The following is a conversation with Russ Tedrick, 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 get into a lot of topics

in this conversation from his time leading MIT's Dabra 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 underactory dynamical systems like the human body,

this fact makes Russ one of the most fascinating people I know.

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here's my conversation with Russ Tedrick. What is the most beautiful motion of animal or robot

that you've ever seen? 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. How does it work? Well, okay, the simplest model,

it's kind of like a slinky, it's like an elaborate slinky. One of the simplest models we

used to think about it is actually a rimless wheel. So imagine taking a 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 it's foot, it's 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 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 called our compass gate walker. That can still, you give it a little push,

starts falling down a ramp. It looks a little bit more like walking. At least it's a biped.

But what Steve and Andy and Ted 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. Give it a little push and that looked like a stroll

through the park. How do you design something like that? I mean, is that art or science?

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 made a beautiful robot,

but then the finesse, because 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. At that time, the best walking robot in the world was Hondo's Asamo,

absolutely marvel of modern engineering. This was in the 97 when they first released,

it sort of announced P2 and then it went through. It was Asamo by then in 2004.

It looks like this very cautious walking. You're walking on hot coals or something like that.

I think it gets a bad rap. Asamo is a beautiful machine. It does walk with its knees bent.

Our Atlas walking had its knees bent, but actually, Asamo was pretty fantastic,

but it wasn't energy efficient. Neither was Atlas when we worked on Atlas. None of our robots

that have been that complicated have been very energy efficient, but 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. This was a very sort of beautiful example that

you don't have to do that. You can just let physics do most of the work 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.

Yeah, but okay, so that sounds very Zen, but I can also imagine how many

like failed versions they had to go through. I would say it's probably, would you say it's in

the thousands that they've had to have the system fall down before they figured out?

I don't know if it's thousands, but it's a lot. It takes some patience. There's no question.

So in that sense, control might help a little bit.

I think everybody, even at the time, said that the answer is to do that with control,

but it was just pointing out that maybe the way we're doing control right now

isn't the way we should. Got it. So what about on the animal side, the ones that figured out

how to move efficiently? Is there anything you find inspiring or beautiful in the movement of

any particular animal? I do have a favorite example. Okay. So it sort of goes with the

passive walking idea. So is there, how energy efficient are animals? Okay, there's a great

series of experiments by George Lauder at Harvard and Mike Tranifillo at MIT.

They were studying fish swimming in a water tunnel. Okay. And one of these, the type of fish

they were studying were these rainbow trout because there was a phenomenon well understood

that rainbow trout, when they're swimming upstream at mating season, they kind of hang out behind

the rocks. And it 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 street, 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 walking down the street. You

sort of have a sense of how a human walks. The fish has a characteristic gait.

You put that fish behind the rock, its gait changes. And what they saw was that it was

actually resonating and kind of surfing between the vortices. Now, here was the experiment that

really was the clincher because 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. This was now a dead fish.

They took a dead fish. They put a string that tied the mouse of the fish to the rock so it

couldn't go back and get caught in the grates. And then they asked, what would that dead fish do

when it was hanging up behind the rock? And so what you'd expect is sort of flopped around like a

dead fish in the vortex wake until something sort of amazing happens. And this video is worth putting

in. What happens? The dead fish basically starts swimming upstream. 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.

Who do you credit for that too? Is that just evolution constantly just

figuring out by killing a lot of generations of animals like the most efficient motion?

Is that or maybe the physics of our world completely like, it's like evolution applied

not only to animals, but just the entirety of it somehow drives to efficiency like nature likes

efficiency. I don't know if that question even makes any sense. I understand the question.

Do they co evolve? Yeah, somehow, yeah. I don't know if an environment can evolve, but

I mean, there are experiments that people do, careful experiments that show that

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 animals motor system

and probably its mechanics adapt in order to be more efficient, but efficiency isn't the only

goal of course. 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.

By the way, let's draw a distinction between control and mechanics. How would you define each?

Yeah. I think part of the point is that we shouldn't draw a line as clearly as we tend to,

but 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. Gravity does the work. You can put

springs, I would call that mechanics. If we have springs and dampers, which our muscles are springs

and dampers and tendons, but then you have something that's doing active work, putting

energy in which your motors on the robot, the controller's job is to send commands to the

motor that add new energy into the system. So the mechanics and control interplay somewhere

the divide is around, did you decide to send some commands to your motor or did you just

leave the motors off and let them do their work? Would you say is most of nature on the

dynamic side or the control side? If you look at biological systems,

we're living in a pandemic now. Do you think a virus is a dynamic system or is there a lot of

control, intelligence? I think it's both, but I think we maybe have underestimated how important

the dynamics are. I mean, even our bodies, the mechanics of our bodies, certainly with exercise,

they evolve, but so I actually, I lost a finger in early 2000s and it's my fifth

metacarpal. It turns out you use that a lot 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 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. Over a couple of 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 not right.

Right. Maybe sticking on evolution for a bit because it's tended to create some interesting

things. Bipedal walking, why the heck did evolution give us, I think we're, are we the only mammals

that walk on two feet? No. I mean, there's a bunch of animals that do it a bit. I think we are the

most successful bipeds. I think I read somewhere that the reason the, you know, evolution made us

walk on two feet is because 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 cooperative thing. So like

to carry stuff back to a place of shelter and so on to share with others. Do you understand

at all the value of walking on two feet from both the robotics and the human perspective?

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. Sure. Most of them

are probably bad, but what else can we do? I mean, I think a lot of what dominated our evolution

probably was not the things that worked well sort of in the steady state, you know, when things are,

when things are good, but, but for instance, people talk about what we should eat now because

our ancestors were meat eaters or, or whatever. Oh yeah, I love that. Yeah. But probably, you know,

the reason that one pre, prehomo sapien species versus another survived was not because of

whether they ate well 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

forage a food that was okay, even, even when the glaciers came or something like that. I mean,

there's a million variables that contributed and we can't, and our actually the amount of

information we're working with and telling these stories, these evolutionary stories is,

is very little. So yeah, just like you said, it seems like if we, if we study history, it seems

like history turns on like these little events that, that otherwise would seem meaningless, but

in the grant, like when you in retrospect, or turning points, absolutely. And then that's

probably how like somebody got hit in the head with a rock, because somebody slept with the wrong

person back in the cave days and somebody get angry and that turned, you know, warring tribes

combined with the environment, all those millions of things. And the meat eating,

which I get a lot of criticism because I don't know, I don't know what your dietary processes

are like, but these days I've been eating only meat, which is there's, there's a large community

people who say, yeah, probably make evolutionary arguments and say, you're doing a great job.

There's probably an even larger community of people, including my mom, who says it's a deeply

unhealthy, it's wrong, but I just feel good doing it. But you're right, these evolutionary

arguments can be flawed. But is there anything interesting to pull out for?

There's a great book, by the way, look, a series of books by Nicholas Taylor about fooled by randomness

and black swan, highly recommend them. But yeah, they make the point nicely that probably it was

a few random events that, yes, maybe it was someone getting hit by a rock, as you say.

That said, do you think, 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 from a robotics perspective to you work with robots on two feet,

is it all useful to build robots that are on two feet as opposed to four? Is there something useful

about it? The reason I spent a long time working on bipedal walking was because it was hard.

It challenged control theory in ways that I thought were important. I wouldn't have

ever tried to convince you that you should start a company around bipeds or something like this.

There are people that make pretty compelling arguments. 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 having a human form is a pretty good way to go. There are places that a biped

can go that would be hard for other form factors to go, even natural places. But at some point,

in the long run, we'll be building our environments for our robots probably. And so maybe that argument

falls aside. So you famously run barefoot. Do you still run barefoot? I still run barefoot.

That's so awesome. Much to my wife's chagrin. Do you want to make an evolutionary argument for

why running barefoot is advantageous? What have you learned about human and robot movement in

general from running barefoot? Human or robot and or? Well, you know, it happened the other way.

So I was studying walking robots and there's a great conference called the Dynamic Walking

Conference where it brings together both the biomechanics community and the walking robots

community. And so I've been going to this for years and hearing talks by people who study

barefoot running and other the mechanics of running. So I did eventually read Born to Run.

Most people read Born to Run in the first day, right? The other thing I had going for me is

actually that 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, I'm a big fan of it for me, but I tend to not try to convince other

people. There's people who run beautifully with shoes on and that's good. But here's why it makes

sense for me. It's all about the long term game, right? So I think it's just too easy to run 10

miles, feel pretty good. And then you get home at night and you realize my knees hurt. I did

something wrong, right? If you take your shoes off, then if you hit hard with your foot at all,

then it hurts. You don't like run 10 miles and then realize you've done something, some damage.

You have immediate feedback telling you that you've done something 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.

So it makes me land softer. And I think my goals for running are to do it for as long as I can

into old age, not to win any races. And so for me, this is a way to protect myself.

Yeah, I think, first of all, I've tried running barefoot many years ago, probably the other way,

just reading born to run. But just to understand, because I felt like I couldn't

put in the miles that I wanted to. And it feels like 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. Like it's funny, there's so many activities we do every day, like brushing our

teeth. I think a lot of us, at least me, probably have never deeply studied how to properly brush

my teeth or wash as now with a pandemic or how to properly wash our hands and 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 was absurd, not to study how to do correctly, because it's the source of so much

pain and suffering. Like I hate running, but I do it. I do it because I hate it, but 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 steps and landing hard on the heel, all those elements. And so yeah, from that I

actually learned to take really small steps. Look, I already forgot the number, but I feel like it was

180 a minute or something like that. And I remember I actually just took songs that are 180 beats per

minute and then tried to run at that beat. And just to teach myself, it took a long time. And I

feel like after a while you learn to run properly, you adjust it properly without going all the way

to barefoot. But I feel like barefoot is the legit way to do it. I mean, I think a lot of people

would be really curious about it. If they're interested in trying, how would you recommend

they start or try or explore? Slowly. 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 think

I lucked out in the sense that I couldn't run very far when I first started trying. And I run with

minimal shoes too. I mean, I will 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. What's the difference between

a minimal shoe and nothing at all? What's like feeling wise? What does it feel like?

There is it. I mean, I noticed my gait changing, right? So, I mean, your foot has as many muscles

and sensors as your hand does, right? Sensors. Ooh, okay. And we do amazing things with our hands.

And we stick our foot in a big salad shoe, right? So, there's, I think, when you're barefoot,

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.

Rocks and stuff. I mean, yeah. So, I think people who are afraid of barefoot running

are worried about getting cuts or getting stepping on rocks. First of all, even if that

was a concern, I think those are all very short term. 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, this, again, to my wife's chagrin,

your feet get tough, right? And. Cow's. Okay. Yeah. I can run over animals to anything now.

I mean, what, maybe, can you talk about, is there, like, is there tips or tricks that you have

suggestions about? Like, if I wanted to try it. You know, there is a good book, actually. There's

probably more good books since I read them. But Ken Bob, barefoot Ken Bob Saxton.

He's an interesting guy. But I think his book captures the right way to describe running,

barefoot running to somebody better than any other I've seen.

So, you run pretty good distances in your bike. And is there, you know, if we talk about bucket

list items, is there something crazy on your bucket list, athletically, that you hope to do one day?

I mean, my commute is already a little crazy. What are we talking about here?

What, what, what distance are we talking about? 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, a bike there. And ways? Yeah. But normally, I would try to run in and then bike

home, bike in, run home. But you have run there and back before? Sure. Barefoot? Yeah. Or with

minimal shoes or whatever that? 12 times two. Yeah. Okay. 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 my favorite one these days,

I've been taking the Charles River to work. So, I can put in a little robot, not so far from my

house. But the Charles River takes a long way to get the MIT. So, 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, I've had people

ask me, how can you justify taking that time? But for me, it's just a magical time to think,

to compress, decompress. You know, especially, I'll wake up, do a lot of work in the morning,

and then I kind of have to just let that settle before I'm ready for all my meetings. And then

on the way home, it's a great time to sort of let that settle. You lead a, like a large group of

people. I mean, is there days where you're like, oh, shit, I got to get to work in an hour?

I mean, is there, is there a tension there? 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, how do you,

that Zen, how do you, 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 we're going to fix this robot,

there's this critical meeting, and then there's this, the Zen beauty of just running the simplicity

of it, you along with nature. What do you do with that? I would say I'm not a fast runner,

particularly. Probably my fastest splits ever was when I had to get to daycare on time, because

they were going to charge me, you know, some, some dollar per minute that I was late. I've run some

fast splits to daycare. But that those times are past now. I think work, you can find a work life

balance in that way. I think you just have to. I think I am better at work because I take time to

think on the way in. So I plan my day around it. And I rarely feel that those are really in at odds.

So what the bucket list item, if we're talking 12 times two, or approaching a marathon,

what have you run an ultra marathon before? Do you do races? Is there what's

to win? I'm not going to like take a dingy across the Atlantic or something, if that's what you

want. But, but if someone does and wants to write a book, I would totally read it because I have a

sucker for that kind of thing. No, I do have some fun things that I will try. I like to,

when I travel, I almost always bike to Logan airport and fold up a little folding bike on

and then take it with me and bike to wherever I'm going. And it's taken me or I'll take a

stand up paddle board these days 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, but not for the, you know, I now talk,

I don't know if you know who David Goggins is by any chance, but I talk to him now every day. So

he's the person who made me do this stupid challenge. So he, he's insane. And he does things for the

purpose in the best kind of way. He does things like for the explicit purpose of suffering.

Like he picks the thing that like whatever he thinks he can do, he does more. So is that,

do you have that thing in you or you? I think it's become the opposite.

It's, uh,

So you're like that dynamical system that the walk or the efficient, uh,

Yeah, it's, uh, leave no pain, right? You should end feeling better than you started.

But, um, it's mostly, I think, and COVID has tested this because 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, and it's more about just getting outside and getting away from the keyboard for

some time just to let things compress. Let's go into robotics a little bit. What to use 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. 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 and,

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, uh, just a couple of weeks ago where I had 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 a, one of my favorite control theorists and,

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 are that much

much more simple or, or abstract than the ones I care about deeply. And the contrast of that is,

um, I don't know, it really gets me. I think people underestimate, um, maybe the power of

clear thinking. 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. Yeah. So that's really interesting. So

understanding like the fundamentals, the first principles of the, of the, the, the problem

more in this case is mechanics, like how a thing moves, how a thing behaves, like all the forces

involved, like really getting a deep understanding of that. I mean, from physics, the first principle

thing comes from physics. And here it's literally physics. Yeah. And this applies in deep learning.

This applies to, um, not just, I mean, it applies so cleanly 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 it

that work on autonomous vehicles that don't study driving like deeply. I might be coming

a little bit from the psychology side, but, um, I remember I spent a ridiculous number of hours

at lunch, uh, at this like lawn chair and I would sit somewhere, um, somewhere in MIT's

campus. There's a few interesting intersections and we just watch people cross. So we were studying,

um, pedestrian behavior. And I felt like as we record a lot of video to try 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 just, 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? Like what are we, like what's the game, what's the underlying game theory

here? What are, what are the, the incentives? And then I finally found a live stream 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, that's tough. That's a tough example because

I mean, the learning humans involved, not just because human, but 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 the, tell, tell that story.

I may be even, uh, uh, one level more meta 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 want to lose it. 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 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 just, well, I'm, I'm actually optimistic about it. I just want to, um, do my part to try to steer

that rigorous thinking. So there's like two questions I want to ask. 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, you know, in all the computer science disciplines

that we've mentioned? Yeah. I mean, 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, you've biased the system and you've

suddenly put a bottleneck in there that 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. So, so in that sense, rigorous thinking is, uh, understanding the,

the scope and limitations of the method of the methods that we have, like how to use the tools

of mathematics properly. Yeah. I think, you know, taking a class on analysis is all I'm sort of

arguing is to take, take a chance to stop and enforce 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 want to make

sure we, we keep preaching. We don't lose it. Yeah. 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. But I think that's the good stuff.

Right? Um, that's how you understand the fundamentals. I think so. I think maybe even

that's a key to intelligence or something, but I mean, okay, what if Newton and Galileo had deep

learning and, and, and they had done a bunch of experiments and they told the world, here's your

weights of your neural network. I've, we've solved the problem. I am. 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 want another Newton to come along because I think there's more to do in terms of

coming up with the simple models for more complicated tasks.

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.

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 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,

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 learning that, that was, the dimension, state dimension is not the right

metric. Of complexity. Of complexity. But for me, I think a lot about contact,

the mechanics of contact, the robot hand is picking up an object or something.

And when I write down the equations of motion for that, they're, 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 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 thinking. And, uh, yeah, I'm very optimistic

that machine learning will get us there. 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 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? Yeah, sure.

So the DARPA robotics challenge, it came on the tales 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, Gil Pratt was at DARPA and pitched a new challenge that involved disaster response.

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 disaster. And certainly after the, um,

in the disaster response, there were times we would love, we would have loved to have sent

robots in. So in practice, what we ended up with was, uh, a grand challenge, a DARPA robotics

challenge, um, where Boston Dynamics was, uh, was to make humanoid robots. People like me

and 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 final challenge, which was a physical challenge. And at that point,

it was already, so it was decided that it's humanoid robots early on.

So there were, there were two tracks that 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.

Which are called Atlas.

Atlas.

Humanoid robots.

Yeah. It was a 400 pound marvel, but a, you know, pretty big, scary looking robot.

Expensive too.

Expensive.

At least at the time.

Yeah.

Okay. So, uh, 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 it kind of feeling I would have, uh, like when you first look at the problem and sound

wheels, but like humanoid robots, that sounds really hard. Uh, so what, like, what, what are

you, psychologically speaking, what were you feeling excited, scared? Why the heck did you

get yourself involved in this kind of messy challenge?

We didn't really know for sure what we were signing up for, um, in the sense that you could

have had 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, because the robot had to go into this disaster area and turn valves and,

and pick up a drill, cut the hole through a wall. It had to do some interesting things.

The challenge could have really highlighted perception and autonomous planning,

or it ended up that, you know, locomoting over a complex, uh, terrain played a pretty big role

in the competition. So, um,

And the degree of autonomy wasn't clear.

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, uh, 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 in 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 or do this, go there,

do this versus someone who's picking every footstep or something like that.

So what were some, uh, memories, painful, triumphant from the experience? Like, what was that

journey? Maybe 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 early idea stages to actually competing.

I mean, this was a defining experience for me. It was, it came at the right time for me in my

career. I had gotten tenure before I was due a sabbatical and most people do something, you know,

relaxing and restorative for sabbatical. So you got tenure before the, the, before this? 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 metal to do more proper software engineering. Um, the team, we all just worked

our butts off. We, you know, we're 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 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 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? You know, we've got a

robot just watching us do all the manual labor so that it can take its little, um, scroll across

the tree. But, um, I mean, even the, even the virtual robotics challenge was, was super nerve

racking and dramatic. I remember, um, so, so we were using gazebo as a simulator on the cloud.

And there was all these interesting challenges. I think, um, the investment that, that OSRs, 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 computer,

this controller, this computer, and, and the physics will run on the other and you have to,

to 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 want to tell you, it works

way better if it's at frame rate. Oh, cool. But it was very hard to simulate these

complex, these complex scenes at real time rate. So right up to like days before the competition,

the, the simulator wasn't quite at real time rate. And that was great for me because my controller

was solving a big, pretty big optimization problem 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 they knew that if

my controller was too slow, the robot was going to fall down. And, and you know, no matter how

good their perception system was, if I can't make my controller fast, anyways, we get this email like

three days before the virtual competition, you know, it's for all the marbles, we're going to

either get a humanoid robot or we're not. And we get an email saying, good news, we made the robot,

does the simulator faster? It's now one point. And I, we're, I was just like, oh man, what are we

going to do here? So that came in late at night for me. A few days ahead. A few days ahead. I went

over, there was, it happened at Frank Permanter, who's a very, very sharp. He's a student at the

time working on optimization. He was still in lab. Frank, we need to make the quadratic programming

solver faster, not like a little faster. It's actually, you know, and we wrote a new solver for

that QP together that night. And he's terrifying. So there's a really hard optimization problem

that you're constantly solving. You didn't make the optimization problem simpler. You wrote a new

solver. So, I mean, your observation is almost spot on. What we did was what everybody, I mean,

people know how to do this, but we had not yet done this idea of worm 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 going

to solve with the next. We had course had told our commercial solver to use worm 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, 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, could stabilize the robot. And if they couldn't, we would fall back to the solver.

You couldn't really test this well, right?

Right. So we always knew that if we fell back, 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. So it was a harrowing sort of ledge we're sort of on. But I mean,

actually, like the 400 pound human could come crashing to the ground if your solver's not

fast enough. But, you know, we have lots of good experiences. So can I ask a weird question I get

about the idea of hard work? So actually people like students of yours that I've interacted with

and just in robotics people in general, but they have moments at moments of work harder than

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. Like just like, I don't know. It's the same thing with

like running people that push themselves to like the limit. They also seem to be like the most like

full of life somehow. And I get often criticized like, you're not getting enough sleep. What are

you doing to your body, blah, blah, blah, like this kind of stuff. And I usually just kind of

respond like I'm doing what I love. I'm passionate about it. I love it. I feel like it's it's

invigorating. I actually think I don't think the lack of sleep is what hurts you. I think what

hurts you is stress and lack of doing things that you're passionate about. But in this world,

yeah, I mean, can you comment about why the heck robotics people are

willing to push themselves to that degree? Is there value in that? And why are they so happy?

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 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 lot of time on it.

What a luxury to have something that you want to spend all your time on, right?

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 success that you carry into your work today

about all the different ideas that were involved in making, whether in the simulation

or in the real world, making the semi autonomous system work?

I mean, it really did teach me something fundamental about what it's going to take to

get robustness out of a system of this complexity. I would say the DARPA challenge really

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 we've spent every waking minute running this robot. And again, 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, how could we have found that? 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 then I think that, I mean, I would say that the team that won

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

discipline that we didn't keep up with. What does testing look like? What are we talking about here?

Like, what's a loop of tests? Like, from start to finish, what is a loop of testing?

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 small piece of code. You write one function,

you should write a test that checks that function's input outputs. You should also write an

integration test at the other extreme of running the whole system together, you know,

that try to turn on all the different functions that 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, it's no different,

but on a real robot, it's impossible to run the same experiment twice. So 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 estimation is just run as many experiments as we can, maybe try to set up

the environment 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, you know, because there's an argument that the things that'll get you are the things

that are really nuanced in the world. And it'd be very hard to, for instance, put back in a simulation.

Yeah, the I guess the edge cases. What was the hardest thing? Like, so you said walking over

rough terrain, like that just taking footsteps. I mean, people, it's so dramatic and painful in

a certain kind of way to watch these videos from the DRC of robots falling. Yeah, I just so

heartbreaking. I don't know. Maybe it's because for me, at least we anthropomorphize the robot.

Of course, it's funny for some reason, like humans falling is funny. For I don't it's some dark

reason. I'm not sure why it is so, but it's also like tragic and painful. And so speaking of which,

I mean, what what made the robots fall and fail in your view? So I can tell you exactly what

happened on we I contributed one of those our team contributed one of those spectacular falls.

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 a green light to be able

to proceed and its batteries, you know, and therefore it just fell backwards and smashed its head

to ground and it was hilarious. But it wasn't because of bad software, right? But for ours,

so the hardest part of the challenge, the hardest task, in my view, was getting out of the Polaris.

It was actually relatively easy to drive the Polaris. Can you tell the story of the car?

People should watch this video. I mean, the thing you've come up with is just brilliant. But

anyway, sorry, what's, we kind of joke, we call it the big robot little car problem, because

somehow the race organizers decided to give us a 400 pound humoid. 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. We actually had to straddle the the main

column of the and have basically one foot in the passenger seat, one foot in the driver's seat,

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. It didn't have a door, that was okay. But it's just getting up

from crouched from sitting when you're in this very constrained environment. First of all,

I remember after watching those videos, I was much more cognizant of how hard is it,

it is for me to get in and out of the car, and out of the car especially. Like,

it's actually a really difficult control problem. Yeah. I'm very cognizant of it when I'm like

injured for whatever reason. It's really hard. Yeah. So how did you, how did you approach this

problem? So we had a, you know, you think of NASA's operations, and they have these checklists,

you know, prelaunch 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 we used a lot of

motion planning to figure out a sort of configuration of the robot that we get up and over. We

relied heavily on our balancing controller. And, 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 thought it was still driving, told it's

left foot to go like this. And, and that wasn't good. But, but it turned disastrous for us,

because what happened was a little bit of push here. Actually, if 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 and there's no place

to step. So we're relying on our just lowest level reflexes. And even then, I think just hitting

the foot on the seat on the floor, we probably could have recovered from it. But the thing that

was bad that happened is when we did that, and we jostled a little bit, the tailbone of our robot

was only a little off the seat, it hit the seat. And the other foot came off the ground just a

little bit. 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. And then the thing is, once you get off the script,

things 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 in our face first on out of the robot.

But like into the destination.

That's true. We fell in and we got our point for egress.

But so is there any hope for, that's interesting, is there any hope for

Atlas to be able to do something when it's just on its butt and feet in the air?

Absolutely.

So you can, what do you?

No, so that's, that is one of the big challenges. And I think it's still true.

You know, Boston Dynamics and, and, and EMI and there's this incredible work on,

on legged robots happening around the world.

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 more mature solutions have, 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 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,

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. So people are just afraid of

contact because contact is seen as a difficult. It's still a difficult control problem and sensing

problem. Now you're a serious person. I'm a little bit of an idiot and I'm going to ask

you some dumb questions. So I do, I do martial arts. So like Jiu Jitsu, I wrestled my whole life.

So let me, let me ask the question, you know, like whenever people learn that I do any kind

of AI or like I mentioned robots and things like that, they say, when are we going to have robots

that, you know, they can win in a wrestling match or in a fight against a human. So we just mentioned

sitting on your butt, if you're in the air, that's a common position. Jiu Jitsu, when you're on the

ground, you're your down opponent. 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? And we're talking about a lot,

like, I don't know if you're familiar with wrestling, but essentially,

not very, it's basically the art of contact. It's like, it's because you're, you're, you're

picking contact points, and then using like leverage, like to off balance to, to trick people,

like you make them feel like you're doing one thing, and then they, they change their balance,

and then you switch what you're doing, and then results in a throw or whatever. So like, it's

basically the art of multiple contacts. So awesome. It's a nice description of it. So there's also an

opponent in there, right? So, so if very dynamic, right? If you are wrestling a human, and are in

a game theoretic situation with a human, that's still hard. But just to speak to the, you know,

quickly reasoning about contact part of it, for instance,

yeah, maybe even throwing the game theory out of it, almost like,

yeah, almost like a non dynamic opponent, right? There's reasons to be optimistic,

but I think our best understanding of those problems are still pretty hard. I have been

increasingly focused on manipulation, partly where that's a case where the contact has to be

much more rich. And there are some really impressive examples of deep learning policies,

controllers, that, 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 going to happen to objects as they go

through contact. But I think the challenge you just offered there still eludes us,

right? The ability to make a decision based on those models quickly.

You know, I have to think though, it's hard for humans to, when you get that complicated. I think

probably you had maybe a slow motion version of where you learn the basic skills, and you've

probably gotten better at it. And 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.

Well, the, I mean, one of the most amazing things to me about the, we can talk about

martial arts. We could also talk about dancing. It doesn't really matter. It's too human.

I think it's the most interesting study of contact. It's not even the dynamic element of it. It's the,

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 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 feeling, um, sorry, you're learning it intellectually a little

bit, but a lot of it is the body learning it somehow, like instinctually. And whatever that

learning is, that's really, I'm not even sure if that's 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. Yeah, I think there's hierarchy and composition, probably in the systems that

we don't capture very well yet. 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 planning a vacation to

some distant country, which is probably, you probably don't have a controller, a policy for

every possible destination you'll ever pick, right? 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 Josh Tenenbaum just showed

it in his talk the other day. He's got a game he likes to talk about. I think he calls it the

Pick 3 game or something, where he puts a bunch of clutter down in front of a person and he says,

okay, pick three objects and it might be a telephone or a shoe or a Kleenex box or whatever.

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 physics

and mechanics and contact mechanics 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

amazing thing is this ability to be flexible with our models, plan when we need to use our

well oiled controllers when we don't, when we're in familiar territory. Having models,

I think the other thing you just said was something about, I think your awareness of what's

happening is even changing 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 muscles or balanced components that you just weren't

even aware of before. So how do you scaffold that? Yeah, plus the fear of injury, the ambition of goals

of excelling and fear of mortality. Let's see what else is in there as the motivations,

overinflated ego in the beginning, like, and then a crash of confidence in the middle,

all of those seem to be essential for the learning process. And if all that's good,

then you're probably optimizing energy efficiency. Yeah, right. So we have to get that right. So

you know, there was this idea that you would have robots play soccer better

than human players by 2050. That was the goal. Basically, was the goal to beat world champion

team to become a world cup, be like a world cup level team. So are we going to see that first?

Or a robot, if you're familiar, there's an organization called UFC for mixed martial arts.

Are we going to see a world cup championship soccer team that are robots? Or a UFC champion

mixed martial artist as a robot? I mean, it's very hard to say one thing is a harder one.

Some problems are harder than the other. What probably matters is who started the organization

that I mean, I think Robocop has a pretty serious following and there 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. So if you want to have mixed martial arts compete,

you better start your organization now, right? I think almost independent of which problem is

technically harder because they're both hard and they're both different. That's a good point.

I mean, those videos are just hilarious that like, especially the human robot's trying to

try to play soccer. I mean, they're kind of terrible right now. I mean, I guess there is Robo

Sumo wrestling. There's like the Robo one competitions where they do have these robots

that go on the table and basically fight. So maybe I'm wrong. Maybe first of all,

do you have a year in mind for Robocop? Just from a robotics perspective,

seems like a super exciting possibility that like in the physical space, this is what's

interesting. I think the world is captivated. I think it's really exciting. It's it inspires

just a huge number of people when 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 world of digital. I don't think

machines have beat humans at a game in the physical space yet, but that would be just...

You have to make the rules very carefully, right? I mean, if Atlas kicked me in the shins,

I'm down and game over. So it's very subtle on what's fair.

I think the fighting one is a weird one. Yeah, because you're talking about a machine that's

much stronger than you. But yeah, in terms of soccer, basketball, all those kinds.

Even soccer, right? I mean, as soon as there's contact or whatever. And there's,

there are some things that the robot will do better. 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. You're not going to get the

perfect soccer player robot. You're going to get something that exploits the rules, exploits its

super actuators. It's super low band with feedback loops or whatever. And it's going to play the

game differently than you want it to play. And I bet there's ways, I bet there's loopholes.

We saw that in the DARPA challenge that it's very hard to write a set of rules that someone can't

find a way to exploit. Let me ask another ridiculous question. I think this might be

the last ridiculous question, but I doubt it. I aspire to ask as many ridiculous questions

of a brilliant MIT professor. Okay. I don't know if you've seen the black mirror.

It's funny. I never watched the episode. I know when it happened though, because I gave a talk

to some MIT faculty one day on a, on assuming, you know, Monday or whatever, I was telling them

about the state of robotics. And I showed some video from Boston Dynamics of the quadruped

spot at the time. It was the early version of spot. And there was a look of horror that went

across the room. And I said, what, you know, I've shown videos like this a lot of times. What

happened? And it turns out that this video had got, yeah, this black mirror episode had changed

the way people watched. Yeah, the videos I was putting out. The way they see these kinds of

robots. So I talked to so many people who are just terrified because of that episode probably

of these kinds of robots. Hey, I almost want to say that you almost kind of 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 kind of like enjoy being afraid. I don't want

to simplify it, but it feels like they talk about it so often. It almost, there does seem to be an

addictive quality to it. I talked to a guy, a guy named Joe Rogan, who's kind of the flag bearer

for being terrified of these robots. Do you have two questions? One, do you have an understanding

of why people are afraid of robots? And the second question is, 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, it's basically a spot with a gun. And how far are we

away from having robots that go rogue like that, you know, basically spot that goes rogue for some

reason and somehow finds a gun. Right. So I mean, I'm not a psychologist. I think I don't know exactly

why people react the way they do. 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. I don't think robots are going to come after me with a kitchen knife or a

pellet gun right away. And I mean, if they were programmed in such a way, but I used to joke with

Atlas that all I had to do was run for five minutes and its battery would run out. But actually,

they've got a very big battery in there by the end. So it was over an hour.

I think the fear is a bit cultural though, because I mean, you notice that like, I think

in my age, in the US, we grew up watching Terminator. If I'd grown up at the same time in

Japan, I probably would have been watching Astro Boy. And there's a very different reaction to

robots in different countries, right? So I don't know if it's a human innate fear of

metal marvels, or if it's something that we've done to ourselves with our sci fi.

Yeah, the stories we tell ourselves through, through movies, through just

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 we're going to have these robots

very soon that will hurt us. Like, how do you approach making me feel better?

Like, why shouldn't people be afraid? There's a, I think there's a video that went viral

recently. Everything, everything was spot in Boston. Nameless goes viral in general.

But usually it's like really cool stuff, like they're doing flips and stuff, or like sad stuff.

Atlas being hit with a broomstick or something like that. But there's a video where I think

one of the new productions bought 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 can just have a camera, like something about spot being able to walk on four feet

with like really terrified people. So what, what do you say to those people?

I think there is a legitimate fear there, because so much of our future is uncertain.

But at the same time, technically speaking, it seems like we're not there yet. So what do you

say? I mean, I think technology is complicated. It can be used in many ways. I think there are purely

software attacks somebody could use to do great damage. Maybe they have already. You know, I think

wheeled robots could be used in bad ways to drones, right? I don't think that. Let's see. I don't want

to be 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, in the sense that

I think we should continue to create and evolve and our world will change. And if 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. So it's interesting because you didn't mention, like,

this is technically too hard. 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 consciousness or something that

they don't have yet, right? So it's not, I think our ability to anthropomorphize those robots is

probably, we're assuming that they have a level of intelligence that they don't yet have. And that

might be part of the fear. So in that sense, it's too hard. But, you know, there are many scary

things in the world, right? So I think we're right to ask those questions. We're right to

think about the implications of our work.

Right. In the short term, as we're working on it for sure, is there something long term

that scares you about our future with AI and robots? A lot of folks from Elon Musk to Sam

Harris to a lot of folks talk about the, you know, existential threats about artificial

intelligence. Oftentimes robots kind of inspire that the most because of the anthropomorphism.

Do you have any fears?

It's an important question. I actually, I think I like Rod Brooks answer maybe the best on this,

I think, and it's not the only answer he's given over the years, but maybe one of my favorites is,

he says, it's not going to be, he's got a book Flesh and Machines, I believe.

It's not going to be the robots versus the people. We're all going to be robot people

because, you know, we already have smartphones, some of us have serious technology implanted

in our bodies already, whether we have a hearing aid or a pacemaker or anything like this. People

with amputations might have prosthetics. That's a trend, I think, that is likely to continue. I

mean, this is now wild speculation. But I mean, when do we get to cognitive implants and the like?

And yeah, with Neuralink, brain computer interfaces. That's interesting. So there's a dance

between humans and robots that's going to be, it's going to be impossible to be scared of

the other out there, the robot, because the robot will be part of us, essentially. It'd be so

intricately sort of part of our society that it might not even be implanted part of us,

but just it's so much a part of our society. So in that sense, the smartphone is already the

robot we should be afraid of. Yeah. I mean, yeah. And then all the usual fears arise,

the misinformation, the manipulation, all those kinds of things that,

the problems are all the same. They're human problems, essentially, it feels like.

Yeah. I mean, I think the way we interact with each other online is changing the value we put on

personal interaction. And that's a crazy big change that's going to happen and rip through

our, has already been ripping through our society, right? And that has implications that are

massive. I don't know if they should be scared of it or go with the flow, but

I don't see some battle lines between humans and robots being the first thing to worry about.

I mean, I do want to just, as a kind of comment, maybe you can comment about your just feelings

about Boston Dynamics in general, but you know, I love science. I love engineering. I think there's

so many beautiful ideas in it. And when I look at Boston Dynamics or legged robots in general,

I think they inspire people curiosity and feelings in general, excitement about engineering

more than almost anything else in popular culture. And I think that's such an exciting

like responsibility and possibility for robotics. And Boston Dynamics is riding that wave pretty

damn well. Like they found it, they've discovered that hunger and curiosity in the people and they're

doing magic with it. I don't care if the, I mean, I guess that their company have to make money,

right? But they're already doing incredible work and inspiring the world about technology.

I mean, do you have thoughts about Boston Dynamics and maybe others, your own work

in robotics and inspiring the world in that way?

I completely agree. I think Boston Dynamics is absolutely awesome. I think I show my kids those

videos, you know, and the best thing that happens is sometimes they've already seen them, you know,

right. I think, I just think it's a pinnacle of success in robotics that is just one of the

best things that's happened. I absolutely completely agree. One of the heartbreaking things to me

is how many robotics companies fail. How hard it is to make money with a robotics company.

Like iRobot went through hell just to arrive at a Roomba to figure out one product. And then

there's so many home robotics companies like Gebo and Anki, Anki, the cutest toy. There's a great

robot I thought went down. I'm forgetting a bunch of them, but a bunch of robotics companies fail,

Rod's company rethink robotics. Do you have anything hopeful to say about the possibility

of making money with robots? Oh, I think you can't just look at the failures. I mean,

Boston Dynamics is a success. There's lots of companies that are still doing amazingly good

work in robotics. I mean, this is the capitalist ecology or something, right? I think you have

many companies. You have many startups and they push each other forward and many of them fail and

some of them get through and that's sort of the natural way of those things. I don't know that

is robotics really that much worse. I feel the pain that you feel too. Every time I read one of

these, sometimes it's friends and I definitely wish it went better differently. But I think it's

healthy and good to have bursts of ideas, bursts of activities, ideas. If they are really aggressive,

they should fail sometimes. Certainly, that's the research mantra, right? If you're

succeeding at every problem you attempt, then you're not choosing aggressively enough.

Is it exciting to you, the new spot? Oh, it's so good.

When are you getting them as a pet? Yeah, I mean, I have to dig up 75k right now. It's so cool

that there's a price tag. You can go and actually buy it. I have a Skydio R1. Love it.

No, I would absolutely be a customer. I wonder what your kids would think about it. I actually,

Zach from Boston Dynamics would let my kid drive in one of their demos one time,

and that was just so good. I'll forever be grateful for that.

And there's something magical about the anthropomorphization of that arm. It has another

level of human connection. I'm not sure we understand from a control aspect the value of

anthropomorphization. I think that's an understudied and under understood

engineering problem. Psychologists have been studying it. I think it's part, manipulating

our mind to believe things is a valuable engineering. This is another degree of

freedom that can be controlled. I like that. Yeah, I think that's right. I think,

there's something that humans seem to do, or maybe my dangerous introspection, is

I think we are able to make very simple models that assume a lot about the world very quickly.

And then it takes us a lot more time, like your wrestling. You probably thought you knew what

you were doing with wrestling, and you were fairly functional as a complete wrestler,

and then you slowly got more expertise. So maybe it's natural that our first

level of defense against seeing a new robot is to think of it in our existing models of how

humans and animals behave. And it's just, as you spend more time with it,

then you'll develop more sophisticated models that will appreciate the differences.

Exactly. Can you say, what does it take to control a robot? Like, what is the control

problem of a robot? And in general, what is a robot in your view? Like, how do you think of this

system? What is a robot? What is a robot? I think, I told you ridiculous questions.

No, no, it's good. I mean, there's standard definitions of combining computation with

some ability to do mechanical work. I think that gets us pretty close. But I think

robotics has this problem that once things really work, we don't call them robots anymore.

My dishwasher at home is pretty sophisticated. Beautiful mechanisms. There's actually a pretty

good computer, probably a couple of chips in there doing amazing things. We don't think of

that as a robot anymore, which isn't fair. Because then, roughly, it means that robotics

always has to solve the next problem and doesn't get to celebrate its past successes.

I mean, even factory room floor robots are super successful. They're amazing. But that's not the

ones, I mean, people think of them as robots, but they don't, if you ask what are the successes of

robotics, somehow it doesn't come to your mind immediately. So the definition of robot is a

system with some level automation that fails frequently. Something like it's the computation

plus mechanical work and unsolved problems. Yeah. So from a perspective of control and mechanics,

dynamics, what is a robot? So there are many different types of robots. The control that you

need for a Jibo robot, some robot that's sitting on your countertop and interacting with you,

but not touching you, for instance, is very different than what you need for an autonomous car

or an autonomous drone. It's very different than what you need for a robot that's going to walk or

pick things up with its hands. My passion has always been for the places where you're

interacting or doing more dynamic interactions with the world. So walking, now manipulation.

And the control problems there are beautiful. I think contact is one thing that differentiates

them from many of the control problems we've solved classically. The modern control grew up

stabilizing fighter jets that were passively unstable. And there's amazing success stories

from control all over the place. Power grid, I mean, there's all kinds of, it's everywhere

that we don't even realize, just like AI is now. Do you mention contact? Like, what's contact?

So an airplane is an extremely complex system or a spacecraft landing or whatever. But at least

it has the luxury of things change relatively continuously. That's an oversimplification.

But if I make a small change in the command I send to my actuator, then the path that the robot will

take tends to take a change only by a small amount. And there's a feedback mechanism here.

That's what we're talking about. And there's a feedback mechanism. And thinking about this as

locally like a linear system, for instance, I can use more linear algebra tools to study

systems like that, generalizations of linear algebra to these smooth systems.

What is contact? The robot has something very discontinuous that happens when it

makes or breaks, when it starts touching the world. And even the way it touches or the order of

contacts can change the outcome in potentially unpredictable ways. Not unpredictable, but

complex ways. I do think there's a little bit of... A lot of people will say that contact is hard

in robotics, even to simulate. And I think there's a little bit of a... There's truth to that, but

maybe a misunderstanding around that. So what is limiting is that when we think about our robots

and we write our simulators, we often make an assumption that objects are rigid. And when it

comes down, that their mass moves all... It stays in a constant position relative to each other

itself. And that leads to some paradoxes when you go to try to talk about rigid body mechanics

and contact. And so, for instance, if I have a three legged stool with just a... Imagine it comes

to a point at the leg. So it's only touching the world at a point. If I draw my physics...

My high school physics diagram of this system, then there's a couple of things that I'm given

by elementary physics. I know if the system... If the table is at rest, if it's not moving,

zero velocities. That means that the normal force... All the forces are in balance. So the

force of gravity is being countered by the forces that the ground is pushing on my table legs.

I also know, since it's not rotating, that the moments have to balance. And since it's a three

dimensional table, it could fall in any direction, it actually tells me uniquely what those three

normal forces have to be. If I have four legs on my table, four legged table,

and they were perfectly machined to be exactly the right same height and they're set down and

the table's not moving, then the basic conservation laws don't tell me... There are many solutions for

the forces that the ground could be putting on my legs that would still result in the table not

moving. Now, the reason that seems fine, I could just pick one. But it gets funny now because if

you think about friction, what we think about with friction is our standard model says the amount

of force that the table will push back if I were to now try to push my table sideways,

I guess I have a table here, is proportional to the normal force. So if I have... If I'm

barely touching and I push, I'll slide, but if I'm pushing more and I push, I will slide less.

It's called Coulomb friction is our standard model. Now, if you don't know what the normal

force is on the four legs and you push the table, then you don't know what the friction forces are

going to be. And so you can't actually tell, the laws just aren't explicit yet about which

way the table's going to go. It could veer off to the left, it could veer off to the right,

it could go straight. So the rigid body assumption of contact leaves us with some paradoxes,

which are annoying for writing simulators and for writing controllers.

We still do that sometimes because soft contact is potentially harder numerically or whatever,

and the best simulators do both or do some combination of the two. But anyways, because

of these kind of paradoxes, there's all kinds of paradoxes in contact, mostly due to these

rigid body assumptions. It becomes very hard to write the same kind of control laws that we've

been able to be successful with for fighter jets. We haven't been as successful writing those

controllers for manipulation. And so you don't know what's going to happen at the point of

contact, at the moment of contact. There are situations absolutely where you... Where our

laws don't tell us. So the standard approach, that's okay. I mean, instead of having a differential

equation, you end up with a differential inclusion, it's called. It's a set valued

equation. It says that I'm in this configuration, I have these forces applied on me,

and there's a set of things that could happen.

And those aren't continuous, I mean, so when you're saying non smooth, they're not only not

smooth, but this is discontinuous. The non smooth comes in when I make or break a new contact first,

or when I transition from stick to slip. So you typically have static friction,

and then you'll start sliding, and that'll be a discontinuous change in velocity, for instance.

Especially if you come to rest or... That's so fascinating.

Okay, so what do you do? Sorry, I interrupted you. What's the hope under so much uncertainty about

what's going to happen? What are you supposed to do? I mean, control has an answer for this.

Robust control is one approach, but roughly, you can write controllers which try to still

perform the right task, despite all the things that could possibly happen. The world might want

the table to go this way and this way, but if I write a controller that pushes a little bit more

and pushes a little bit, I can certainly make the table go in the direction I want.

It just puts a little bit more of a burden on the control system. And these discontinuities

do change the control system, because the way we write it down right now,

every different control configuration, including sticking or sliding or parts of my body that

are in contact or not, looks like a different system. And I think of them, I reason about them

separately or differently, and the combinatorics of that blow up. So I just don't have enough time

to compute all the possible contact configurations of my humanoid. Interestingly, I mean, I'm a

humanoid. I have lots of degrees of freedom, lots of joints. I've only been around for a

handful of years, it's getting up there, but I haven't had time in my life to visit all of the

states in my system, certainly all the contact configurations. So if step one is to consider

every possible contact configuration that I'll ever be in, that's probably not a problem I need to

solve, right? Just as a small tangent, what's the contact configuration? Just so we can

enumerate what are we talking about? How many are there? The simplest example maybe would be

imagine a robot with a flat foot. And we think about the phases of gate where the heel strikes,

and then the front toe strikes, and then you can heel up, toe off. Those are each different

contact configurations. I only had two different contacts, but I ended up with four different

contact configurations. Now, of course, my robot might actually have bumps on it or other things,

so it could be much more subtle than that, right? But it's just even with one sort of box

interacting with the ground already in the plane has that many, right? And if I was just even a

3D foot, then probably my left toe might touch just before my right toe and things get subtle.

Now, if I'm a dexterous hand, and I go to talk about just grabbing a water bottle,

if I have to enumerate every possible order that my hand came into contact with the bottle,

then I'm dead in the water. Any approach that we were able to get away with that in walking,

because we mostly touch the ground with a small number of points, for instance,

and we haven't been able to get dexterous hands that way.

So, you've mentioned that people think that contact is really hard, and that's the reason

that robotic manipulation is problem is really hard. Is there any flaws in that thinking?

So, I think simulating contact is one aspect, and people often say that one of the reasons

that we have a limit in robotics is because we do not simulate contact accurately in our

simulators. And I think that is the extent to which that's true is partly because our

simulators, we haven't got mature enough simulators. There are some things that are still hard,

difficult that we should change. But we actually know what the governing equations are. They have

some foibles like this indeterminacy, but we should be able to simulate them accurately.

We have incredible open source community in robotics, but it actually just takes a professional

engineering team a lot of work to write a very good simulator like that.

Now, where does I believe you've written Drake?

There's a team of people. I certainly spent a lot of hours on it myself.

What is Drake? What does it take to create a simulation environment for the kind of

difficult control problems we're talking about?

Right. So, Drake is the simulator that I've been working on. There are other good simulators out

there. I don't like to think of Drake as just a simulator because we write our controllers in

Drake. We write our perception systems a little bit in Drake, but we write all of our low level

control and even planning and optimization capabilities. Drake is three things roughly.

It's an optimization library, which provides a layer of abstraction in C++ and Python

for commercial solvers. You can write linear programs, quadratic programs,

semi definite programs, sums of squares programs, the ones we've used mixed integer programs,

and it will do the work to curate those and send them to whatever the right solver is,

for instance, and it provides a level of abstraction. The second thing is a system

modeling language, a bit like LabView or Simulink, where you can make block diagrams out of complex

systems, or it's like ROS in that sense, where you might have lots of ROS nodes that are each

doing some part of your system, but to contrast it with ROS, we try to write, if you write a Drake

system, then you have to, it asks you to describe a little bit more about the system. If you have any

state, for instance, in the system, there are any variables that are going to persist, you have to

declare them, parameters can be declared and the like, but the advantage of doing that is that you

can, if you like, run things all on one process, but you can also do control design against it,

you can do, I mean, simple things like rewinding and playing back your simulations. For instance,

these things, you get some rewards for spending a little bit more upfront cost in describing each

system. And I was inspired to do that because I think the complexity of Atlas, for instance,

is just so great. And I think although, I mean, ROS has been incredible,

absolute huge fan of what it's done for the robotics community, but the ability to rapidly

put different pieces together and have a functioning thing is very good. But I do think that it's

hard to think clearly about a bag of disparate parts, Mr. Potato Head kind of software stack.

And if you can, you know, ask a little bit more out of each of those parts, then you can understand

there the way they work better, you can try to verify them and the like, or you can do learning

against them. And then one of those systems, the last thing I said the first two things that Drake

is, but the last thing is that there is a set of multi body equations, rigid body equations,

that is trying to provide a system that simulates physics. And we also have renderers and other

things, but I think the physics component of Drake is special in the sense that we have done

excessive amount of engineering to make sure that we've written the equations correctly.

Every possible tumbling satellite or spinning top or anything that we could possibly write as a test

is tested. We are making some, you know, I think fundamental improvements on the way you simulate

contact. Just what does it take to simulate contact? I mean, it just seems, I mean, there's

something just beautiful the way you're like explaining contact and you're like tapping

your fingers on the on the table while you're while you're doing it, just easily easily,

just like, just not even like, it was like helping you think, I guess. What I do, you have this like

awesome demo of loading or unloading a dishwasher, just picking up a plate, grasping it like for the

first time. That just seems like so difficult. How do you simulate any of that? So it was really

interesting that what happened was that we started getting more professional about our software

development during the DARPA Robotics Challenge. I learned the value of software engineering and

how to bridle complexity. I guess that's what I want to somehow fight against and bring some

of the clear thinking of controls into these complex systems we're building for robots.

Shortly after the DARPA Robotics Challenge, Toyota opened a research institute,

TRI, Toyota Research Institute. They put one of their, there's three locations, one of them is

just down the street from MIT, and I helped ramp that up as a part of the end of my sabbatical,

I guess. So TRI has given me the TRI Robotics effort, has made this investment in simulation

in Drake, and Michael Sherman leads a team there of just absolutely top notch dynamics experts

that are trying to write those simulators that can pick up the dishes. And there's also a team

working on manipulation there that is taking problems like loading the dishwasher,

and we're using that to study these really hard corner cases kind of problems in manipulation.

So for me, this simulating the dishes, we could actually write a controller, if we just cared

about picking up dishes in the sink once, we could write a controller without any simulation

whatsoever, and we could call it done. But we want to understand what is the path you take

to actually get to a robot that could perform that for any dish in anybody's kitchen

with enough confidence that it could be a commercial product, right? And it has deep

learning perception in the loop, it has complex dynamics in the loop, it has controller, it has

a planner, and how do you take all of that complexity and put it through this engineering

discipline and verification and validation process to actually get enough confidence to deploy?

I mean, the DARPA challenge made me realize that that's not something you throw over the fence

and hope that somebody will harden it for you, that there are really fundamental challenges

in closing that last gap. During the validation and the testing?

I think it might even change the way we have to think about the way we write systems.

What happens if you have the robot running lots of tests and it screws up, it breaks a dish, right?

How do you capture that? I said you can't run the same simulation or the same experiment twice

on a real robot. Do we have to be able to bring that one off failure back into simulation in

order to change our controllers, study it, make sure it won't happen again? Is it enough to just try

to add that to our distribution and understand that on average, we're going to cover that situation

again? There's really subtle questions at the corner cases that I think we don't yet have

satisfying answers for. How do you find the corner cases? That's one kind of... Do you think

it's possible to create a systematized way of discovering corner cases efficiently

in whatever the problem is? Yes. I mean, I think we have to get better at that.

I mean, control theory has, for decades, talked about active experiment design.

What's that? People call it curiosity these days. It's roughly this idea of trying to

exploration or exploitation, but the active experiment design is even more specific. You

could try to understand the uncertainty in your system, design the experiment that will

provide the maximum information to reduce that uncertainty. If there's a parameter you want

to learn about, what is the optimal trajectory I could execute to learn about that parameter,

for instance. Scaling that up to something that has a deep network in the loop and a

planning in the loop is tough. We've done some work with Matt O. Kelly and Amansina. We've worked

on some falsification algorithms that are trying to do rare event simulation that try to just

hammer on your simulator. If your simulator is good enough, you can write good algorithms

that try to spend most of their time in the corner cases. You basically imagine you're building

an autonomous car and you want to put it in, I don't know, downtown New Delhi all the time,

accelerated testing. If you can write sampling strategies which figure out where your controller

is performing badly in simulation and start generating lots of examples around that.

It's just the space of possible places where things can go wrong is very big,

so it's hard to write those algorithms. Rare event simulation is just a really compelling

notion if it's possible. We joke and we call it the black swan generator.

Because you don't just want the rare events, you want the ones that are highly impactful.

I mean, those are the most profound questions we ask of our world. What's the worst that

can happen? What we're really asking isn't some kind of computer science worst case analysis.

We're asking what are the millions of ways this can go wrong? That's our curiosity.

We humans, I think are pretty bad at, we just run into it. I think there's a distributed

sense because there's now like 7.5 billion of us. There's a lot of them and then a lot of them write

blog posts about the stupid thing they've done, so we learn in a distributed way.

I think that's going to be important for robots too. That's another massive theme

at Toyota Research for Robotics is this fleet learning concept. The idea that I as a human,

I don't have enough time to visit all of my states. It's very hard for one robot to experience

all the things, but that's not actually the problem we have to solve. We're going to have

fleets of robots that can have very similar appendages. At some point, maybe collectively,

they have enough data that their computational processes should be set up differently than ours.

All these dishwasher unloading robots, that robot dropping a plate and a human looking

at the robot probably pissed off, but that's a special moment to record. I think one thing

in terms of fleet learning, and I've seen that because I've talked to a lot of folks just like

Tesla users or Tesla drivers. They're another company that's using this kind of fleet learning

idea. One hopeful thing I have about humans is they really enjoy when a system improves

learns, so they enjoy fleet learning. The reason it's hopeful for me is they're willing to put up

with something that's kind of dumb right now. If it's improving, they almost enjoy being part

of the teaching it, almost like if you have kids, you're teaching them something. I think that's

a beautiful thing because that gives me hope that we can put dumb robots out there. The problem

on the Tesla side with cars, cars can kill you. That makes the problem so much harder.

Dishwasher unloading is a little safe. That's why Home Robotics is really exciting. Just to

clarify, for people who might not know, I mean, TRI, Toyota Research Institute, they're pretty

well known for autonomous vehicle research, but they're also interested in Home Robotics.

There's a big group working on multiple groups working on Home Robotics. It's a major part

of the portfolio. There's also a couple other projects and advanced materials discovery using

AI and machine learning to discover new materials for car batteries and the like,

for instance. That's been actually an incredibly successful team. There's new projects starting

up too. Do you see a future of where robots are in our home and robots that have actuators that

look like arms in our home or more like humanoid type robots? We're going to do the same thing

that you just mentioned that dishwasher is no longer a robot. We're going to just not even

see them as robots. What's your vision of the home of the future, 10, 20 years from now,

50 years if you get crazy? I think we already have Roombas cruising around. We have

Alexis or Google Homes on our kitchen counter. It's only a matter of time till they spring arms

and start doing something useful like that. I do think it's coming. I think lots of people

have lots of motivations for doing it. It's been super interesting actually learning about

Toyota's vision for it, which is about helping people age in place,

because I think that's not necessarily the first entry, the most lucrative entry point,

but it's the problem maybe that we really need to solve no matter what.

I think there's a real opportunity. It's a delicate problem. How do you work with people,

help people, keep them active, engaged, but improve the quality of life and help them age

in place, for instance? It's interesting because older folks are also, I mean, there's a contrast

there because they're not always the folks who are the most comfortable with technology, for

example. There's a division that's interesting there that you can do so much good with a robot

for older folks, but there's a gap to feel of understanding. I mean, it's actually kind of

beautiful. Robot is learning about the human and the human is kind of learning about this

new robot thing. Also, when I talk to my parents about robots, there's a little bit of a blank slate

there too. I mean, they don't know anything about robotics, so it's completely wide open.

My parents haven't seen Black Mirror, so it's a blank slate. Here's a cool thing,

like what can you do for me? It's an exciting space.

I think it's a really important space. I do feel like a few years ago, drones were successful enough

in academia. They kind of broke out and started an industry and autonomous cars have been happening.

It does feel like manipulation in logistics, of course, first, but in the home shortly after,

seems like one of the next big things that's going to really pop. I don't think we talked about it,

but what's soft robotics? We talked about rigid bodies. If we can just linger on this

whole touch thing. What's soft robotics? I told you that I really dislike the fact that robots

are afraid of touching the world all over their body. There's a couple reasons for that. If you

look carefully at all the places that robots actually do touch the world, they're almost

always soft. They have some sort of pad on their fingers or a rubber sole on their foot,

but if you look up and down the arm, we're just pure aluminum or something. That makes it hard,

actually. In fact, hitting the table with your rigid arm or nearly rigid arm has some of the

problems that we talked about in terms of simulation. I think it fundamentally changes

the mechanics of contact when you're soft. You turn point contacts into patch contacts,

which can have torsional friction. You can have distributed load. If I want to pick up an egg,

right? If I pick it up with two points, then in order to put enough force to sustain the

weight of the egg, I might have to put a lot of force to break the egg. If I envelop it with

contact all around, then I can distribute my force across the shell of the egg and have a

better chance of not breaking it. Soft robotics is for me a lot about changing the mechanics

of contact. Does it make the problem a lot harder? Quite the opposite.

It changes the computational problem. I think our world and our mathematics has

biased us towards rigid, but it really should make things better in some ways.

Right? I think the future is unwritten there, but the other thing is...

I think ultimately, sorry to interrupt, but I think ultimately it will make things simpler

if we embrace the softness of the world. It makes things smoother, right? The result of

small actions is less discontinuous, but it also means potentially less

instantaneously bad, for instance. I won't necessarily contact something and send it flying off.

The other aspect of it that just happens to dovetail really well is that soft robotics tends

to be a place where we can embed a lot of sensors to. If you change your hardware and make it more

soft, then you can potentially have a tactile sensor, which is measuring the deformation.

There's a team at TRI that's working on soft hands, and you get so much more information.

If you can put a camera behind the skin roughly and get fantastic tactile information, which is

it's super important. In manipulation, one of the things that really

is frustrating is if you work super hard on your head mounted on your perception system for your

head mounted cameras, and then you've identified an object, you reach down to touch it, and the

last thing that happens right before the most important time, you stick your hand and you're

occluding your head mounted sensors. In all the part that really matters, all of your offboard

sensors are occluded. Really, if you don't have tactile information, then you're blind in an

important way. It happens that soft robotics and tactile sensing tend to go hand in hand.

I think we've kind of talked about it, but you taught a course on underactuated robotics.

I believe that was the name of it, actually. Can you talk about it in that context? What is

underactuated robotics? Underactuated robotics is my graduate course. It's online mostly now,

in the sense that the lectures. Several versions of it, I think.

Right. It's really great. I recommend it highly. Look on YouTube for the 2020 versions

until March, and then you have to go back to 2019, thanks to COVID.

No, I've poured my heart into that class. Lecture one is basically explaining what the

word underactuated means. People are very kind to show up and then maybe have to learn

what the title of the course means over the course of the first lecture.

That first lecture is really good. You should watch it.

It's a strange name, but I thought it captured the essence of what control was good at doing

and what control was bad at doing. What do I mean by underactuated? A mechanical system

has many degrees of freedom, for instance. I think of a joint as a degree of freedom,

and it has some number of actuators, motors. If you have a robot that's bolted to the table

that has five degrees of freedom and five motors, then you have a fully actuated robot.

If you take away one of those motors, then you have an underactuated robot.

Why on earth? I have a good friend who likes to tease me. He said,

Russ, if you had more research funding, would you work on fully actuated robots?

The answer is no. The world gives us underactuated robots, whether we like it or not. I'm a human.

I'm an underactuated robot, even though I have more muscles than my big degrees of freedom,

because I have in some places multiple muscles attached to the same joint.

But still, there's a really important degree of freedom that I have, which is the location of my

center of mass in space, for instance. I can jump into the air, and there's no motor that connects

my center of mass to the ground, in that case. I have to think about the implications of not

having control over everything. The passive dynamic walkers are the extreme view of that,

where you've taken away all the motors, and you have to let physics do the work.

But it shows up in all of the walking robots, where you have to use some of the actuators

to push and pull even the degrees of freedom that you don't have an actuator on.

That's referring to walking if you're falling forward. Is there a way to walk that's fully

actuated? It's a subtle point. When you're in contact and you have your feet on the ground,

there are still limits to what you can do. Unless I have suction cups on my feet,

I cannot accelerate my center of mass towards the ground faster than gravity,

because I can't get a force pushing me down. But I can still do most of the things that I want to.

So you can get away with basically thinking of the system as fully actuated, unless you

suddenly needed to accelerate down super fast. But as soon as I take a step, I get into more

nuanced territory. And to get to really dynamic robots, or airplanes, or other things, I think

you have to embrace the underactuated dynamics. Manipulation, people think is manipulation

underactuated? Even if my arm is fully actuated, I have a motor, if my goal is to control

the position and orientation of this cup, then I don't have an actuator for that directly. So I

have to use my actuators over here to control this thing. Now it gets even worse, like what if I have

to button my shirt? What are the degrees of freedom of my shirt? That's a hard question

to think about. It kind of makes me queasy as thinking about my state space control ideas.

But actually, those are the problems that make me so excited about manipulation right now,

is that it breaks a lot of the foundational control stuff that I've been thinking about.

Is there what are some interesting insights you can say about trying to solve an underactuated

control in an underactuated system? So I think the philosophy there is let

physics do more of the work. The technical approach has been optimization. So you typically

formulate your decision making for control as an optimization problem. And you use the

language of optimal control and sometimes often numerical optimal control in order to make those

decisions and balance these complicated equations and in order to control. You don't have to use

optimal control to do underactuated systems, but that has been the technical approach that has

borne the most fruit at least in our line of work. So in underactuated systems, when you say

let physics do some of the work, so there's a kind of feedback loop that observes the state

that the physics brought you to. So there's a perception there. There's a feedback

somehow. Do you ever loop in complicated perception systems into this whole picture?

Right. Right around the time of the DARPA challenge, we had a complicated perception

system in the DARPA challenge. We also started to embrace perception for our flying vehicles at

the time. We had a really good project on trying to make airplanes fly at high speeds through

forests. Sir Tash Karaman was on that project, and it was a really fun team to work on. He's

carried it much farther forward since then. And that's using cameras for perception?

So that was using cameras. At the time, we felt like LiDAR was too heavy and too power

heavy to be carried on a light UAV, and we were using cameras. And that was a big part of it,

was just how do you do even stereo matching at a fast enough rate with a small camera,

a small onboard compute. Since then, we have now, so the deep learning revolution unquestionably

changed what we can do with perception for robotics and control. So in manipulation,

we can address, we can use perception, you know, I think a much deeper way. And we get into not

only, I think the first use of it naturally would be to ask your deep learning system to

look at the cameras and produce the state, which is like the pose of my thing, for instance.

But I think we've quickly found out that that's not always the right thing to do.

Why is that? Because what's the state of my shirt? Imagine, I'm very noisy, I mean,

if the first step of me trying to button my shirt is estimate the full state of my shirt,

including like what's happening in the back, you know, whatever, whatever.

Yeah. That's just not the right specification. There are aspects of the state that are very

important to the task. There are many that are unobservable and not important to the task.

So you really need, it begs new questions about state representation. Another example that we've

been playing with in lab has been just the idea of chopping onions, okay? Or carrots,

turns out to be better. So onions stink up the lab and they're hard to see in a camera.

The details matter, yeah. Details matter, you know. So,

if I'm moving around a particular object, right, then I think about, oh, it's got a position

on orientation and space, that's the description I want. Now, when I'm chopping an onion, okay,

the first chop comes down. I have now a hundred pieces of onion. Does my control system really

need to understand the position and orientation and even the shape of the hundred pieces of onion

in order to make a decision? Probably not, you know, and like, if I keep going, I'm just getting,

more and more is my state space getting bigger as I cut. It's not right. So,

somehow there's, I think there's a richer idea of state. It's not the state that is given to us

by Lagrangian mechanics. There is a, there is a proper Lagrangian state of the system,

but the relevant state for this is some latent state is what we call it in machine learning.

But, you know, there's some, some different state representation.

Some compressed representation. And that's what I worry about saying compressed because it doesn't,

I don't mind that it's low dimensional or not, but it has to be something that's easier to think

about. By us humans. Or my algorithms. Or the algorithms being like control optimal. So, for

instance, if the contact mechanics of all of those onion pieces and all the permutations

of possible touches between those onion pieces, you know, you can give me a high dimensional

state representation, I'm okay if it's linear. But if I have to think about all the possible

shattering combinatorics of that, then my robot's going to sit there thinking and

the soup's going to get cold or something. So, since you taught the course, it kind of entered my

mind, the idea of under actuated as really compelling to see the, to see the world in this

kind of way. Do you ever, you know, if we talk about onions or you talk about the world with

people in it, in general, do you see the world as a basically an under actuated system? Do you

like often look at the world in this way? Or is this overreach?

Under actuated as a way of life, man. Exactly. I guess that's what I'm asking.

I do think it's everywhere. I think some, in some places,

we already have natural tools to deal with it. You know, it rears its head. I mean,

in linear systems, it's not a problem. We just, like an under actuated linear system is really

not sufficiently distinct from a fully actuated linear system. It's a subtle point about when

that becomes a bottleneck in what we know how to do with control. It happens to be a bottleneck.

Although we've gotten incredibly good solutions now, but for a long time that I felt that that was

the key bottleneck in legged robots. And roughly now, the under actuated course is,

you know, me trying to tell people everything I can about how to make Atlas do a backflip, right?

I have a second course now in that I teach in the other semesters, which is on manipulation.

And that's where we get into now more of the, that's a newer class. I'm hoping to put it online

this fall completely. And that's going to have much more aspects about these perception problems

and the state representation questions. And then how do you do control? And the,

the thing that's a little bit sad is that for me, at least, is there's a lot of manipulation tasks

that people want to do and should want to do. They could start a company with it and be very

successful that don't actually require you to think that much about under act or dynamics at all,

even, but certainly under actuated dynamics. Once I have, if I, if I reach out and grab something,

if it, if I can sort of assume it's rigidly attached to my hand, then I can do a lot of

interesting meaningful things with it without really ever thinking about the dynamics of that

object. So they built, we've built systems that kind of reduce the need for that,

enveloping grasps and the like. But I think the really good problems in manipulation. So

I, manipulation, by the way, is more than just pick and place. That's like a lot of people think

of that, just grasping. I don't mean that. I mean, buttoning my shirt. I mean, tying shoelaces.

How do you program a robot to tie shoelaces and not just one shoe, but every shoe, right?

That's a really good problem. It's tempting to write down like the infinite dimensional

state of the, of the laces. That's probably not needed to write a good controller. I know we

could hand design a controller that would do it, but I don't want that. I want to understand the

principles that would allow me to solve another problem that's kind of like that. But I think

if we can stay pure in our approach, then the challenge of tying anybody's shoes is a great

challenge. That's a great challenge. I mean, and the soft touch comes into play there. That's

really interesting. Let me ask another ridiculous question on this topic. How important is

touch? We haven't talked much about humans, but I have this argument with my dad,

where like, I think you can fall in love with a robot based on language alone. And he believes

that touch is essential. Touch and smell, he says, but so in terms of robots, you know, connecting

with humans and we can go philosophical in terms of like a deep meaningful connection,

like love, but even just like collaborating in an interesting way. How important is touch? Like

from an engineering perspective and a philosophical one?

I think it's super important. Even just in a practical sense, if we forget about the emotional

part of it, but for robots to interact safely while they're doing meaningful mechanical work

in the close contact with or vicinity of people that need help, I think we have to have them,

we have to build them differently. They have to be afraid, not afraid of touching the world. So

I think Baymax is just awesome. That's just like the movie of Big Hero 6 and the concept of Baymax,

that's just awesome. I think we should, and we have some folks at Toyota that are trying to,

Toyota Research that are trying to build Baymax roughly. And I think it's just a fantastically

good project. I think it will change the way people physically interact. The same way,

I mean, you gave a couple of examples earlier, but if the robot that was walking around my home

looked more like a teddy bear and a little less like the Terminator, that could change completely

the way people perceive it and interact with it. And maybe they'll even want to teach it, like you

said, right? You could not quite gamify it, but somehow instead of people judging it and looking

at it as if it's not doing as well as a human, they're going to try to help out the cute teddy

bear, right? Who knows? But I think we're building robots wrong and being more soft and more

contact is important, right? Yeah, like all the magical moments I can remember with robots,

well, first of all, just visiting your lab and seeing Atlas, but also Spotmini. When I first

saw Spotmini in person and hung out with him, her, it, I don't have trouble in gendering robots.

I feel robotics people really say, oh, is it it? I kind of like the idea that it's a her or a him.

There's a magical moment, but there's no touching. I guess the question I have, have you ever been,

like, have you had a human robot experience where like a robot touched you? And like it was like,

wait, like, was there a moment that you've forgotten that a robot is a robot? And like the

anthropomorphization stepped in and for a second you forgot that it's not human? I mean, I think

when you're in on the details, then we, of course, anthropomorphized our work with Atlas, but in,

you know, in verbal communication and the like, I think we were pretty aware of it as a machine

that needed to be respected. I actually, I worry more about the smaller robots that could still,

you know, move quickly if programmed wrong. And we have to be careful, actually, about safety

and the like right now. And that if we build our robots correctly, I think then those, a lot of

those concerns could go away. And we're seeing that trend. We're seeing the lower cost, lighter

weight arms now that could be fundamentally safe. I mean, I do think touch is so fundamental. Ted

Adelson is great. He's a perceptual scientist at MIT. And he studied vision most of his life.

And he said, when I had kids, I expected to be fascinated by their perceptual development.

But what really, what he noticed was felt more impressive, more dominant was the way that they

would touch everything and lick everything and pick things up to get on their tongue and whatever.

And he said, watching his daughter convinced him that actually he needed to study tactile

sensing more. So there's something very important. I think it's a little bit also of the passive

versus active part of the world, right? You can passively perceive the world.

But it's fundamentally different if you can do an experiment, right? And if you can change the

world, you can learn a lot more than a passive observer. So you can in dialogue, that was your

initial example, you could have an active experiment exchange. But I think if you're just a camera

watching YouTube, I think that's a very different problem than if you're a robot that can apply

force and touch. I think it's important. Yeah, I think it's just an exciting area of

research. I think you're probably right that this hasn't been under researched. It's, to me,

as a person who's captivated by the idea of human robot interaction, it feels like

such a rich opportunity to explore touch, not even from a safety perspective, but like you said,

the emotional too. I mean, safety comes first. But the next step is like,

like, you know, like a real human connection, even in the world, like even in the industrial

setting, it just feels like it's nice for the robot. I don't know, you might disagree with this,

but because I think it's important to see robots as tools often. But I don't know. I think they're

just always going to be more effective once you humanize them. Like, it's convenient now to think

of them as tools because we want to focus on the safety. But I think ultimately, to create

like a good experience for the worker, for the person, there has to be a human element. I don't

know, for me, it feels like like an industrial robotic arm would be better if has a human element.

I think like rethink robotics had that idea with the Baxter and having eyes and so on having,

I don't know, I'm a big believer in that. It's not my area, but I am also a big believer.

Do you have an emotional connection to Alice? Do you miss them?

I mean, yes, I don't know if I'd more so than if I had a different science project that I'd

worked on super hard, right? But yeah, I mean, the robot, we basically had to do heart surgery

on the robot in the final competition because we melted the core. And yeah, there was something

about watching that robot hanging there. We know we had to compete with it in an hour and it was

getting its guts ripped out. Those are all historic moments. I think if you look back like 100 years

from now, yeah, I think those are important moments in robotics. I mean, these are the early

day, you look at like the early days of a lot of scientific disciplines, they look ridiculous,

it's full of failure. But it feels like robotics will be important in the coming 100 years. And

these are the early days. So I think a lot of people are, look at a brilliant person such as

yourself and are curious about the intellectual journey they've took. Is there maybe three books,

technical fiction, philosophical, that had a big impact on your life that you would recommend,

perhaps others reading? Yeah, so I actually didn't read that much as a kid, but I read

fairly voraciously now. There are some recent books that if you're interested in this kind of

topic, like AI Superpowers by Kaifuli is just a fantastic read. You must read that. Yuval Harari

is just, I think that can open your mind. Sapiens. Sapiens is the first one, Homo Deus is the

second. We mentioned The Black Swan by Talib. I think that's a good sort of mind opener.

I actually, so there's maybe a more controversial recommendation I could give.

Great. I would love that first.

In some sense, it's so classical, it might surprise you. But I actually recently read

Mortimer Adler's How to Read a Book not so long ago. It was a while ago, but

some people hate that book. I loved it. I think we're in this time right now where,

boy, we're just inundated with research papers that you could read on archive with

limited peer review and just this wealth of information.

I don't know. I think the passion of what you can get out of a book, a really good book or

a really good paper if you find it, the attitude, the realization that you're only going to find

a few that really are worth all your time. But then once you find them, you should just dig in

and understand it very deeply and it's worth marking it up and having the hard copy,

writing in the side notes, side margins. I read it at the right time where I was just

feeling just overwhelmed with really low quality stuff, I guess. And similarly,

I'm just giving more than three now. I'm sorry if I've exceeded my quota.

But on that topic just real quick is so basically finding a few companions to keep for the rest of

your life in terms of papers and books and so on. And those are the ones like not doing

what is it, foam wall fear, missing out, constantly trying to update yourself,

but really deeply making a life journey of studying a particular paper essentially,

set of papers. Yeah, I think when you really find something, a book that resonates with you

might not be the same book that resonates with me. But when you really find one that resonates

with you, I think the dialogue that happens and that's what I love that Adler was saying,

you know, I think Socrates and Plato say the written word is never going to capture the beauty

of dialogue, right? But Adler says, no, no, a really good book is a dialogue between you and

the author and it crosses time and space. And I don't know, I think it's a very romantic,

there's a bunch of like specific advice, which you can just gloss over, but the romantic view of

how to read and really appreciate it is so good. And similarly, teaching, I

thought a lot about teaching. And so Isaac Asimov, great science fiction writer,

has also actually spent a lot of his career writing nonfiction, right? His memoir is fantastic.

He was passionate about explaining things, right? He wrote all kinds of books on all

kinds of topics in science. He was known as the great explainer. And I do really resonate with

his style and just his way of talking about, by communicating and explaining to something

is really the way that you learn something. I think about problems very differently because

of the way I've been given the opportunity to teach them at MIT. And we have questions asked,

the fear of the lecture, the experience of the lecture and the questions I get and the interactions

just forces me to be rock solid on these ideas in a way that I didn't have that. I don't know,

I would be in a different intellectual space. Also video, does that scare you that your

lectures are online? And people like me in sweatpants can sit sipping coffee and watch

you give lectures that I think it's great. I do think that something's changed right now,

which is, you know, right now we're giving lectures over Zoom, I mean, giving seminars

over Zoom and everything. I'm trying to figure out, I think it's a new medium.

Do you think it's possible? Yeah, I've been, I've been quite cynical

about the human to human connection over that medium. But I think that's because it's

hasn't been explored fully. And teaching is a different thing.

Every lecture is a, I'm sorry, every seminar even, I think every talk I give,

you know, it's an opportunity to give that differently. I can deliver content directly

into your browser. You have a WebGL engine right there. I could, I can throw 3D content into your

browser while you're listening to me, right? Yeah. And I can assume that you have a, you know,

at least a powerful enough laptop or something to watch Zoom while I'm doing that while I'm

giving a lecture. That's a new communication tool that I didn't have last year, right? And

I think robotics can potentially benefit a lot from teaching that way.

Okay. We'll see. It's going to be an experiment this fall.

It's interesting. I'm thinking a lot about it.

Yeah. And also like the, the length of lectures or the length of like, there's something, so like,

I guarantee you, you know, it's like 80% of people who started listening to our conversation

are still listening to now, which is crazy to me. But so there's a, there's a patience and

interest in long form content, but at the same time, there's a magic to forcing yourself to

condense an idea to the shortest possible, uh, shortest possible like clip. It can be a part

of a longer thing, but like just a really beautifully condensed an idea. There's a lot of

a opportunity there that's easier to do in remote with, I don't know, uh, with editing too. Editing

is an interesting thing. Like what, uh, you know, when most professors don't get, when they give a

lecture, you don't get to go back and edit out parts like Chris, like Chris bit up a little bit.

That's also, it can do magic. Like if you remove like five to 10 minutes from an hour lecture,

it can, it can actually, it can make something special of a lecture. I've, uh, I've seen that

of myself and, and, and in others too, cause I added other people's lectures to extract clips.

It's like there's certain tangents they're like that lose, they're not interesting. They're,

they're, they're mumbling. They're just not, they're not clarifying. They're, they're not helpful

at all. And once you remove them, it's just, I don't know, editing can be magic. It takes a

lot of time. Yeah. It takes, it depends like what is teaching you have to ask. Yeah. Um,

um, um, yeah. Cause I find the editing process is also beneficial as, uh, for teaching, but also

for your own learning. I don't know if, have you watched yourself on the other day? Have you watched

those videos? It's, I mean, not all of them. It could be, it could be painful and to see like how

to improve. So do you find that, uh, I know you segment your, um, your podcast. Do you think that

it helps people with the, the attention span aspect of it? Or is it segment like sections? Like,

yeah, we're talking about this topic, whatever. Nope. Nope. That just helps me. It's actually bad.

So, uh, and you've been incredible. Um, so I'm, I'm learning, like I'm afraid of conversation.

This is even today. I'm terrified of talking to you. I mean, it's something I'm, um, trying to

remove for myself. I, there's, there's a guy, I mean, I've learned from a lot of people, but

really, um, there's been a few people who's been inspirational to me in terms of conversation.

Whatever people think of him, uh, Joe Rogan has been inspirational to me because, uh, comedians

have been to being able to just have fun and enjoy themselves and lose themselves in conversation

that requires you to be a great storyteller, to be able to, uh, pull a lot of different pieces of

information together, but mostly just to enjoy yourself in conversations. I'm trying to learn

that these notes are, you see me looking down. That's like a safety blanket that I'm trying to

let go of more and more. Cool. Um, so that's that people love just regular conversation.

That's, that's what they, the structure is like, whatever. Uh, I would say, I would say maybe

like 10 to like, so there's a bunch of, you know, there's, uh, probably a couple of thousand

PhD students listening to this right now, right? And they might know what we're talking about,

but there is somebody I guarantee you right now in Russia, some kid who's just like,

who's just smoked some weed is sitting back and just enjoying the hell out of this conversation,

not really understanding. You kind of watch some Boston Dynamics videos. He's just enjoying it.

Um, and I salute you, sir. Uh, no, but just like there's a, so much variety of people,

uh, that just have curiosity about engineering, about sciences, about mathematics and, um, and

also like I should, I mean, um, enjoying it is one thing, but also often notice it in inspires

people to, there's a lot of people who are like in their undergraduate studies, trying to figure

out what, uh, trying to figure out what to pursue. And these conversations can really spark the

direction of their, of their life. And in terms of robotics, I hope it does. Cause, uh, I'm excited

about the possibilities of robotics brings on that topic. Um, do you have advice? Like what advice

would you give to a young person about life? A young person about life or a young person about

life in robotics? Uh, it could be in robotics. It could be in life in general. It could be career.

It could be, uh, relationship advice. It could be running advice, just like

they're, um, that's one of the things I see, like we talked like 20 year olds. They're, they're like,

how do I, how do I do this thing? What, what do I do? Um, if they come up to you, what would you

tell them? I think it's an interesting time to be a kid these days. Everything points to this being

sort of a winner take all economy and the like, I think the people that will really excel in my

opinion are going to be the ones that can think deeply about problems. Um, you have to be able to

ask questions, agilely and use the internet for everything it's good for and stuff like this.

And I think a lot of people will develop those skills. I think the leaders, thought leaders,

you know, robotics leaders, whatever, are going to be the ones that can do more and they can think

very deeply and critically. Um, and that's a harder thing to learn. I think one, one path to

learning that is through mathematics, through engineering. Um, I would encourage people to

start math early. I mean, I didn't really start. I mean, I, I was always in the, the better math

classes that I could take, but I wasn't pursuing super advanced mathematics or anything like that

until I got to MIT. I think MIT lit me up and, uh, really started the life that I'm living now.

But, uh, yeah, I really want kids to, to dig deep, really understand things, building things too.

I mean, pull things apart, put them back together. Like that's just such a good way to really

understand things and expect it to be a long journey, right? It's, uh, you don't have to

know everything. You're never going to know everything.

So think deeply and stick with it.

Enjoy the ride, but just make sure you're not, um, yeah, just, just make sure you're, you're,

you're stopping to think about why things work.

And it's true. It's, uh, it's easy to lose yourself in the, in the, in the distractions of the world.

We're overwhelmed with content right now, but

you have to stop and pick some of it and, and really understand it.

Yeah. On the book point, I've read, um, Animal Farm by George Orwell, a ridiculous number of times.

So for me, like that book, I don't know if it's a good book in general, but for me,

it connects deeply somehow. Uh, it somehow connects. So I was born in the Soviet Union,

so it connects to me to the entirety of the history of the Soviet Union and to World War II,

and to the love and hatred and suffering that went on there and the, uh, the corrupting nature of

power and greed. And just somehow I just, that, that, that book has taught me more about life

than like anything else, even though it's just like a silly, like childlike book about pigs.

I don't know why it just connects and inspires. And the same, there's a few, um, yeah, there's a

few technical books too and algorithms that just, yeah, you return too often. Right. I'm, I'm, I'm

with you. Um, yeah, there's, uh, I don't, and I've been losing that because of the internet.

I've been like, uh, going on, I've been going on archive and blog posts and GitHub and, and the

new thing and, uh, you lose your ability to really master an idea. Right. Well, exactly right.

What's the fond memory from childhood when baby Russ Tedrick? Well, I guess I just said that, um,

um, at least my current life begins, began when I got to MIT. If I have to go farther than that.

Yeah. What was, was there a life before MIT? Oh, absolutely. But, but let me actually tell

you what happened when I first got to MIT. Cause that, I think might be relevant here. But I,

you know, I had taken a computer engineering degree at Michigan. I enjoyed it immensely,

learned a bunch of stuff. I was, I liked computers. I liked programming. Um, but when I did get to

MIT and started working with Sebastian Song, theoretical physicist, computational neuroscientist,

the culture here was just different. Um, it demanded more of me, certainly mathematically

and in the critical thinking. And I remember the day that I, uh, to borrowed one of the books from

my advisor's office and walked down to the Charles River and was like, I'm getting my butt kicked,

you know? Um, and I think that's going to happen to everybody who's doing this kind of stuff.

Right. I think, uh, I expected you to ask me the meaning of life. You know, I think that the, uh,

somehow I think that's, that's got to be part of it. This.

I'm doing hard things. Yeah. Did you, uh, did you consider quitting at any point?

Did you consider this isn't for me? No, never that. I mean, I was,

I was working hard, but I was loving it. Right. I mean, there's, I think the,

there's this magical thing where you, uh, you know, I'm lucky to surround myself with people that

basically almost every day I'll, I'll, I'll see something. I'll be told something or something

that I realized, wow, I don't understand that. And if I could just understand that there's,

there's something else to learn that if I could just learn that thing, I would connect another

piece of the puzzle. And, and, uh, you know, I think that is just such an important aspect and

being willing to understand what you can and can't do and, and loving the journey of going

and learning those other things. I think that's the best part.

I don't think there's a better way to end it for us. I've, um, you've been an inspiration to me

since I showed up at MIT. Uh, your work has been an inspiration to the world. This conversation

was amazing. I can't wait to see what you do next with robotics, home robots. I,

I hope to see you work in my home one day. So thanks so much for talking today. It's been awesome.

Cheers. Thanks for listening to this conversation with Rostedrik and thank you to our sponsors,

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at Lex Friedman spelled somehow without the E just F R I D M A N. And now let me leave you

with some words from Neil deGrasse Tyson talking about robots in space and the emphasis we humans

put on human based space exploration. Robots are important. If I don my pure scientist hat,

I would say just send robots. I'll stay down here and get the data. But nobody's ever given a parade

for a robot. Nobody's ever named a high school after a robot. So when I don my public educator hat,

I have to recognize the elements of exploration that excite people. It's not only the discoveries

and the beautiful photos that come down from the heavens. It's the vicarious participation

in discovery itself. Thank you for listening and hope to see you next time.