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 Dauber 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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And now 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 its foot, its spoke comes around and hits the ground, it loses a little energy.
Every time it takes a step forward, it gains a little energy.
Those things can come into perfect balance, and actually they 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, looks a little
bit more like walking, at least it's a biped.
So what Steve and Andy, 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, they made a beautiful robot, but
then the finesse, because this was working, 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.
We were, at that time, the walking robot, the best walking robot in the world was Hondo's
Asamo, absolutely marvel of modern engineering.
This is 90s?
This was in 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, like 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 go.
Let physics do most of the work, and you just have to give it a little bit of energy.
Just when you only walk down a ramp, it would never walk on the flat.
To walk on the flat, you have to give it 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.
That sounds very Zen, but I can also imagine how many failed versions they had to go through.
How many?
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 how they could?
I don't know if it's thousands, but it's a lot.
It takes some patience.
There's no question.
In that sense, control might help a little bit.
I think everybody, even at the time, said that the answer is to do with that with control,
but it was just pointing out that maybe the way we're doing control right now isn't the
way we should.
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.
It sort of goes with the passive walking idea.
Is there, how energy efficient are animals?
There's a great series of experiments by George Lodder at Harvard and Mike Tranafilo
at MIT.
They were studying fish swimming in a water tunnel.
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.
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.
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.
They put a real fish behind this and watched how it swims.
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 gate.
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 human walks, the fish has a characteristic gate.
You put that fish behind the rock, its gate changes.
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 it wasn't clear
how much of that was mechanics of the fish, how much of that is control, the brain.
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.
Then they asked, what would that dead fish do when it was hanging up behind the rock?
What you'd expect is it flopped around like a dead fish in the vortex wake until something
sort of amazing happens.
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?
Or maybe the physics of our world completely, 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.
I mean, do they co-evolve?
Yeah, somehow co-evolve.
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.
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 mean, 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 your motors
on the robot, the controller's job is to send commands to the motor that add new energy
into the system.
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.
Even our bodies, the mechanics of our bodies, certainly with exercise, they evolved.
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.
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 bypass.
I think some, I think I read somewhere that the reason the 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 a robotics and a 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, when things are good, but for instance, people
talk about what we should eat now because our ancestors were meat eaters or whatever.
Oh yeah, I love that.
But probably the reason that one pre-homo 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, probably one of them happened to be in the wrong place.
One of them happened to forage a food that was okay even when the glaciers came or something
like that.
There's a million variables that contributed and are actually the amount of information
we're working with in telling these stories, these evolutionary stories is very little.
So yeah, just like you said, it seems like if you study history, it seems like history
turns on these little events that otherwise would seem meaningless, but in the grant,
when you in retrospect were turning points.
That's probably how 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 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 what your dietary processes are like,
but these days I've been eating only meat, which is, there's a large community of 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 walking?
There's a great book, by the way, a series of books by Nicholas Taylor 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 maybe it was someone getting hit by a rock, as you say.
That said, 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 too, 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?
I mean, the reason I spent a long time working on bipedal walking was because it was hard,
and 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, right?
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 had 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, 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'm not going to, 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.
All right.
I think a lot of us, at least me, probably have never deeply studied how to properly
brush my teeth, right, or wash as now with 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 it 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 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.
Oh, okay.
And we do amazing things with our hands and we stick our foot in a big solid shoe, right?
So there's, I think, you know, when you're barefoot, you're 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, they're worried about getting cuts
or getting stepping on rocks.
First of all, even if that was a concern, I think those are all like very short term,
you know, if I get a scratch or something, it'll heal in a week.
If I blow out my knees, I'm done running forever.
So I will trade the short term for the long term anytime.
But even then, you know, this, again, to my wife's chagrin, your feet get tough, right?
And uh.
Cow's.
Okay.
Yeah.
I can run over animals to anything now.
I mean, what, maybe can you talk about, is there a hint, like, is there tips or tricks
that you have, uh, suggestions about, like, if I wanted to try it, you know, there, there
is a good book actually, uh, there's probably more good books since I read them.
But Ken Bob, barefoot Ken Bob Saxton, um, he's an interesting guy, but I think his
book captures, uh, the right way to describe running barefoot running to somebody better
than any other I've seen.
So you run pretty good distances and you bike and is, is there, um, 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.
Um, what are we talking about here with, with, uh, 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 at bike there, um, 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.
Uh, yeah.
Or with minimal shoes or whatever that 12, 12 times two.
Yeah.
Okay.
It's, it became kind of a game of how can I get to work?
I've rollerbladed.
I've done all kinds of weird stuff, but, um, my favorite one these days, I've been taking
the Charles River to work.
So, um, 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, um, I can spend a long time getting there.
And it's, you know, it's not about, I don't know, it's just about, uh, I've had people
ask me, how can you justify taking that time?
Uh, but for me, it's just a magical time to think, to compress, decompress, um, you know,
especially, I'll wake up, do a lot of work in the morning, and then I kind of have to
just let that settle before I, 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, a large group of people, I mean, you're, is there days where you're
like, Oh shit, I got to get to work in an hour.
Like, I mean, uh, is, is there, is there a tension there where, and like, if we look
at the grand scheme of things, just like you said long-term, that meeting probably doesn't
matter.
Like you can always say, I'll just, I'll run and let the meeting happen.
How it happens.
Like what, uh, how do you, uh, that Zen, how do you, uh, what do you do with that tension
between the real world saying urgently, you need to be there, this is important, everything
is melting down, how we're going to fix this robot.
There's this, uh, a critical meeting and then there's this, the Zen beauty of just running
the simplicity of it and 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 a, you know, some, some dollar per minute that I was late.
Uh, I've run some fast splits to daycare, uh, but that those times are past now.
Um, I think work, uh, you can find a work-life balance in that way.
I think you just have to, um, I think I am better at work because I take time to think
on the way in.
So I plan my day around it, um, and I 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, uh,
what, uh, have you run an ultra marathon before?
Do you do races?
Is there, what's, uh, to win, not to, uh, I'm not going to like take a dingy across
the Atlantic or something.
If that's what you want, but, uh, 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, I do have some fun things that I will try and 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 if they, I'll take a standup paddle
board these days on, on the airplane and then I'll try to paddle around where I'm going
or whatever.
And I've done some crazy things, but, um,
But not for the, um, you know, I've, I, I now talk, I don't know if you know who David
Goggins is by any chance.
Not well, but yeah, but I, I talk to him now every day.
So he's the person who made me, uh, 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.
Uh, so is that, do you have that thing in you?
Or are you, uh, 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?
Uh, you should end feeling better than you started, but, um, it's mostly, I think, and
COVID has tested this cause I've lost my commute.
I think I'm perfectly happy walking around, uh, around town with my wife and, uh, kids
if they could get them to go, uh, 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 do you 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, um, the clarity of thought
that some of the more mathematical control theory can bring to even very complex, messy
looking problems is really, it really had a big impact on me and, and, uh, I had a day
even, 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, um, and we went from these, from these abstract discussions about maybes
and what ifs and, and what a great idea to these super precise statements about systems
that aren't that much more simple or, or abstract than the ones I care about deeply.
And the contrast of that is, um, yeah, 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 problem
more in this case, it's 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 I do 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, uh, of an intersection that's like high def that
I just, I would watch so I wouldn't have to sit out there.
But that's interesting.
Like I feel, that's tough, that's a tough example because I mean the learning humans
are 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, they'll tell that story.
I may be even, I, 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 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 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, uh, there are times where problems that can be solved with well-known, mature
methods, um, could also be solved with, uh, with a deep learning approach.
And, um, there's an argument that you must use learning even for the parts we already
think we know, because if the human has touched it, then you've, you've, 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.
And 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.
Don't lose it.
Yeah.
So, 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 the F equals MA equations themselves.
So, 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 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 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, and you have a important history for MIT, I think, as 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, put a spotlight on self-driving
cars.
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 averted disaster.
And certainly in the disaster response, there were times we would have loved to have sent
robots in.
So in practice, what we ended up with was a grand challenge, a DARPA Robotics Challenge,
where Boston Dynamics was to make humanoid robots.
People like me and the amazing team at MIT 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 Boston Dynamics
humanoids in order to compete in the final challenge, which was a physical challenge.
And at that point, it was decided that it's humanoid robots early on.
There were two tracks.
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 pretty big, scary-looking robot.
Expensive too.
Expensive.
Okay.
So, 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 not really
from a robotics perspective.
It's like, didn't they do it in the 80s?
Is the kind of feeling I would have when you first look at the problem, it's on wheels,
but like, humanoid robots, that sounds really hard.
So what are the, psychologically speaking, what were you feeling, excited, scared?
Why the heck did you get yourself involved in this kind of messy challenge?
We didn't really know for sure what we were signing up for, in the sense that you could
have something that, as it was described in the call for participation, 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 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 locomoting over a complex terrain played a pretty big role in the competition.
And the degree of autonomy wasn't clear?
The degree of autonomy was always a central part of the discussion.
So what wasn't clear was how far we would be able to get with it.
So the idea was always 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 we had degraded networks at 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 operate the robot, every joint of the robot.
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 it would differentiate the teams that
put more autonomy on the robot and had a few clicks and just said, go there, do this,
go there, do this versus someone who's picking every footstep or something like that.
So what were some 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 whole process of from the early idea stages to actually competing.
I mean, this was a defining experience for me.
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 relaxing and
restorative for a sabbatical.
So you got tenure before this?
Yeah.
Yeah.
Yeah.
It was a good time for me.
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.
The team, we all just worked our butts off.
We, you know, we're in that lab almost all the time.
Okay.
So, I mean, there were some, of course, high highs and low lows throughout that, anytime
you're, you know, not sleeping and devoting your life to a 400-pound humanoid.
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, hanging limp, you know, 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?
You've got a robot just watching us do all the manual labor so that it can take its little
stroll across the train.
But I mean, even the virtual robotics challenge was super nerve-wracking and dramatic.
I remember, so we were using Gazebo as a simulator on the cloud, and there was all these interesting
challenges.
The investment that OSR's FC, whatever they were called at that time, Brian Gerke's team
at Open Source Robotics, they were pushing on the capabilities of Gazebo in order to
scale it to the complexity of these challenges.
So, 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, and the physics will run on the other, and
you have to connect.
Now, the physics, they wanted it to run at real-time rates, because there was an element
of human interaction, and humans could, if you do want to tell the op, it works way better
if it's at frame rate.
And it was very hard to simulate these complex scenes at real-time rate.
So right up to like days before the competition, the simulator wasn't quite at real-time rate.
And that was great for me, because my controller was solving a 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.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 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.
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 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, it happened at Frank Permanter, who's a very, very sharp, he was 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.
It was 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 of 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?
So we always knew that if we fell back to, 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, I could come crashing to the ground if your
solver's not fast enough.
But 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, and robotics
people in general, but they, they have moments at moments have worked 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
people 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 earth
willing to push themselves to that degree?
Is there value in that?
And why are they so happy?
I think, I think you got it right.
I mean, I think the causality is not that we work hard and I think other disciplines
work very hard too, but it's, I don't think it's that we work hard and therefore we are
happy.
I think we found something that we're truly passionate about.
It makes us very happy.
And then we get a little involved with it and spend a 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, 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?
What's a loop of tests?
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 that try to turn on all of 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, 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.
I guess the edge cases.
What was the hardest thing?
Like, so you said walking over rough terrain, like 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 also 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 made the robots fall and fail in your view?
So I can tell you exactly what happened on a, we, I contributed one of those, our team
contributed one of those spectacular falls.
Every one of those falls 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 so I can interrupt?
No, of course.
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 that 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 main column of the, and have basically one foot in the
passenger seat, one foot 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
it is for me to get in and out of the car, and out of the car especially.
It's actually a really difficult control problem.
I'm very cognizant of it when I'm injured for whatever reason.
It's really hard.
Yeah.
So how did you approach this problem?
So we had a, you think of NASA's operations and they have these checklists, pre-launch
checklists and the like.
We weren't far off from that.
We had this big checklist and on the first day of the competition, we were running down
our checklist.
And one of the things we had to do, we had to turn off the controller, the piece of
software that was running, that would drive the left foot of the robot in order to accelerate
on the gas.
And then we turned on our balancing controller.
And the nerves jitters of the first day of the competition, someone forgot to check that
box and turn that controller off.
So 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 basically, when the robot was in one of its most precarious sort of configurations
trying to sneak its big leg out of the side, the other controller that thought it was still
driving told it's left foot to go like this.
And that wasn't good.
But it turned disastrous for us because what happened was a little bit of push here, actually,
we have videos of us 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 take a step to recover, they were all
disabled because we were in the car and there's no place to step.
So we were relying on our just lowest level reflexes.
And even then, I think just hitting the foot 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, Woody?
No.
So that's, that is one of the big challenges.
And I think it's still true, you know, Boston Dynamics and, and, um, Animal 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, 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 pushed 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, 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 martial arts, so like Jiu-Jitsu, I've wrestled my whole life.
So 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, that can win in a wrestling match or in a fight against a human?
So we just mentioned sitting on your butt in the air, that's a common position, Jiu-Jitsu,
when you're on the ground, you're your down opponent.
Like 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 it's basically the art of contact.
It's like, it's because you're picking contact points and then using like leverage like to
off balance to trick people, like you make them feel like you're doing one thing and
then 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 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 a, 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
can appear to do good things through contact.
We've even got new examples 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 too when you get that complicated.
I think probably you had maybe a slow motion version of where you learned the basic skills
and you've probably gotten better at it and 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 plan the optimal sequence that might be a problem we never solve.
So 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, doesn't really matter, too human.
I think it's the most interesting study of contact.
It's not even the dynamic element of it.
It's the, like when you get good at it, it's so effortless.
Like I can just, I'm very cognizant of the entirety of the learning process being essentially
like learning how to move my body in a way that I could throw very large weights around
effortlessly.
And I can feel the learning, like I'm a huge believer in drilling of techniques and you
can just like feel your, you're not feeling, you're feeling, 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
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 three 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, an overinflated ego in the beginning and then
the 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 there was this idea that you would have robots play soccer better than human players
by 2050.
That was the goal.
Well, basically, it was the goal to beat World Champion team to become a World Cup, beat
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 harder, some problems harder than the other.
What probably matters is who started the organization that, I mean, I think Robocup
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 humanoid robots trying
to play soccer, I mean, they're kind of terrible right now.
I mean, I guess there is RoboSumo wrestling.
There's like the RoboOne competitions where they do have these robots that go on the table
and basically fight.
Maybe I'm wrong, maybe.
First of all, do you have a year in mind for Robocup, just from a robotics perspective?
It 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 inspires just a huge number of people when a machine beats a human at a game that humans
are really damn good at.
So you're talking about chess and Go, but that's in the 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, you know, 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 of things.
Even soccer, right?
I mean, as soon as there's contact or whatever, and 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, you know, the perfect soccer player robot.
You're going to get something that exploits the rules, exploits its super actuators, its
super low bandwidth, you know, 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, right?
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, unassuming, 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, you know, I've shown videos like this a lot of times.
What happened?
It turns out that this video had got, yeah, this black mirror episode had changed the
way people watched, um, 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.
Um, I talked to a guy, so there's a guy named Joe Rogan, who's kind of the flag bearer for
being terrified of these robots.
Uh, do you have a, two questions, one, do you have an understanding of why people are
afraid of robots?
And the second question is, uh, in black mirror, just to tell you the episode, I don't even
remember it that much anymore, but these robots, I think they can shoot like a pellet or something.
They basically have, it's basically a spot with a gun.
And um, how far are we away from, uh, 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, I'm not a psychologist.
Um, I think I don't know exactly why, uh, people react the way they do.
Um, I think, I think we have to be careful about the way robots influence our society
and the like.
That's something that's a responsibility that roboticists need to embrace.
Um, I don't think robots are going to come after me with a kitchen knife or a pellet
gun right away.
And I mean, they, if they were programmed in such a way, but I used to joke with Atlas
that, um, all I had to do was run for five minutes and its battery would run out.
But uh, actually they've got a very big battery in there by the end.
So it was over an hour.
Um, 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,
right?
If I had 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, uh, in different countries, right?
So I don't know if it's a human innate fear of metal marvels or if it's, um, um, something
that we've done to ourselves with our sci-fi, uh, yeah, the stories we tell ourselves through,
uh, through movies, through just, uh, through popular media.
But if, if I were to tell, you know, if, if you were my therapist and I said, I'm really
terrified that, uh, we're going to have these robots, uh, very soon that will hurt us.
Um, like, how do you approach making me feel better?
Um, like, why shouldn't people be afraid?
I mean, there's a, I think there's a video that went viral recently, everything, everything
was spot in it.
And Boston name was goes viral in general, but usually it's like really cool stuff.
Like they're doing flips and stuff or like sad stuff and the Atlas being hit with a broomstick
or something like that, but, uh, there's a video where I think, uh, 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.
Um, 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, um, complicated.
It can be used in many ways.
I think there are purely software, um, attacks somebody could use to do great damage.
Maybe they have already, um, you know, I think, uh, wheeled robots could be used in bad ways
too.
Drones.
Drones.
Right.
Um, I don't think that, let's see.
I don't want to be, um, building technology just because I'm compelled to build technology
and I don't think about it, but I would consider myself, uh, technological optimist, I guess,
um, in the sense that I think we should continue to create and evolve and our world will change.
Um, and if we, 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, 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, um, 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, um, you know, there are many scary things in the
world, right?
So, uh, I think we're right to ask those questions.
We're right to, um, think about the implications of our work.
Right, in the, in the, in the short term as we're working on it for sure, is there something
longterm that scares you about our future with AI and robots?
A lot of folks, uh, 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, um, inspire that the most because of the anthropomorphism.
Do you have any fears?
It's an important question, um, 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, um, he says it's not going to be, he's got a book flesh and machines,
I believe, um, it's not going to be the robots versus the people.
We're all going to be robot people because, um, you know, we already have smartphones,
some of us have, um, serious technology implanted in our bodies already, whether we have a hearing
aid or a pacemaker or anything like this, um, uh, people with amputations might have
prosthetics, um, that's a trend I think that is likely to continue.
I mean, this is now, uh, wild speculation, but, uh, 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, there's a dance between humans and robots that's, it'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 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 of part of our, yeah, our society.
So in that sense, the smartphone is already the robot we should be afraid of.
Yeah.
Uh, I mean, yeah, and all the usual fears arise, uh, the misinformation, um, the manipulation,
all those kinds of things that, um, that the problems are all the same.
They're all, they're human problems, essentially.
It feels like.
Yeah.
I mean, I think the, the way we interact with each other online is changing the value we
put on, you know, 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, um, I don't see,
you know, 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 and the people and they're doing magic with it.
I don't care if the, I mean, I guess it's their company to 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
and 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.
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 that'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.
Like do you, do you have anything, anything hopeful to say about the possibility of making
money with robots?
Oh, I think you can't just look at the failures.
You can all, 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, 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 things, 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, I sometimes it's friends and I definitely wish it went
better or went 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.
I mean, it's so cool that there's a price tag, you can go and then actually buy it.
I have a SkyDior one, love it.
So 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.
So good.
I'll forever be grateful for that.
And there's something magical about the anthropomorphization of that arm as 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.
It's been, psychologists have been studying it.
I think it's part like manipulating our mind to believe things is a valuable engineering.
Like, this is another degree of freedom that could be controlled.
I like that.
I think that's right.
I think, you know, 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 know, as you probably thought
you knew what you're doing with wrestling and you were fairly functional as a complete
wrestler and then you slowly got more expertise.
But 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 robotics...
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.
Like, 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.
Solve problem, 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.
Like modern control grew up, stabilizing fighter jets that were passively unstable, and there's
like 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.
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 a lot of people will say that contact is hard in robotics, even to simulate.
And I think there's a 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, 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.
So for instance, if I have a three-legged stool, 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 the 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'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.
The situation is absolutely 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, right?
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.
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, right?
And this 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, right?
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?
And 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 it 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 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, we 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 what is, 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.
So it has optimization capabilities as well.
Absolutely.
Yeah.
I mean, Drake is three things roughly.
It's an optimization library, which is sits on, it 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, it asks you to describe a little bit more about the system.
If you have any state, for instance, in the system, 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 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.
I was inspired to do that because I think the complexity of Atlas, for instance, is just
so great.
I think, although, I mean, ROS has been an 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.
If you can ask a little bit more out of each of those parts, then you can understand the
way they work better.
You can try to verify them and the like, or you can do learning against them.
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.
We also have renderers and other things, but I think the physics component of Drake is
special in the sense that we have done an 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, I think, fundamental improvements on the way you simulate contact.
What does it take to simulate contact?
It just seems, I mean, there's something just beautiful the way you're explaining contact
and you're tapping your fingers on the table while you're doing it.
Easily.
Easily.
Just not even like, it was like helping you think, I guess.
You have this awesome demo of loading or unloading a dishwasher.
Just picking up a plate, grasping it for the first time.
That just seems 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, 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.
They're doing 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.
Nick, how do you find the corner cases?
That's one kind of...
Do you think there's possible to create a systematized way of discovering corner cases
efficiently in whatever the problem is?
Yes.
I think we have to get better at that.
Control theory has, for decades, talked about active experiment design.
How's that?
People call it curiosity these days.
It's roughly this idea of trying to exploration or exploitation, but in 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.
There's a parameter you want to learn about.
What is the optimal trajectory I could execute to learn about that parameter, for instance?
Bringing 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 on...
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 downtown New
Delhi all the time, an 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.
It's hard to write those algorithms.
Rare event simulation is just a really compelling notion if it's possible.
We joked 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.
That's the most...
Those are the most profound questions we ask of our world, like, what's the worst that
can happen?
But 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 and so
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
is 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 and at some
point, maybe collectively, they have enough data that their computational processes should
be set up differently than ours, right?
It's this vision of just, I mean, 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.
They enjoy fleet learning, and the reason it's hopeful for me is they're willing to
put up with something that's kind of dumb right now.
They're like, 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.
I mean, 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.
Also at 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.
Yeah.
There's a big group working on multiple groups working on home robotics.
It's a major part of the portfolio.
Awesome.
There are 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, or are we going to do the same
thing that you just mentioned that the 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?
Yeah.
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.
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 that you can do so much good with a robot for older
folks, but there's a gap to fill 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.
It's also with... At least when I talk to my parents about robots, there's a little bit
of a blank slate there too.
They don't know anything about robotics.
It's completely wide open.
My parents haven't seen Black Mirror.
It's a blank slate.
Here's a cool thing.
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.
Yeah.
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 of 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, 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.
I think the future is unwritten there.
But the other thing is...
I think ultimately you'll make things simpler if we embrace the softness of the world.
It makes things smoother, so 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 too.
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.
You can put a camera behind the skin, roughly, and get fantastic tactile information, which
is 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 off-board sensors are occluded.
Basically 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 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.
The YouTube...
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.
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 you 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.
Now, 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.
It shows up in all of the walking robots, where you have to use some of 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.
But 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.
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 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 in 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've 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, 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 the lab has been just the idea of chopping
onions 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, like 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 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 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 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.
Why as 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 the 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 of it kind of entered my mind.
The idea of under actuated is really compelling to see the, to see the world in this kind
of way.
Um, 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, uh, overreach, um, under actuated
is a way of life, man.
Exactly.
Um, I guess that's what I'm asking.
I do think it's everywhere.
I think some, in some places, um, 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, we just like an under actuated linear system is really not sufficiently distinct
from a fully actuated linear system.
It's, it's a, 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, um, 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.
Um, I have a second course now in that I teach in the other semesters, which is on, 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, um, 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, um, 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 maybe 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, um, reduce the need for that enveloping
grasps and the like, um, but I think the really good problems in manipulation.
So 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, butting 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.
Um, let me ask another ridiculous question on this topic.
Um, how important is touch, we haven't talked much about humans, but, uh, 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 or touch and smell, he says, but, um, so in terms
of robots, you know, connecting with humans, 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, uh, from an engineering perspective and a philosophical one.
I think it's super important.
Let's even just in a practical sense, if we forget about the emotional part of it, um,
but for robots to interact safely while they're doing meaningful mechanical work in the, in
the, uh, you know, close contact with or vicinity of people that need help.
I think we have to have them, they have, we have to build them differently.
Um, they have to be afraid, not afraid of touching the world.
So, uh, I think Baymax is just awesome.
That's just like the, the, the movie of big hero six and the, the concept of Baymax, that's
just awesome.
I think we should, um, and we have some folks at Toyota that are trying to Toyota research
that are trying to build Baymax roughly, and, uh, uh, I think it's just a fantastically
good project.
Um, I think it will change the way people physically interact the same way.
I mean, you, you gave a couple of examples earlier, but, but if I, um, 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, um, not quite gamify it, but somehow instead of people judging it and looking at
it as if, uh, 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, 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, uh, well, first of all, just,
uh, visiting your lab and seeing Atlas, uh, but also spot many when I first spot saw spot
many in person and hung out with him, her, uh, 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, uh, there's a magical moment, but
there's no touching.
Uh, I guess the question I have, have you ever been, um, 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, 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.
Um, I actually, I worry more about the smaller robots that could still, you know, move quickly
if programmed wrong and, uh, 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.
Um, I mean, I do think touch is so fundamental.
Ted Adelson is, uh, is great.
He's a perceptual scientist at MIT, uh, and he studied vision most of his life and he
said, uh, 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, um, watching his daughter, uh, convinced him that actually he needed to study
tactile sensing more.
So there's something very, um, important.
I think it's, it's a little bit also of the passive versus active, uh, part of the world,
right?
You can passively perceive the world.
Um, but it's fundamentally different if you can do an experiment, right?
And if you can change the world and 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, right?
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.
Uh, to me as a person who's captivated by the idea of human-robot interaction, it feels
like, um, such a rich opportunity to explore touch, not even from a safety perspective,
but like you said, the emotional to, I mean, safety comes first, um, but the next step is
like, you know, uh, like a real human connection.
Even in the war, like even in the industrial setting, it just feels like, uh, it's nice
for the robot.
I don't know.
I, you know, you, you might disagree with this, but, um, 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.
Uh, like it's convenient now to think about 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, I, I, it feels like, like an industrial robotic arm would be better if it has a human
element.
I think like rethink robotics had that idea with the Baxter and having eyes and so on
and having, I don't know, I'm a big believer in that.
I, I, it's not my area, but I am also a big believer.
Do you have an emotional connection to Alice?
Like, do you miss them?
I mean, um, yes, I, 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, um, yeah, I mean the, the, the robot, we basically
had to do heart surgery on the robot in the final competition because we melted the core.
And, uh, 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.
Um, those are all historic moments.
I think if you look back like a hundred years from now, and 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.
There's full of failure, but it feels like robotics will be important in the coming,
uh, a hundred years.
And these are the early days.
So, so I think a lot of people are, look at, uh, a brilliant person such as yourself and,
and are curious about the intellectual journey they've took.
Um, is there maybe three books, technical fiction, philosophical that, um, had a big
impact on your life that you would recommend perhaps others reading?
Yeah.
So, um, I actually didn't read that much as a kid, but I read fairly voraciously now.
Um, there are some recent books that if you're interested in this kind of topic, like AI
Superpowers by Kai-Fu Lee is just a fantastic read.
You must read that.
Um, Yuval Harari is just, I think that can open your mind.
Um, sapiens.
As, as the first one, homo deus, the second, yeah, I think we mentioned the black swan
by Talib.
I think that's a good sort of mind opener.
I actually, um, so, so there's maybe a more controversial recommendation I could give.
Um, great.
Well, I'd love to, in some sense, it's, it's so classical, it might surprise you.
But I actually recently read, um, Mortimer Adler's, uh, How to Read a Book.
Not so long ago.
It was a while ago, but, um, some people hate that book.
I loved it.
I think we're in this time right now where, um, boy, we're just inundated with research
papers that you could read on archive with limited peer review and just this wealth of
information.
Um, I don't know, I think the passion of, um, 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.
Um, but then once you find them, you should just dig in and, and, and understand it very
deeply and it's worth, you know, marking it up and, and, uh, you know, having the hard
copy, writing in the side notes, side margins, um, I think that was really, I read it at
the right time where I was just feeling just overwhelmed with really low quality stuff,
I guess.
Um, and similarly, uh, I'm just giving more than three now, I'm sorry if I've exceeded
my, my quota, but on that topic, just real quick is, uh, 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, um, what is it, formal 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, which a book that resonates with you might
not be the same book that resonates with me, but, um, when you really find one that resonates
with you, I think the dialogue that happens and that's what I loved that Adler was saying,
you know, I think Socrates and Plato say, um, the, the written word is never going to
capture the beauty of dialogue, right, but Adler says, no, no, um, a really good book
is a dialogue between you and the author and it crosses time and space and, uh, 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, is, is so good.
And similarly teaching, I, um, I thought a lot about teaching and, uh, and so Isaac
Asimov, great science fiction writer, it's 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 some, you know, I, I do really resonate with his
style and, uh, and just his way of talking about, you know, by communicating and explaining
to something is a really the way that you learn something.
I think 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, you know, 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, on these ideas in a way that if 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, watch, 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, um, I'm
trying to figure out, I think it's a new medium.
Do you think it's, yeah, I've been, um, I've been quite, um, cynical about, uh, human to
human connection over, 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, I, I, you
know, it is an opportunity to, to give that differently.
I can, I can deliver content directly into your browser.
You have a WebGL engine right there.
I could, I can throw 3D, uh, content into your browser while you're listening to me.
Right.
Yeah.
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, that's a, that's a new communication
tool that I didn't have last year, right.
And, uh, I think robotics can potentially benefit a lot from teaching that way.
We'll see.
It's going to be an experiment this fall thinking a lot about it.
Yeah.
It's also like, um, the, the length of lectures or the length of like, um, 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 as short as possible, uh, as short as 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, don't
get to go back and edit out parts like Chris, like a crisp it 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
quit.
It can make something special of a lecture.
I've, uh, I've seen that in myself and, and in others too, because 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, they're not helpful at all.
And once you remove them, it's just, I don't know, editing can be magic.
Uh, a lot of time.
Yeah.
It takes, it depends like what is teaching.
You have to ask, um, um, yeah, because 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, have you watched those videos?
It's, I mean, not all of them, but 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
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.
It 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, it's something I'm, um, trying to remove for myself.
I, there's, there's a guy, I mean, I learned from a lot of people, but really, um, there's
been a few people who has 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 too.
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 and I'm trying to
learn that these notes are, you see me looking down.
It'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 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 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, he's sitting back and
just enjoying the hell out of this conversation.
Not really understanding.
He kind of watched 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 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 because, 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.
Like there, um, that's one of the things I see, like we talked to 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 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 that's true.
It's, uh, it's easy to lose yourself in the, in the, in the distractions of the world.
You're overwhelmed with content right now, but you have to stop and pick some of it and,
and really understand it.
Yeah.
I've, 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 child-like
book about pigs and I was like, 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've returned to 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 to an archive and blog posts and GitHub and,
and the new thing and, of, um, you lose your ability to really master an idea.
Right.
Wow.
Exactly right.
What's the fond memory from childhood when baby Russ Tedrick?
Well, I guess I just said that, um, at least my current life begins, began when I got to
MIT.
If I have to go farther than that.
Yeah.
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, uh, you know, I, 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 how to like programming, um, but when I did get to MIT and started working with
Sebastian Song, theoretical physicist, computational neuroscientist, um, 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, um, somehow I think that's, that's got to be part of
it.
This.
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, it was working hard, but I was loving it.
Right.
I mean, there's, I think 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 or us have, 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 Ross Tedrick and thank you to our sponsors,
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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't, 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.