

Russ Tedrake 1:05:37 ... that's the point.

Lex Fridman 1:05:37 ... is there any hope for... That's interesting. Is there any hope f- for Atlas to be able to do something when it's just on its butt and feet in the air?

Russ Tedrake 1:05:46 Absolutely.

Lex Fridman 1:05:47 So you can... What do...

Russ Tedrake 1:05:48 No. So that's, um, that is one of the big challenges, and I think it's still true, um, uh, you know, Boston Dynamics and, and, um, AnyMal and there's this incredible work on, on legged robots happening around the world. Uh, most of them still are, are very good at the case where you're making contact with the world at your feet and they have typically point feet relatively. Their balls on their feet, for instance. If that, if those robots get in a situation where the elbow hits the wall or something like this, that's a pretty different situation. Now, they have layers of mechanisms that will make... I think the, the more mature solutions have s- have ways in which the controller won't do stupid things. But a human, for instance, is able to leverage incidental contact in order to accomplish a goal. In fact, I might, if you push me, I might actually put my hand out and make a new, brand new contact.... the feet of the robot are doing this on, on quadrupeds, but we mostly, in robotics, are afraid of contact on the rest of our body, which is crazy. There's this whole field of motion planning, collision-free motion planning, and we write very complex algorithms so that the robot can dance around and make sure it doesn't touch the world. (laughs) Um-

Lex Fridman 1:07:05 So people are just afraid of contact, 'cause contact is seen as a difficult...

Russ Tedrake 1:07:09 It's still a difficult control problem and sensing problem.

Lex Fridman 1:07:15 Now you're a serious person. (laughs) Um, I'm a little bit of an idiot and I'm going to ask you some dumb questions. Uh, so I do, uh, I do martial arts, uh, so like jiujitsu. There's cr- I've wrestled my whole life. So let me, (laughs) let me ask the question, um, you know, like whenever people learn that I do any kind of AI or, like I mention robots and things like that, they say, "When are we gonna have robots that, um, you know, that can win in a wrestling match or in a fight against a human?" Uh, so we just mentioned sitting on your butt, feet in the air. That's a common position in jiujitsu when you're on the ground, you're, uh, when you're, you're an down opponent. Um, like what, how difficult do you think is the problem and when will we have a robot that can defeat a human in a wrestling match?

Russ Tedrake 1:08:07 Mm-hmm.

Lex Fridman 1:08:08 And we're talking about a lot, like if, I don't know if you're familiar with wrestling, but es- essentially, um-

Russ Tedrake 1:08:15 Not very.

Lex Fridman 1:08:16 ... it's basically the art of contact. It's like, it's, 'cause you're, you're, you're, you're picking contact points and then using, like, leverage, like to, uh, off-balance, to, to trick people. Like, you, uh, make them feel like you're doing one thing, and then they s- they change their balance, and then you, uh, switch what you're doing, and then it results in a throw or whatever. So, like, it's basically the art of multiple contacts. So-

Russ Tedrake 1:08:48 Awesome. That's a nice description of it. So there's also an opponent in there, right? So, so if-

Lex Fridman 1:08:54 Very dynamic.

Russ Tedrake 1:08:55 ... right? If you are wrestling a human and, um, are in a game theoretic situation with a human, that's still hard. Uh, but just to speak to the, you know, quickly reasoning about contact part of it, for instance.

Lex Fridman 1:09:11 Yeah, maybe even throwing the game theory out of it.

Russ Tedrake 1:09:13 Right.

Lex Fridman 1:09:13 Almost like a, yeah, almost like a non-dynamic opponent.

Russ Tedrake 1:09:17 Right. There's reasons to be optimistic, but I think our best understanding of those problems are still pretty hard. Um, I have been increasingly focused on manipulation, partly where that's a case where the contact has to be much more rich. Um, and there are some really impressive examples of, of deep learning policies, controllers that, um, that can appear to do good things through contact. We've even got new examples of, of, you know, deep learning models of predicting what's gonna happen to objects as they go through contact. But I think the challenge you just offered there still eludes us, right? The, the ability to make a decision based on those models quickly, um, you know, I have to think though, it's hard for humans too when you get that complicated. I think probably you had maybe a slow motion version of where you learned the basic skills and you've probably gotten better at it and, and, um, there's, there's much more subtlety, but it might still be hard to actually, you know, really on the fly, take a, you know, model of your humanoid and figure out how to, how to plan the optimal sequence. That might be a problem we never solve.

Lex Fridman 1:10:36 Well, the repeti- the, I mean, one of the most amazing things to me about the, we, we, we can talk about martial arts, uh, we could also talk about dancing. It doesn't really matter. To human, um, I think it's the most interesting study of contact. It's not even the dynamic element of it. It's the, like, when you get good at it, it's so effortless. Like, I can just, I'm very cognizant of the entirety of the learning process being essentially, like, learning how to move my body in a way that I could throw very large weights around effortlessly. Like, and, and I can feel the learning. Like, I'm a huge believer in drilling of techniques. And you can just, like, feel your, I don't, you're not feeling, you're, you're feeling, um, sorry, you're learning it intellectually a little bit, but a lot of it is the body learning it somehow, uh, like instinctually. And wha- whatever that learning is, that's really... I'm not even sure if that's, um, equivalent to a, like, a deep learning, learning a controller. I think it's something more, it feels like there's a lot of distributed learning going on. (laughs)

Russ Tedrake 1:11:51 Yeah, I think there's hierarchy and composition-

Lex Fridman 1:11:54 Yeah.

Russ Tedrake 1:11:54 ... um, probably in the systems that we don't capture very well yet. Uh, you, you have layers of control systems. You have reflexes at the bottom layer, and you have a, you know, a system that's capable of, you know, planning a vacation to some distant country, which is probably, you probably don't have a controller or a policy for every possible destination you'll ever pick. Right? Um, but there's something magical in the in between and how do you go from these low-level feedback loops to something that feels like a pretty complex set of outcomes. You know, my guess is, I think, I think there's evidence that you can plan at some of these levels, right? So, uh, Josh Tenenbaum just showed it, uh, in his talk the other day. He's got a game he likes to talk about it. I think he calls it the pick three game or something.... where he puts a bunch of clutter down in front of, uh, a person and he says, "Okay. Pick three objects." And it might be a telephone or a shoe or a Kleenex box or whatever. Um, and apparently, you pick three items and then you pick ... He says, "Okay. Pick the first one up with your right hand, the second one up with your left hand. Now, using those objects, those ... now, as tools, pick up the third object." Right? So that's down at the level of, of physics and mechanics and contact mechanics that, um, that I think we do learning ... or we do have policies for, we do control for, almost feedback, but somehow we're able to still ... I mean, I've never picked up a telephone with a shoe and a water bottle before, and somehow ... And it takes me a little longer to do that the first time, but most of the time, we can sort of figure that out. So, yeah, I think the, the amazing thing is this ability to be flexible with our models-

Lex Fridman 1:13:44 Right.

Russ Tedrake 1:13:45 ... um, plan when we need to, use our well-oiled controllers when we don't, when, when we're in familiar territory. Um, having models. I think the oth- the other thing you just said was something about I think your awareness of what's happening is even changing as you, as you get ... as you improve your expertise, right? So, maybe you have a very approximate model of the mechanics to begin with and as you gain expertise, you get a more refined version of that model. You're aware of, of muscles or, or, um, balance components that you were just- weren't even aware of before. So how do you scaffold that?

Lex Fridman 1:14:21 Yeah. Plus the fear of injury, the ambition of goals, of, uh, excelling, and, uh, fear of mortality. Let's see, what else is in there as motivations? Um, uh, a over inflated ego in the beginning, uh, th- like, and then a crash of confidence in the middle. All those (laughs) seem to be essential for the learning process.

Russ Tedrake 1:14:46 And also- ... And if all that's good, then you're probably optimizing energy efficiency.

Lex Fridman 1:14:50 Yeah. Right. (laughs) So we have to get that right. Uh, so, um, you know, th- there was this idea that you would have, uh, robots play soccer better than human players by 2050. That was the goal. Uh, world ... Basically, uh, was the goal to beat world champion team-

Russ Tedrake 1:15:10 Yeah.

Lex Fridman 1:15:10 ... to become a World Cup ... Be like a World Cup-

Russ Tedrake 1:15:12 Right.

Lex Fridman 1:15:12 ... level team? So, are we gonna see that first or, um, a robot ... If you're familiar, there's a organization called UFC for mixed martial arts. Are we gonna see a World Cup championship soccer team that are robots or a UFC champion mixed martial artist, uh, that's-

Russ Tedrake 1:15:32 Ah.

Lex Fridman 1:15:32 ... a robot?

Russ Tedrake 1:15:33 I mean, it's very hard to, to say one thing is harder than ... One ... Some problem is harder than the other. What probably matters, i- is, um-

Lex Fridman 1:15:42 (laughs)

Russ Tedrake 1:15:42 ... who, who, who started the organization that, that ... I mean, I think RoboCup has a pretty serious following and there are ... is a history now of people playing that game, learning about that game, building robots to play that game, building increasingly more human robots. It's got momentum. And so if you want to, uh, to have mixed martial arts compete, you better start your-

Lex Fridman 1:16:02 Then RoboCup

Narrator 1:16:02 Will be the robotics competition.

Russ Tedrake 1:16:02 ... start your organization now, right? Um, I think almost independent of which problem is technically harder, 'cause they're both hard and they're both different.

Lex Fridman 1:16:11 That's a good point. I mean, those videos are just hilarious. The ... Like, uh, especially the humanoid robots trying to, um, (laughs) trying to play soccer. I mean, they're kinda terrible right now. Uh, the-

Russ Tedrake 1:16:23 I mean, I guess there is robo-sumo wrestling. There's like the Robo-1 competitions, um, where they do have these robots go on a table and basically fight. So, maybe I'm wrong. Maybe-

Lex Fridman 1:16:33 First of all, do you have a year in mind for, uh, RoboCup, just from a robotics perspective? Seems like a super exciting possibility that, um ... Like, in the physical space. This is what's interesting. Uh, I think the world is captivated. I think it's really exciting. It's, um ... It inspires just a huge number of people when a machine beats a human at a game that humans are really damn good at. So you're talking about chess and Go, but that's in the, in the world of, uh, digital. I don't think machines have beat humans at a game in the physical space yet, but that would be just, uh-

Russ Tedrake 1:17:17 You have to make the rules very carefully, right?

Lex Fridman 1:17:20 (laughs)

Russ Tedrake 1:17:20 I mean if, if a- ... If Atlas kick me in the shins, I'm down and, uh, uh, you know, and, and game over. So there's, uh ... You know, it's, it's very subtle on-

Lex Fridman 1:17:30 Yeah. I think the f-

Russ Tedrake 1:17:30 ... what's fair.

Lex Fridman 1:17:31 I think the fighting one is a weird one, yeah, 'cause, uh, you're, you're talking about a machine that's much stronger than you. But, yeah, in terms of soccer, basketball, all those kinds of things.

Russ Tedrake 1:17:39 Even soccer, right? I mean, as soon as there's contact-

Lex Fridman 1:17:42 (laughs)

Russ Tedrake 1:17:42 ... or whatever. And there's, there are some things that the robot will do better. I th- I think if you really set yourself up to try to see could robots win the game of soccer as the rules were written, the right thing for the robot to do is to play very differently than a human would play. It's the ... You're not gonna get, you know, the perfect soccer player robot. You're gonna get something that exploits the rules, exploits its super actuators, its super low bandwidth, um, you know, feedback loops or whatever, and it's gonna play the game differently than you want it to play.

Lex Fridman 1:18:16 Yeah.

Russ Tedrake 1:18:17 Um, and it ... I bet there's ways ... There's ... I bet there's loopholes. Right? We saw that in the, (laughs) in the DARPA Challenge, uh, uh, that, that it's very hard to write a set of rules that someone can't find, uh, a way to exploit.

Lex Fridman 1:18:32 Let me ask another ridiculous question. Uh, pro- ... I think this, this might be the last ridiculous question, but, uh-

Russ Tedrake 1:18:38 I doubt it. (laughs)

Lex Fridman 1:18:39 (laughs) I, I aspired to ask as many, uh, ridiculous questions of, uh, of a br- brilliant MIT professor. Okay. Uh, I don't know if you've seen the Black Mirror?

Russ Tedrake 1:18:53 It's funny, I- I- I never watched the episode. I know when it happened though because I gave a talk to some MIT faculty one day on a- a unassuming, you know, Monday or whatever. I was telling them about the state of robotics, and I showed some video of, from Boston Dynamics of the quadruped Spot at the time, uh, it was their early version of Spot, and there was a look of horror that went across the room.

Lex Fridman 1:19:18 (laughs)

Russ Tedrake 1:19:19 And I said, "What? You know, I've- I've- I've shown videos like this a lot of times. What happened?" And it turns out that this video had gone, uh, this Black Mirror episode had changed the way people watched, um, yeah, the videos I was putting out.

Lex Fridman 1:19:33 The way they see these kinds of robots, so I- I talked to so many people who are just terrified because of that episode probably of these kinds of robots. They, I almost wanna say they almost kinda like enjoy being terrified. I don't even know what it is about human psychology, that kind of imagine doomsday, the destruction of the universe or our society, and kinda like enjoy being afraid. Um, I don't wanna simplify it, but it feels like they talk about it so often it almost ... They, th- there does seem to be an addictive quality to it. Um, I talked to a guy at, so this, a guy named Joe Rogan who's kinda the flag bearer for being terrified of these robots. (laughs) Uh, do you have a ... Two questions. One, do you have an understanding of why people are afraid of robots? And the second question is, uh, in Black Mirror, just to tell you the episode, I don't even remember it that much anymore, but these robots, I think they can shoot like a pellet or something. They basically have, it's- it's basically a Spot with a gun. And, um, how far are we away from, um, having robots that go rogue like that, you know, basically Spot that goes rogue for some reason and somehow finds a gun. (laughs)

Russ Tedrake 1:20:51 Right. So, I mean, I'm, I'm not a psychologist.

Lex Fridman 1:20:55 (laughs)

Russ Tedrake 1:20:55 Um, I think, I don't know exactly why, uh, people react the way they do. Um, I think, I think we have to be careful about the way robots influence our society and the like. I think that's something, that's a responsibility that roboticists need to embrace. Um, I don't think robots are gonna come after me with a kitchen knife or a pellet gun right away, and, I mean, they, if they were programmed in such a way, but I used to joke with Atlas that, um, all I had to do was run for five minutes and its battery would run out. (laughs)

Lex Fridman 1:21:30 (laughs)

Russ Tedrake 1:21:30 But, uh, actually they've got a very big battery in there by the end, so it was over an hour.

Lex Fridman 1:21:35 (laughs)

Russ Tedrake 1:21:37 Um, I think the fear is cu- a bit cultural though 'cause I, I, I mean, you notice that, like, I think in my age in the US we grew up watching Terminator, right? If I had grown up at the same time in Japan, I probably would've been watching Astro Boy, and there's a very different reaction to robots, uh, in different countries, right? So, I don't know if it's a human innate fear of metal marvels or if it's, um, um, something that we've done to ourselves with our sci-fi. (laughs) Uh ...

Lex Fridman 1:22:09 Yeah, the stories we tell ourselves through, uh, through movies, through just, uh, through popular media. But if- if I were to tell, you know, if- if you were my therapist and I said, "I'm really terrified that, uh, we're going to have these robots, uh, very soon that will hurt us," um, like how do you approach m- making me feel better? Um, like why shouldn't people be afraid? I mean, there's a, I think there's a video that went viral recently. Everything, everything with Spot in- in Boston Dynamics goes viral in general, but usually it's like really cool stuff, like they're doing flips and stuff or like sad stuff with b- s- Atlas being hit with a broomstick or something like that. But, uh, there's a video where I think, um, w- one of the new production Spot robots, which are awesome, it was like patrolling somewhere in like, in some country and like people immediately were like saying like, "This is like the dystopian future, like the surveillance state." For some reason like you could just have a camera, like, well, something about Spot being able to walk on four feet would like really terrified people. So like what, what do you say to those people? I think there is a legitimate fear there because so much of our future is uncertain. Um, but at the same time, technically speaking, it seems like we're not there yet. So what do you say?

Russ Tedrake 1:23:44 I mean, I think technology is, um, complicated. It can be used in many ways. I think there are purely software, um, attacks that somebody could use to do great damage. Maybe they have already. Um, you know, I think, uh, wheeled robots could be used in bad ways too.

Lex Fridman 1:24:08 Drones.

Russ Tedrake 1:24:10 Drones, right. Um, I don't think that ... Let's see. I don't want to be, um, building technology just because I'm compelled to build technology and I don't think about it. But I would consider myself a technological optimist I guess, um, in the sense that I think we should continue to create and evolve and our world will change. Um, and if w- we're, we will introduce new challenges, we'll screw something up maybe, but I think also we'll invent ourselves out of those challenges and life will go on.

Lex Fridman 1:24:49 So, it's interesting 'cause you, you didn't mention like this is technically too hard.

Russ Tedrake 1:24:53 Mm-hmm.I don't think robots are... I think people attribute a robot that looks like an animal as maybe having a level of self-awareness or- or consciousness, or something that they don't have yet, right? So, it's not, I think our