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’ll get into a lot of topics in this conversation

from his time leading MIT’s Delta Robotics Challenge team

to the awesome fact that he often runs

close to a marathon a day to and from work barefoot.

For a world class roboticist interested in elegant,

efficient control of underactuated dynamical systems

like the human body, this fact makes Russ

one of the most fascinating people I know.

Quick summary of the ads.

Three sponsors, Magic Spoon Cereal, BetterHelp,

and ExpressVPN.

Please consider supporting this podcast

by going to magicspoon.com slash lex

and using code lex at checkout,

going to betterhelp.com slash lex

and signing up at expressvpn.com slash lexpod.

Click the links in the description,

buy the stuff, get the discount.

It really is the best way to support this podcast.

If you enjoy this thing, subscribe on YouTube,

review it with five stars on Apple Podcast,

support it on Patreon, or connect with me

on Twitter at lexfreedman.

As usual, I’ll do a few minutes of ads now

and never any ads in the middle

that can break the flow of the conversation.

This episode is supported by Magic Spoon,

low carb keto friendly cereal.

I’ve been on a mix of keto or carnivore diet

for a very long time now.

That means eating very little carbs.

I used to love cereal.

Obviously, most have crazy amounts of sugar,

which is terrible for you, so I quit years ago,

but Magic Spoon is a totally new thing.

Zero sugar, 11 grams of protein,

and only three net grams of carbs.

It tastes delicious.

It has a bunch of flavors, they’re all good,

but if you know what’s good for you,

you’ll go with cocoa, my favorite flavor

and the flavor of champions.

Click the magicspoon.com slash lex link in the description,

use code lex at checkout to get the discount

and to let them know I sent you.

So buy all of their cereal.

It’s delicious and good for you.

You won’t regret it.

This show is also sponsored by BetterHelp,

spelled H E L P Help.

Check it out at betterhelp.com slash lex.

They figure out what you need

and match you with a licensed professional therapist

in under 48 hours.

It’s not a crisis line, it’s not self help,

it is professional counseling done securely online.

As you may know, I’m a bit from the David Goggins line

of creatures and still have some demons to contend with,

usually on long runs or all nighters full of self doubt.

I think suffering is essential for creation,

but you can suffer beautifully

in a way that doesn’t destroy you.

For most people, I think a good therapist can help in this.

So it’s at least worth a try.

Check out the reviews, they’re all good.

It’s easy, private, affordable, available worldwide.

You can communicate by text anytime

and schedule weekly audio and video sessions.

Check it out at betterhelp.com slash lex.

This show is also sponsored by ExpressVPN.

Get it at expressvpn.com slash lex pod

to get a discount and to support this podcast.

Have you ever watched The Office?

If you have, you probably know it’s based

on a UK series also called The Office.

Not to stir up trouble, but I personally think

the British version is actually more brilliant

than the American one, but both are amazing.

Anyway, there are actually nine other countries

with their own version of The Office.

You can get access to them with no geo restriction

when you use ExpressVPN.

It lets you control where you want sites

to think you’re located.

You can choose from nearly 100 different countries,

giving you access to content

that isn’t available in your region.

So again, get it on any device at expressvpn.com slash lex pod

to get an extra three months free

and to support this podcast.

And now here’s my conversation with Russ Tedrick.

What is the most beautiful motion

of an 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 gait walker.

That can still, you give it a little push,

it starts falling down a ramp.

It looks a little bit more like walking.

At least it’s a biped.

But what Steve and Andy,

and Tad McGeer started the whole exercise,

but what Steve and Andy did was they took it

to this beautiful conclusion

where they built something that had knees, arms, a torso.

The arms swung naturally, give it a little push.

And that looked like a stroll through the park.

How do you design something like that?

I mean, is that art or science?

It’s on the boundary.

I think there’s a science to getting close to the solution.

I think there’s certainly art in the way

that they made a beautiful robot.

But then the finesse, because they were working

with a system that wasn’t perfectly modeled,

wasn’t perfectly controlled,

there’s all these little tricks

that you have to tune the suction cups at the knees,

for instance, so that they stick,

but then they release at just the right time.

Or there’s all these little tricks of the trade,

which really are art, but it was a point.

I mean, it made the point.

We were, at that time, the walking robot,

the best walking robot in the world was Honda’s Asmo.

Absolutely marvel of modern engineering.

Is this 90s?

This was in 97 when they first released.

It sort of announced P2, and then it went through.

It was Asmo by then in 2004.

And 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.

Asmo is a beautiful machine.

It does walk with its knees bent.

Our Atlas walking had its knees bent.

But actually, Asmo 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.

And 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, right?

And you just have to give it a little bit of energy.

This one only walked down a ramp.

It would never walk on the flat.

To walk on the flat,

you have to give a little energy at some point.

But maybe instead of trying to take the forces imparted

to you by the world and replacing them,

what we should be doing is letting the world push us around

and we go with the flow.

Very zen, very zen robot.

Yeah, but okay, so that sounds very zen,

but I can also imagine how many like failed versions

they had to go through.

Like how many, like, 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 to get it?

I don’t know if it’s thousands, but it’s a lot.

It takes some patience.

There’s no question.

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

Oh, 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.

Got it.

So what about on the animal side,

the ones that figured out how to move efficiently?

Is there anything you find inspiring or beautiful

in the movement of any particular animal?

I do have a favorite example.

Okay.

So it sort of goes with the passive walking idea.

So is there, you know, how energy efficient are animals?

Okay, there’s a great series of experiments

by George Lauder at Harvard and Mike Tranofilo at MIT.

They were studying fish swimming in a water tunnel.

Okay.

And one of these, the type of fish they were studying

were these rainbow trout,

because there was a phenomenon well understood

that rainbow trout, when they’re swimming upstream

in mating season, they kind of hang out behind the rocks.

And it looks like, I mean,

that’s tiring work swimming upstream.

They’re hanging out behind the rocks.

Maybe there’s something energetically interesting there.

So they tried to recreate that.

They put in this water tunnel, a rock basically,

a cylinder that had the same sort of vortex street,

the eddies coming off the back of the rock

that you would see in a stream.

And they put a real fish behind this

and watched how it swims.

And the amazing thing is that if you watch from above

what the fish swims when it’s not behind a rock,

it has a particular 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 a human walks.

The fish has a characteristic gate.

You put that fish behind the rock, its gate changes.

And what they saw was that it was actually resonating

and kind of surfing between the vortices.

Now, here was the experiment that really was the clincher.

Because there was still, it wasn’t clear how much of that

was mechanics of the fish,

how much of that is control, the brain.

So the clincher experiment,

and maybe one of my favorites to date,

although there are many good experiments.

They took, this was now a dead fish.

They took a dead fish.

They put a string that went,

that tied the mouth of the fish to the rock

so it couldn’t go back and get caught in the grates.

And then they asked what would that dead fish do

when it was hanging out behind the rock?

And so what you’d expect, it sort of flopped around

like a dead fish in the vortex wake

until something sort of amazing happens.

And this video is worth putting in, right?

What happens?

The dead fish basically starts swimming upstream, right?

It’s completely dead, no brain, no motors, no control.

But it’s 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 give credit for that to?

Is that just evolution constantly just figuring out

by killing a lot of generations of animals,

like the most efficient motion?

Is that, or maybe the physics of our world completely like,

is like if 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.

That’s reasonable.

I mean, do they co evolve?

Yeah, somehow co, yeah.

Like I don’t know if an environment can evolve, but.

I mean, there are experiments that people do,

careful experiments that show that animals can adapt

to unusual situations and recover efficiency.

So there seems like at least in one direction,

I think there is reason to believe

that the animal’s 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.

Like 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, okay?

Gravity does the work.

You can put springs, I would call that mechanics, right?

If we have springs and dampers,

which our muscles are springs and dampers and tendons.

But then you have something that’s doing active work,

putting energy in, which are your motors on the robot.

The controller’s job is to send commands to the motor

that add new energy into the system, right?

So the mechanics and control interplay somewhere,

the divide is around, you know,

did you decide to send some commands to your motor

or did you just leave the motors off,

let them do their work?

Would you say is most of nature

on the dynamic side or the control side?

So like, if you look at biological systems,

we’re living in a pandemic now,

like, do you think a virus is a,

do you think it’s 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, right?

I mean, even our bodies, the mechanics of our bodies,

certainly with exercise, they evolve.

But so I actually, I lost a finger in early 2000s

and it’s my fifth metacarpal.

And 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, right?

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.

A bit.

I think we are the most successful bipeds.

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, you know,

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, yeah.

But probably, you know, the reason

that one pre Homo sapiens species versus another survived

was not because of whether they ate well

when there was lots of food.

But when the ice age came, you know,

probably one of them happened to be in the wrong place.

One of them happened to forage a food that was okay

even when the glaciers came or something like that, I mean.

There’s a million variables that contributed

and we can’t, and our, actually the amount of information

we’re working with and telling these stories,

these evolutionary stories is very little.

So yeah, just like you said, it seems like,

if you study history, it seems like history turns

on like these little events that otherwise

would seem meaningless, but in a grant,

like when you, in retrospect, were turning points.

Absolutely.

And that’s probably how like somebody got hit in the head

with a rock because somebody slept with the wrong person

back in the cave days and somebody get angry

and that turned, you know, warring tribes

combined with the environment, all those millions of things

and the meat eating, which I get a lot of criticism

because I don’t know 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 deeply unhealthy,

it’s wrong, but I just feel good doing it.

But you’re right, these evolutionary arguments

can be flawed, but is there anything interesting

to pull out for?

There’s a great book, by the way,

well, a series of books by Nicholas Taleb

about Fooled by Randomness and Black Swan.

Highly recommend them, but yeah,

they make the point nicely that probably

it was a few random events that, yes,

maybe it was someone getting hit by a rock, as you say.

That said, do you think, I don’t know how to ask this

question or how to talk about this,

but there’s something elegant and beautiful

about moving on two feet, obviously biased

because I’m human, but from a robotics perspective, 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 think the most, 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, right?

The other thing I had going for me is actually

that I wasn’t a runner before,

and I learned to run after I had learned

about barefoot running, or I mean,

started running longer distances.

So I didn’t have to unlearn.

And I’m definitely, I’m a big fan of it for me,

but I’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 longterm 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 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, 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 the pandemic,

or how to properly wash our hands.

We do it every day, but we haven’t really studied,

like, am I doing this correctly?

But running felt like one of those things,

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.

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 like 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, you adjust 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.

Can you, if they’re interested in trying,

what would you, 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 a, I mean, I notice my gait changing, right?

So, I mean, your foot has as many muscles

and sensors as your hand does, right?

Sensors, ooh, okay.

And we do amazing things with our hands.

And we stick our foot in a big, 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 are worried about getting cuts

or 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, and this, again,

to my wife’s chagrin, your feet get tough, right?

And, yeah, I can run over almost anything now.

I mean, what, can you talk about,

is there, like, is there tips or tricks

that you have, suggestions about,

like, if I wanted to try it?

You know, there is a good book, actually.

There’s probably more good books since I read them.

But Ken Bob, Barefoot Ken Bob Saxton.

He’s an interesting guy.

But I think his book captures the right way

to describe running, barefoot running,

to somebody better than any other I’ve seen.

So you run pretty good distances, and you bike,

and is there, you know, if we talk about bucket list items,

is there something crazy on your bucket list,

athletically, that you hope to do one day?

I mean, my commute is already a little crazy.

What are we talking about here?

What 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 many years, I’ve biked there.

Old ways?

Yeah, but normally I would try to run in

and then bike home, bike in, run home.

But you have run there and back before?

Sure.

Barefoot?

Yeah, or with minimal shoes or whatever that.

12, 12 times two?

Yeah.

Okay.

It became kind of a game of how can I get to work?

I’ve rollerbladed, I’ve done all kinds of weird stuff,

but my favorite one these days,

I’ve been taking the Charles River to work.

So, I can put in the rowboat not so far from my house,

but the Charles River takes a long way to get to MIT,

so I can spend a long time getting there.

And it’s not about, I don’t know, it’s just about,

I’ve had people ask me,

how can you justify taking that time?

But for me, it’s just a magical time to think,

to compress, decompress.

Especially, I’ll wake up, do a lot of work in the morning,

and then I kind of have to just let that settle

before I’m ready for all my meetings.

And then on the way home, it’s a great time to sort of

let that settle.

You lead a large group of people.

Is there days where you’re like,

oh shit, I gotta get to work in an hour?

Like, I mean, is there a tension there?

And like, if we look at the grand scheme of things,

just like you said, long term,

that meeting probably doesn’t matter.

Like, you can always say, I’ll just, I’ll run

and let the meeting happen, how it happens.

Like, what, how do you, that zen, how do you,

what do you do with that tension

between the real world saying urgently,

you need to be there, this is important,

everything is melting down,

how are we gonna fix this robot?

There’s this critical meeting,

and then there’s this, the zen beauty of just running,

the simplicity of it, you along with nature.

What do you do with that?

I would say I’m not a fast runner, particularly.

Probably my fastest splits ever was when

I had to get to daycare on time

because they were gonna charge me, you know,

some dollar per minute that I was late.

I’ve run some fast splits to daycare.

But those times are past now.

I think work, you can find a work life balance in that way.

I think you just have to.

I think I am better at work

because I take time to think on the way in.

So I plan my day around it,

and I rarely feel that those are really at odds.

So what, the bucket list item.

If we’re talking 12 times two, or approaching a marathon,

what, have you run an ultra marathon before?

Do you do races?

Is there, what’s a…

Not to win.

I’m not gonna like take a dinghy across the Atlantic

or something if that’s what you want.

But if someone does and wants to write a book,

I would totally read it

because I’m a sucker for that kind of thing.

No, I do have some fun things that I will try.

You know, I like to, when I travel,

I almost always bike to Logan Airport

and fold up a little folding bike

and then take it with me and bike to wherever I’m going.

And it’s taken me,

or I’ll take a stand up paddle board these days

on the airplane,

and then I’ll try to paddle around where I’m going

or whatever.

And I’ve done some crazy things, but…

But not for the, you know, I now talk,

I don’t know if you know who David Goggins is by any chance.

Not well, but yeah.

But I talk to him now every day.

So he’s the person who made me do this stupid challenge.

So he’s insane and he does things for the purpose

in the best kind of way.

He does things like for the explicit purpose of suffering.

Like he picks the thing that,

like whatever he thinks he can do, he does more.

So is that, do you have that thing in you or are you…

I think it’s become the opposite.

It’s a…

So you’re like that dynamical system

that the walker, the efficient…

Yeah, it’s leave no pain, right?

You should end feeling better than you started.

Okay.

But it’s mostly, I think, and COVID has tested this

because I’ve lost my commute.

I think I’m perfectly happy walking around town

with my wife and kids if they could get them to go.

And it’s more about just getting outside

and getting away from the keyboard for some time

just to let things compress.

Let’s go into robotics a little bit.

What to use the most beautiful idea in robotics?

Whether we’re talking about control

or whether we’re talking about optimization

and the math side of things or the engineering side of things

or the philosophical side of things.

I think I’ve been lucky to experience something

that not so many roboticists have experienced,

which is to hang out

with some really amazing control theorists.

And the clarity of thought

that some of the more mathematical control theory

can bring to even very complex, messy looking problems

is really, it really had a big impact on me

and I had a day even 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 about the ideas

that were flowing and the like.

And then I had a late afternoon meeting

with one of my favorite control theorists

and we went from these abstract discussions

about maybes and what ifs and what a great idea

to these super precise statements

about systems that aren’t that much more simple

or abstract than the ones I care about deeply.

And the contrast of that is,

I don’t know, it really gets me.

I think people underestimate

maybe the power of clear thinking.

And so for instance, deep learning is amazing.

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 how do we make sure people get a taste

of the more rigorous thinking

that I think goes along with some different approaches.

Yeah, so that’s really interesting.

So understanding like the fundamentals,

the first principles of the problem,

where 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

come from physics, and here it’s literally physics.

Yeah, and this applies, in deep learning,

this applies to 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 that work on autonomous vehicles

that work on autonomous vehicles that don’t study driving,

like deeply.

I might be coming a little bit from the psychology side,

but I remember I spent a ridiculous number of hours

at lunch, at this like lawn chair,

and I would sit somewhere in MIT’s campus,

there’s a few interesting intersections,

and we’d just watch people cross.

So we were studying pedestrian behavior,

and I felt like, as we record a lot of video,

to try, and then there’s the computer vision

extracts their movement, how they move their head, and so on,

but like every time, I felt like I didn’t understand enough.

I just, I felt like I wasn’t understanding

what, how are people signaling to each other,

what are they thinking,

how cognizant are they of their fear of death?

Like, what’s the underlying game theory here?

What are the incentives?

And then I finally found a live stream of an intersection

that’s like high def that I just, I would watch

so I wouldn’t have to sit out there.

But it’s interesting, so like, I feel.

But that’s tough, that’s a tough example,

because I mean, the learning.

Humans are involved.

Not just because human, but I think the learning mantra

is that basically the statistics of the data

will tell me things I need to know, right?

And, you know, for the example you gave

of all the nuances of, 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 that story.

I maybe even, one level more meta than what you’re saying.

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 I just don’t wanna lose it.

There are certainly super rigorous control,

or sorry, machine learning people.

I just think deep learning makes it so easy

to do some things that our next generation,

are not immediately rewarded

for going through some of the more rigorous approaches.

And then I wonder where that takes us.

Well, I’m actually optimistic about it.

I just want to do my part

to try to steer that rigorous thinking.

So there’s like two questions I wanna 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 how to practice rigorous thinking

in all the computer science disciplines that we’ve mentioned?

Yeah, I mean, there are times where problems

that can be solved with well known mature methods

could also be solved with a deep learning approach.

And 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 biased the system

and you’ve suddenly put a bottleneck in there

that is your own mental model.

But something like converting a matrix,

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 in that sense, rigorous thinking is understanding

the scope and the limitations of the methods that we have,

like how to use the tools of mathematics properly.

Yeah, I think taking a class on analysis

is all I’m sort of arguing is to take a chance to stop

and force yourself to think rigorously

about even the rational numbers or something.

It doesn’t have to be the end all problem.

But that exercise of clear thinking,

I think goes a long way,

and I just wanna make sure we keep preaching it.

We don’t lose it.

But do you think when you’re doing rigorous thinking

or maybe trying to write down equations

or sort of explicitly formally describe a system,

do you think we naturally simplify things too much?

Is that a danger you run into?

Like in order to be able to understand something

about the system mathematically,

we make it too much of a toy example.

But I think that’s the good stuff, right?

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 they had done a bunch of experiments

and they told the world,

here’s your weights of your neural network.

We’ve solved the problem.

Where would we be today?

I don’t think we’d be as far as we are.

There’s something to be said

about having the simplest explanation for a phenomenon.

So I don’t doubt that we can train neural networks

to predict even physical F equals MA type equations.

But 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, let’s not offend AI systems from 50 years

from now that are listening to this

that are probably better at,

might be better coming up

with F equals MA equations themselves.

So sorry, I actually think learning is probably a route

to achieving this, but the representation matters, right?

And I think having a function that takes my inputs

to outputs that is arbitrarily complex

may not be the end goal.

I think there’s still the most simple

or parsimonious explanation for the data.

Simple doesn’t mean low dimensional.

That’s one thing I think that we’ve,

a lesson that we’ve learned.

So a standard way to do model reduction

or system identification and controls

is 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 learning

that state dimension is not the right metric.

Of complexity.

But for me, I think a lot about contact,

the mechanics of contact,

if a robot hand is picking up an object or something.

And when I write down the equations of motion for that,

they look incredibly complex,

not because, 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,

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 him 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 tails 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 after the, 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 already, so it was decided

as 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.

Sure, called Atlas.

Atlas.

Humanoid robots.

Yeah, it was a 400 pound Marvel,

but a pretty big, scary looking robot.

Expensive too.

Expensive, yeah.

Okay, so I mean, how did you feel

at the prospect of this kind of challenge?

I mean, it seems 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,

like when you first look at the problem,

it’s on wheels, but like humanoid robots,

that sounds really hard.

So what are your, 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,

it 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 complex terrain

played a pretty big role in the competition.

So…

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 we would be able,

how far we’d 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

in every joint of the robot.

So, and then the question was,

and the gamesmanship of the organizers

was to figure out what we’re capable of,

push us as far as we could,

so that 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.

We had a bunch of algorithms that we were very happy with.

We wanted to see how far we could push them,

and this was a chance to really test our mettle

to do more proper software engineering.

So the team, we all just worked our butts off.

We were in that lab almost all the time.

Okay, so there were some, of course,

high highs and low lows throughout that.

Anytime you’re 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

and hanging limp on its harness,

and the humans were there picking up

and laying the brick down

so that the robot could walk over it.

And I thought, what is wrong with this?

We’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.

I think the investment that OSR FC,

whatever they were called at that time,

Brian Gerkey’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, if you do want to teleop,

it works way better if it’s at frame rate.

Oh, cool.

But 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 0.7.

And I remember getting this email.

And by the way, the perception folks on our team hated

that they knew that if my controller was too slow,

the robot was gonna 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 gonna either get a humanoid robot or we’re not.

And we get an email saying,

good news, we made the robot, the simulator faster.

It’s now at one point.

And I was just like, oh man, what are we gonna do here?

So that came in late at night for me.

A few days ahead.

I went over, it happened at Frank Permenter,

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 warm starting.

So we are solving a big optimization problem

at every time step.

But if you’re running fast enough,

the optimization problem you’re solving

on the last time step is pretty similar

to the optimization you’re gonna solve with the next.

We had course had told our commercial solver

to use warm starting, but even the interface

to that commercial solver was causing us these delays.

So what we did was we basically wrote,

we called it fast QP at the time.

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?

Or like?

I mean, so we always knew that if we fell back to,

if we, it got to the point where if for some reason

things slowed down and we fell back to the original solver,

the robot would actually literally fall down.

So it was a harrowing sort of edge we were,

ledge we were sort of on.

But I mean, it actually,

like the 400 pound human could come crashing to the ground

if your solver’s not fast enough.

But you know, we had lots of good experiences.

So can I ask you a weird question I get

about 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 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 like running.

People that push themselves to like the limit,

they also seem to be like the most like full of life

somehow.

And I get often criticized like,

you’re not getting enough sleep.

What are you doing to your body?

Blah, blah, blah, like this kind of stuff.

And I usually just kind of respond like,

I’m doing what I love.

I’m passionate about it.

I love it.

I feel like it’s, it’s invigorating.

I actually think, I don’t think the lack of sleep

is what hurts you.

I think what hurts you is stress and lack of doing things

that you’re passionate about.

But in this world, yeah, I mean,

can you comment about why the heck robotics people

are willing to push themselves to that degree?

Is there value in that?

And why are they so happy?

I think, 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 wanna 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 this semi autonomous system work?

I mean, it really did teach me something fundamental

about what it’s gonna take to get robustness

out of a system of this complexity.

I would say the DARPA challenge

really 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?

Like, what’s a loop of tests?

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

Yeah, I mean, I think there’s a whole philosophy to testing.

There’s the unit tests, and you can do that on a hardware,

you can do that in a small piece of code.

You write one function, you should write a test

that checks that function’s input and outputs.

You should also write an integration test

at the other extreme of running the whole system together,

where they 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, you know,

cause there’s an argument that the things that’ll get you

are the things that are really nuanced in the world.

And there’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.

Yep.

It’s 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 our,

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 against the 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?

Sorry to interrupt.

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…

Yeah, 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 humanoid.

And then they also provided the vehicle,

which was a little Polaris.

And the robot didn’t really fit in the car.

So you couldn’t drive the car with your feet

under the steering column.

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.

Yeah.

I’m very cognizant of it when I’m like injured

for whatever reason.

Oh, that’s really hard.

Yeah.

So how did you approach this problem?

So we had, you think of NASA’s operations,

and they have these checklists,

prelaunched 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 could 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 its 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 like take a step to recover,

they were all disabled because we were in the car

and there was 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 seat,

on the floor, we probably could have recovered from it.

But the thing that was bad that happened

is when we did that and we jostled a little bit,

the tailbone of our robot was only a little off the seat,

it hit the seat.

And the other foot came off the ground just a little bit.

And nothing in our plans had ever told us what to do

if your butt’s on the seat and your feet are in the air.

Feet 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 out of the robot.

But like into the destination.

That’s true, we fell in, we got our point for egress.

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

is there any hope for Atlas to be able to do something

when it’s just on its butt and feet in the air?

Absolutely.

So you can, what do you?

No, so that is one of the big challenges.

And I think it’s still true, you know,

Boston Dynamics and Antimal and there’s this incredible work

on legged robots happening around the world.

Most of them still are very good at the case

where you’re making contact with the world at your feet.

And they have typically point feet relatively,

they have balls on their feet, for instance.

If those robots get in a situation

where the elbow hits the wall or something like this,

that’s a pretty different situation.

Now they have layers of mechanisms that will make,

I think the more mature solutions have ways

in which the controller won’t do stupid things.

But a human, for instance, is able to leverage

incidental contact in order to accomplish a goal.

In fact, I might, if you push me,

I might actually put my hand out

and make a new brand new contact.

The feet of the robot are doing this on quadrupeds,

but we mostly in robotics are afraid of contact

on the rest of our body, which is crazy.

There’s this whole field of motion planning,

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 the scene is 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 wrestled my whole life.

So let me ask the question, 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 can win

in a wrestling match or in a fight against a human?

So we just mentioned sitting on your butt,

if you’re in the air, that’s a common position.

Jiu jitsu, when you’re on the ground,

you’re a 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.

Not very.

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, that’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, 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 gonna 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 subtle to you.

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.

Well, the, I mean, one of the most amazing things to me

about the, we can talk about martial arts.

We could also talk about dancing.

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, like, and I can feel the learning.

Like I’m a huge believer in drilling of techniques

and you can just like feel your, I don’t,

you’re not feeling, you’re feeling, 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 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 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, 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 balance components

that you just weren’t even aware of before.

So how do you scaffold that?

Yeah, plus the fear of injury,

the ambition of goals, of excelling,

and fear of mortality.

Let’s see, what else is in there?

As the motivations, overinflated ego in the beginning,

and then a crash of confidence in the middle.

All of those seem to be essential for the learning process.

And if all that’s good,

then you’re probably optimizing energy efficiency.

Yeah, right, so we have to get that right.

So there was this idea that you would have robots

play soccer better than human players by 2050.

That was the goal.

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 gonna see that first?

Or a robot, if you’re familiar,

there’s an organization called UFC for mixed martial arts.

Are we gonna see a world cup championship soccer team

that have robots, or a UFC champion mixed martial artist

as a robot?

I mean, it’s very hard to say one thing is harder,

some problem is 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,

like especially the humanoid robots

trying to play soccer.

I mean, they’re kind of terrible right now.

I mean, I guess there is robo sumo wrestling.

There’s like the robo one competitions,

where they do have these robots that go on the table

and basically fight.

So maybe I’m wrong, maybe.

First of all, do you have a year in mind for RoboCup,

just from a robotics perspective?

Seems like a super exciting possibility

that like in the physical space,

this is what’s interesting.

I think the world is captivated.

I think it’s really exciting.

It 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 game over.

So it’s very subtle on what’s fair.

I think the fighting one is a weird one.

Yeah, because you’re talking about a machine

that’s much stronger than you.

But yeah, in terms of soccer, basketball, all those kinds.

Even soccer, right?

I mean, as soon as there’s contact or whatever,

and there 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 gonna get the perfect soccer player robot.

You’re gonna get something that exploits the rules,

exploits its super actuators, its super low bandwidth

feedback loops or whatever, and it’s gonna 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 Monday

or whatever I was telling him 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, I’ve shown videos like this a lot of times,

what happened?

And it turns out that this video had gone,

this black mirror episode had changed

the way people watched 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.

I almost wanna say that they almost 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 wanna simplify it, but it feels like

they talk about it so often.

It almost, there does seem to be an addictive quality to it.

I talked to a guy, a guy named Joe Rogan,

who’s kind of the flag bearer

for being terrified at these robots.

Do you have two questions?

One, do you have an understanding

of why people are afraid of robots?

And the second question is in black mirror,

just to tell you the episode,

I don’t even remember it that much anymore,

but these robots, I think they can shoot

like a pellet or something.

They basically have, it’s basically a spot with a gun.

And how far are we away from having robots

that go rogue like that?

Basically spot that goes rogue for some reason

and somehow finds a gun.

Right, so, I mean, I’m not a psychologist.

I think, I don’t know exactly why

people react the way they do.

I think we have to be careful about the way robots influence

our society and the like.

I think that’s something, that’s a responsibility

that roboticists need to embrace.

I don’t think robots are gonna come after me

with a kitchen knife or a pellet gun right away.

And I mean, if they were programmed in such a way,

but I used to joke with Atlas that all I had to do

was run for five minutes and its battery would run out.

But actually they’ve got to be careful

and actually they’ve got a very big battery

in there by the end.

So it was over an hour.

I think the fear is a bit cultural though.

Cause 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

in different countries, right?

So I don’t know if it’s a human innate fear of metal marvels

or if it’s something that we’ve done to ourselves

with our sci fi.

Yeah, the stories we tell ourselves through movies,

through just through popular media.

But if I were to tell, you know, if you were my therapist

and I said, I’m really terrified that we’re going

to have these robots very soon that will hurt us.

Like, how do you approach making me feel better?

Like, why shouldn’t people be afraid?

There’s a, I think there’s a video

that went viral recently.

Everything, everything was spot in Boston,

which goes viral in general.

But usually it’s like really cool stuff.

Like they’re doing flips and stuff

or like sad stuff, the Atlas being hit with a broomstick

or something like that.

But there’s a video where I think one of the new productions

bought robots, which are awesome.

It was like patrolling somewhere in like in some country.

And like people immediately were like saying like,

this is like the dystopian future,

like the surveillance state.

For some reason, like you can just have a camera,

like something about spot being able to walk on four feet

with like really terrified people.

So like, what do you say to those people?

I think there is a legitimate fear there

because so much of our future is uncertain.

But at the same time, technically speaking,

it seems like we’re not there yet.

So what do you say?

I mean, I think technology is complicated.

It can be used in many ways.

I think there are purely software attacks

that somebody could use to do great damage.

Maybe they have already, you know,

I think wheeled robots could be used in bad ways too.

Drones.

Drones, right, I don’t think that, let’s see.

I don’t want to be building technology

just because I’m compelled to build technology

and I don’t think about it.

But I would consider myself a technological optimist,

I guess, in the sense that I think we should continue

to create and evolve and our world will change.

And if we will introduce new challenges,

we’ll screw something up maybe,

but I think also we’ll invent ourselves

out of those challenges and life will go on.

So it’s interesting because you didn’t mention

like this is technically too hard.

I don’t think robots are, I think people attribute

a robot that looks like an animal

as maybe having a level of self awareness

or consciousness or something that they don’t have yet.

Right, so it’s not, I think our ability

to anthropomorphize those robots is probably,

we’re assuming that they have a level of intelligence

that they don’t yet have.

And that might be part of the fear.

So in that sense, it’s too hard.

But, you know, there are many scary things in the world.

Right, so I think we’re right to ask those questions.

We’re right to think about the implications of our work.

Right, in the short term as we’re working on it for sure,

is there something long term that scares you

about our future with AI and robots?

A lot of folks from Elon Musk to Sam Harris

to a lot of folks talk about the existential threats

about artificial intelligence.

Oftentimes, robots kind of inspire that the most

because of the anthropomorphism.

Do you have any fears?

It’s an important question.

I actually, I think I like Rod Brooks answer

maybe the best on this, I think.

And it’s not the only answer he’s given over the years,

but maybe one of my favorites is he says,

it’s not gonna be, he’s got a book,

Flesh and Machines, I believe, it’s not gonna be

the robots versus the people,

we’re all gonna be robot people.

Because, you know, we already have smartphones,

some of us have serious technology implanted

in our bodies already, whether we have a hearing aid

or a pacemaker or anything like this,

people with amputations might have prosthetics.

And that’s a trend I think that is likely to continue.

I mean, this is now wild speculation.

But I mean, when do we get to cognitive implants

and the like, and.

Yeah, with neural link, brain computer interfaces,

that’s interesting.

So there’s a dance between humans and robots

that’s going to be, it’s going to be impossible

to be scared of the other out there, the robot,

because the robot will be part of us, essentially.

It’d be so intricately sort of part of our society that.

Yeah, and it might not even be implanted part of us,

but just, it’s so much a part of our, yeah, our society.

So in that sense, the smartphone is already the robot

we should be afraid of, yeah.

I mean, yeah, and all the usual fears arise

of the misinformation, the manipulation,

all those kinds of things that,

the problems are all the same.

They’re human problems, essentially, it feels like.

Yeah, I mean, I think the way we interact

with each other online is changing the value we put on,

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 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 in the people and they’re doing magic with it.

I don’t care if the, I mean, I guess is that their company,

they have to make money, right?

But they’re already doing incredible work

and inspiring the world about technology.

I mean, do you have thoughts about Boston Dynamics

and maybe others, your own work in robotics

and inspiring the world in that way?

I completely agree, I think Boston Dynamics

is absolutely awesome.

I think I show my kids those videos, you know,

and the best thing that happens is sometimes

they’ve already seen them, you know, right?

I think, I just think it’s a pinnacle of success

in robotics that is just one of the best things

that’s happened, 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 like went through hell just to arrive

at a Roomba to figure out one product.

And then there’s so many home robotics companies

like Jibo 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 have anything hopeful to say

about the possibility of making money with robots?

Oh, I think you can’t just look at the failures.

I mean, Boston Dynamics is a success.

There’s lots of companies that are still doing amazingly

good work in robotics.

I mean, this is the capitalist ecology or something, right?

I think you have many companies, you have many startups

and they push each other forward and many of them fail

and some of them get through and that’s sort of

the natural way of those things.

I don’t know that is robotics really that much worse.

I feel the pain that you feel too.

Every time I read one of these, sometimes it’s friends

and I definitely wish it went better 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 or it?

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 Skydio R1, 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.

And again, I’ll forever be grateful for that.

And there’s something magical about the anthropomorphization

of that arm, it adds 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.

There’s been a, like 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 can be controlled.

I like that, yeah, I think that’s right.

I think there’s something that humans seem to do

or maybe my dangerous introspection is,

I think we are able to make very simple models

that assume a lot about the world very quickly.

And then it takes us a lot more time, like you’re wrestling.

You probably thought you knew what you were doing

with wrestling and you were fairly functional

as a complete wrestler.

And then you slowly got more expertise.

So maybe it’s natural that our first level of defense

against seeing a new robot is to think of it

in our existing models of how humans and animals behave.

And it’s just, as you spend more time with it,

then you’ll develop more sophisticated models

that will appreciate the differences.

Exactly.

Can you say what does it take to control a robot?

Like what is the control problem of a robot?

And in general, what is a robot in your view?

Like how do you think of this system?

What is a robot?

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 what 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 of automation that fails frequently.

Something like, it’s the computation plus mechanical work

and an unsolved problem.

It’s an unsolved 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,

you know, 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 gonna walk or pick things up with its hands, right?

My passion has always been for the places

where you’re interacting more,

you’re 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,

right, 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.

So you mentioned 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 change only by a small amount.

And there’s a feedback mechanism here.

That’s what we’re talking about.

And there’s a feedback mechanism.

And thinking about this as locally,

like a linear system, for instance,

I can use more linear algebra tools

to study systems like that,

generalizations of linear algebra to these smooth systems.

What is contact?

The robot has something very discontinuous

that happens when it makes or breaks,

when it starts touching the world.

And even the way it touches or the order of contacts

can change the outcome in potentially unpredictable ways.

Not unpredictable, but complex ways.

I do think there’s a little bit of,

a lot of people will say that contact is hard in robotics,

even to simulate.

And I think there’s a little bit of a,

there’s truth to that,

but maybe a misunderstanding around that.

So what is limiting is that when we think about our robots

and we write our simulators,

we often make an assumption that objects are rigid.

And when it comes down, that their mass moves all,

stays in a constant position relative to each other itself.

And that leads to some paradoxes

when you go to try to talk about

rigid body mechanics and contact.

And so for instance, if I have a three legged stool

with just 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’ll 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 gonna be.

And so you can’t actually tell,

the laws just aren’t explicit yet

about which way the table’s gonna 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 kinds 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.

Like fighter jets, we haven’t been as successful

writing those controllers for manipulation.

And so you don’t know what’s going to happen

at the point of contact, at the moment of contact.

There are situations absolutely

where 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 you can…

And those aren’t continuous, I mean, what…

So when you’re saying like 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 philosophy.

In philosophy, for instance,

especially if you come to rest or…

That’s so fascinating.

Okay, so what do you do?

Sorry, I interrupted you.

It’s fine.

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 a contact configuration?

What like, 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 gait

where the heel strikes and then the front toe strikes,

and then you can heel up, toe off.

Those are each different contact configurations.

I only had two different contacts,

but I ended up with four different contact configurations.

Now, of course, my robot might actually have bumps on it

or other things,

so it could be much more subtle than that, right?

But it’s just even with one sort of box

interacting with the ground already in the plane

has that many, right?

And if I was just even a 3D foot,

then 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 touched the ground

within 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.

I know people often say that we don’t,

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, where does, I believe you’ve written, Drake.

There’s a team of people.

I certainly spent a lot of hours on it myself.

But what is Drake and 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, then you have to,

it asks you to describe a little bit more about the system.

If you have any state, for instance, in the system,

any variables that are gonna persist,

you have to declare them.

Parameters can be declared and the like,

but the advantage of doing that is that you can,

if you like, run things all on one process,

but you can also do control design against it.

You can do, I mean, simple things like rewinding

and playing back your simulations, for instance,

these things, you get some rewards

for spending a little bit more upfront cost

in describing each system.

And I was inspired to do that

because I think the complexity of Atlas, for instance,

is just so great.

And I think, although, I mean,

ROS has been an incredible, absolutely huge fan

of what it’s done for the robotics community,

but the ability to rapidly put different pieces together

and have a functioning thing is very good.

But I do think that it’s hard to think clearly

about a bag of disparate parts,

Mr. Potato Head kind of software stack.

And if you can 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.

And then one of those systems, the last thing,

I said the first two things that Drake is,

but the last thing is that there is a set

of multi body equations, rigid body equations,

that is trying to provide a system that simulates physics.

And we also have renderers and other things,

but I think the physics component of Drake is special

in the sense that we have done excessive amount

of engineering to make sure

that we’ve written the equations correctly.

Every possible tumbling satellite or spinning top

or anything that we could possibly write as a test is tested.

We are making some, I think, fundamental improvements

on the way you simulate contact.

Just what does it take to simulate contact?

I mean, it just seems,

I mean, there’s something just beautiful

to the way you were like explaining contact

and you were like tapping your fingers

on the table while you’re doing it, just.

Easily, right?

Easily, just like, just not even like,

it was like helping you think, I guess.

So you have this like awesome demo

of loading or unloading a dishwasher,

just picking up a plate,

or grasping it like for the first time.

That’s just seems like so difficult.

What, 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 these, 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 right up

as a part of my, the end of my sabbatical, I guess.

So TRI has given me, the TRI robotics effort

has made this investment in simulation in Drake.

And Michael Sherman leads a team there

of just absolutely top notch dynamics experts

that are trying to write those simulators

that can pick up the dishes.

And there’s also a team working on manipulation there

that is taking problems like loading the dishwasher.

And we’re using that to study these really hard corner cases

kind of problems in manipulation.

So for me, this, you know, 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 like,

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?

Do we, is it enough to just try to add that

to our distribution and understand that on average,

we’re gonna cover that situation again?

There’s like really subtle questions at the corner cases

that I think we don’t yet have satisfying answers for.

Like how do you find the corner cases?

That’s one kind of, is there,

do you think that’s possible to create a systematized way

of discovering corner cases efficiently?

Yes.

In whatever the problem is?

Yes, I mean, I think we have to get better at that.

I mean, control theory has for decades

talked about active experiment design.

What’s that?

So 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, is more specific.

You could try to understand the uncertainty in your system,

design the experiment that will provide

the maximum information to reduce that uncertainty.

If there’s a parameter you wanna learn about,

what is the optimal trajectory I could execute

to learn about that parameter, for instance.

Scaling that up to something that has a deep network

in the loop and a planning in the loop is tough.

We’ve done some work on, you know,

with Matt Okely and Aman Sinha,

we’ve worked on some falsification algorithms

that are trying to do rare event simulation

that try to just hammer on your simulator.

And if your simulator is good enough,

you can spend a lot of time,

or you can write good algorithms

that try to spend most of their time in the corner cases.

So you basically imagine you’re building an autonomous car

and you wanna put it in, I don’t know,

downtown New Delhi all the time, right?

And accelerated testing.

If you can write sampling strategies,

which figure out where your controller’s

performing badly in simulation

and start generating lots of examples around that.

You know, it’s just the space of possible places

where that can be, where things can go wrong is very big.

So it’s hard to write those algorithms.

Yeah, rare event simulation

is just a really compelling notion, if it’s possible.

We joked and we call it the black swan generator.

It’s a black swan.

Because you don’t just want the rare events,

you want the ones that are highly impactful.

I mean, that’s the most,

those are the most sort of 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 like computer science,

worst case analysis.

We’re asking like, what are the millions of ways

this can go wrong?

And that’s like our curiosity.

And we humans, I think are pretty bad at,

we just like run into it.

And 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.

There’s some.

I think that’s gonna be important for robots too.

I mean, 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, right?

There’s just a, it’s very hard for one robot

to experience all the things.

But that’s not actually the problem we have to solve, right?

We’re gonna 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.

I mean, that robot dropping a plate

and a human looking at the robot probably pissed off.

Yeah.

But that’s a special moment to record.

I think one thing in terms of fleet learning,

and I’ve seen that because I’ve talked to a lot of folks,

just like Tesla users or Tesla drivers,

they’re another company

that’s using this kind of fleet learning idea.

One hopeful thing I have about humans

is they really enjoy when a system improves, learns.

So they enjoy fleet learning.

And the reason it’s hopeful for me

is they’re willing to put up with something

that’s kind of dumb right now.

And they’re like, if it’s improving,

they almost like enjoy being part of the, like teaching it.

Almost like if you have kids,

like you’re teaching them something, right?

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.

That’s why home robotics is really exciting.

And just to clarify, I mean, for people who might not know,

I mean, TRI, Toyota Research Institute.

So they’re, I mean, they’re pretty well known

for like autonomous vehicle research,

but they’re also interested in home robotics.

Yep, there’s a big group working on,

multiple groups working on home robotics.

It’s a major part of the portfolio.

There’s also a couple other projects

in advanced materials discovery,

using AI and machine learning to discover new materials

for car batteries and the like, for instance, yeah.

And that’s been actually an incredibly successful team.

There’s new projects starting up too, so.

Do you see a future of where like robots are in our home

and like robots that have like actuators

that look like arms in our home

or like, you know, more like humanoid type robots?

Or is this, are we gonna do the same thing

that you just mentioned that, you know,

the dishwasher is no longer a robot.

We’re going to just not even see them as robots.

But I mean, 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, you know, Alexis or Google Homes

on our kitchen counter.

It’s only a matter of time until they spring arms

and start doing something useful like that.

So I do think it’s coming.

I think lots of people have lots of motivations

for doing it.

It’s been super interesting actually learning

about Toyota’s vision for it,

which is about helping people age in place.

Cause 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.

And so I think there’s a real opportunity.

It’s a delicate problem.

How do you work with people, help people,

keep them active, engaged, you know,

but improve their 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.

So there’s a division that’s interesting.

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.

And it’s also with, at least with,

like when I talked to my parents about robots,

there’s a little bit of a blank slate there too.

Like you can, I mean, they don’t know anything

about robotics, so it’s completely like wide open.

They don’t have, they haven’t,

my parents haven’t seen Black Mirror.

So like they, it’s a blank slate.

Here’s a cool thing, like what can it do for me?

Yeah, so 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 gonna really pop.

So I don’t think we talked about it,

but what’s soft robotics?

So we talked about like rigid bodies.

Like if we can just linger on this whole touch thing.

Yeah, so what’s soft robotics?

So I told you that I really dislike the fact

that robots are afraid of touching the world

all over their body.

So there’s a couple reasons for that.

If you look carefully at all the places

that robots actually do touch the world,

they’re almost always soft.

They have some sort of pad on their fingers

or a rubber sole on their foot.

But if you look up and down the arm,

we’re just pure aluminum or something.

So 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, right?

You turn point contacts into patch contacts,

which can have torsional friction.

You can have distributed load.

If I wanna pick up an egg, right?

If I pick it up with two points,

then in order to put enough force

to sustain the weight of the egg,

I might have to put a lot of force to break the egg.

If I envelop it with contact all around,

then I can distribute my force across the shell of the egg

and have a better chance of not breaking it.

So 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 because of the, I think our world

and our mathematics has biased us towards rigid.

I see.

But it really should make things better in some ways, right?

I think the future is unwritten there.

But the other thing it can do.

I think ultimately, sorry to interrupt,

but I think ultimately it will make things simpler

if we embrace the softness of the world.

It makes things smoother, right?

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.

So if you change your hardware and make it more soft,

then you can potentially have a tactile sensor,

which is measuring the deformation.

So 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, it’s super important.

Like 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 get a lot of information

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.

So in all the part that really matters,

all of your off board sensors are occluded.

And really, if you don’t have tactile information,

then you’re blind in an important way.

So it happens that soft robotics and tactile sensing

tend to go hand in hand.

I think we’ve kind of talked about it,

but you taught a course on underactuated robotics.

I believe that was the name of it, actually.

That’s right.

Can you talk about it in that context?

What is underactuated robotics?

Right, so underactuated robotics is my graduate course.

It’s online mostly now,

in the sense that the lectures.

Several versions of it, I think.

Right, 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.

No, I’ve poured my heart into that class.

And lecture one is basically explaining

what the word underactuated means.

So people are very kind to show up

and then maybe have to learn

what the title of the course means

over the course of the first lecture.

That first lecture is really good.

You should watch it.

Thanks.

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.

So what do I mean by underactuated?

So 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.

So 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, Ross, if you had more research funding,

would you work on fully actuated robots?

Yeah.

And 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.

All right, I can jump into the air,

and there’s no motor that connects my center of mass

to the ground in that case.

So I have to think about the implications

of not having control over everything.

The passive dynamic walkers are the extreme view of that,

where you’ve taken away all the motors,

and you have to let physics do the work.

But it shows up in all of the walking robots,

where you have to use some of the actuators

to push and pull even the degrees of freedom

that you don’t have an actuator on.

That’s referring to walking if you’re falling forward.

Is there a way to walk that’s fully actuated?

So 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, right?

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, right?

But I can still do most of the things that I want to.

So you can get away with basically thinking of the system

as fully actuated,

unless you suddenly needed to accelerate down super fast.

But as soon as I take a step,

I get into the 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, okay?

What are the degrees of freedom of my shirt, right?

I suddenly, that’s a hard question to think about.

It kind of makes me queasy

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 some of the,

it breaks a lot of the foundational control stuff

that I’ve been thinking about.

Is there, what are some interesting insights

you could say about trying to solve an underactuated,

a control in an underactuated system?

So I think the philosophy there

is let physics do more of the work.

The technical approach has been optimization.

So you typically formulate your decision making

for control as an optimization problem.

And you use the language of optimal control

and sometimes often numerical optimal control

in order to make those decisions and balance,

these complicated equations of,

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 in our,

at least in our line of work.

And there’s some, 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 like you’ve, there’s a perception there,

there’s a feedback somehow.

Do you ever loop in like 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.

Sirtash Karaman was on that project

and we had, it was a really fun team to work on.

He’s carried it farther, much farther forward since then.

And that’s using cameras for perception?

So that was using cameras.

That was, 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,

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 always,

Very noisy, you mean, or?

It’s, 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 here,

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 lab

has been just the idea of chopping onions, okay?

Or carrots, turns out to be better.

So onions stink up the lab.

And they’re hard to see in a camera.

But so,

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 or an orientation in 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 a,

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, some.

And that’s what I worry about saying compressed

because it doesn’t,

I don’t mind that it’s low dimensional or not,

but it has to be something that’s easier to think about.

By us humans.

Or my algorithms.

Or the algorithms being like control, optimal.

So for instance, if the contact mechanics

of all of those onion pieces and all the permutations

of possible touches between those onion pieces,

you know, you can give me

a high dimensional state representation,

I’m okay if it’s linear.

But if I have to think about all the possible

shattering combinatorics of that,

then my robot’s gonna sit there thinking

and the soup’s gonna get cold or something.

So since you taught the course,

it kind of entered my mind,

the idea of underactuated as really compelling

to see the world in this kind of way.

Do you ever, you know, if we talk about onions

or you talk about the world with people in it in general,

do you see the world as basically an underactuated system?

Do you like often look at the world in this way?

Or is this overreach?

Underactuated is a way of life, man.

Exactly, I guess that’s what I’m asking.

I do think it’s everywhere.

I think in some places,

we already have natural tools to deal with it.

You know, it rears its head.

I mean, in linear systems, it’s not a problem.

We just, like an underactuated linear system

is really not sufficiently distinct

from a fully actuated linear system.

It’s a subtle point about when that becomes a bottleneck

in what we know how to do with control.

It happens to be a bottleneck,

although we’ve gotten incredibly good solutions now,

but for a long time that I felt

that that was the key bottleneck in legged robots.

And roughly now the underactuated course

is me trying to tell people everything I can

about how to make Atlas do a backflip, right?

I have a second course now

that I teach in the other semesters,

which is on manipulation.

And that’s where we get into now more of the,

that’s a newer class.

I’m hoping to put it online this fall completely.

And that’s gonna have much more aspects

about these perception problems

and the state representation questions,

and then how do you do control.

And the thing that’s a little bit sad is that,

for me at least, is there’s a lot of manipulation tasks

that people wanna do and should wanna do.

They could start a company with it and be very successful

that don’t actually require you to think that much

about underact, or dynamics at all even,

but certainly underactuated dynamics.

Once I have, if I reach out and grab something,

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 we’ve built systems that kind of reduce the need for that.

Enveloping grasps and the like.

But I think the really good problems in manipulation.

So manipulation, by the way, is more than just pick and place.

That’s like a lot of people think of that, just grasping.

I don’t mean that.

I mean buttoning my shirt, I mean tying shoelaces.

How do you program a robot to tie shoelaces?

And not just one shoe, but every shoe, right?

That’s a really good problem.

It’s tempting to write down like the infinite dimensional

state of the laces, that’s probably not needed

to write a good controller.

I know we could hand design a controller that would do it,

but I don’t want that.

I want to understand the principles that would allow me

to solve another problem that’s kind of like that.

But I think if we can stay pure in our approach,

then the challenge of tying anybody’s shoes

is a great challenge.

That’s a great challenge.

I mean, and the soft touch comes into play there.

That’s really interesting.

Let me ask another ridiculous question on this topic.

How important is touch?

We haven’t talked much about humans,

but I have this argument with my dad

where like I think you can fall in love with a robot

based on language alone.

And he believes that touch is essential.

Touch and smell, he says.

But so in terms of robots, 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 from an engineering perspective

and a philosophical one?

I think it’s super important.

Even just in a practical sense,

if we forget about the emotional part of it.

But for robots to interact safely

while they’re doing meaningful mechanical work

in the close contact with or vicinity of people

that need help, I think we have to have them,

we have to build them differently.

They have to be afraid, not afraid of touching the world.

So I think Baymax is just awesome.

That’s just like the movie of Big Hero 6

and the concept of Baymax, that’s just awesome.

I think we should, and we have some folks at Toyota

that are trying to, Toyota Research

that are trying to build Baymax roughly.

And I think it’s just a fantastically good project.

I think it will change the way people physically interact.

The same way, I mean, you gave a couple examples earlier,

but if the robot that was walking around my home

looked more like a teddy bear

and a little less like the Terminator,

that could change completely the way people perceive it

and interact with it.

And maybe they’ll even wanna teach it, like you said, right?

You could not quite gamify it,

but somehow instead of people judging it

and looking at it as if it’s not doing as well as a human,

they’re gonna try to help out the cute teddy bear, right?

Who knows, but I think we’re building robots wrong

and being more soft and more contact is important, right?

Yeah, I mean, like all the magical moments

I can remember with robots,

well, first of all, just visiting your lab and seeing Atlas,

but also Spotmini, when I first saw Spotmini in person

and hung out with him, her, it,

I don’t have trouble engendering robots.

I feel the robotics people really say, oh, is it it?

I kinda like the idea that it’s a her or a him.

There’s a magical moment, but there’s no touching.

I guess the question I have, have you ever been,

like, have you had a human robot experience

where a robot touched you?

And like, it was like, wait,

like, was there a moment that you’ve forgotten

that a robot is a robot and like,

the anthropomorphization stepped in

and for a second you forgot that it’s not human?

I mean, I think when you’re in on the details,

then we, of course, anthropomorphized our work with Atlas,

but in verbal communication and the like,

I think we were pretty aware of it

as a machine that needed to be respected.

And I actually, I worry more about the smaller robots

that could still move quickly if programmed wrong

and we have to be careful actually

about safety and the like right now.

And that, if we build our robots correctly,

I think then those, a lot of those concerns could go away.

And we’re seeing that trend.

We’re seeing the lower cost, lighter weight arms now

that could be fundamentally safe.

I mean, I do think touch is so fundamental.

Ted Adelson is great.

He’s a perceptual scientist at MIT

and he studied vision most of his life.

And he said, when I had kids,

I expected to be fascinated by their perceptual development.

But what really, what he noticed was,

felt more impressive, more dominant

was the way that they would touch everything

and lick everything.

And pick things up, stick it on their tongue and whatever.

And he said, watching his daughter convinced him

that actually he needed to study tactile sensing more.

So there’s something very important.

I think it’s a little bit also of the passive

versus active part of the world, right?

You can passively perceive the world.

But it’s fundamentally different if you can do an experiment

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 I think that’s a very different problem

than if you’re a robot that can apply force and touch.

I think it’s important.

Yeah, I think it’s just an exciting area of research.

I think you’re probably right

that this hasn’t been under researched.

To me as a person who’s captivated

by the idea of human robot interaction,

it feels like such a rich opportunity to explore touch.

Not even from a safety perspective,

but like you said, the emotional too.

I mean, safety comes first,

but the next step is like a real human connection.

Even in the industrial setting,

it just feels like it’s nice for the robot.

I don’t know, you might disagree with this,

but because I think it’s important

to see robots as tools often,

but I don’t know,

I think they’re just always going to be more effective

once you humanize them.

Like it’s convenient now to think of them as tools

because we want to focus on the safety,

but I think ultimately to create like a good experience

for the worker, for the person,

there has to be a human element.

I don’t know, for me,

it feels like 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,

having, I don’t know, I’m a big believer in that.

It’s not my area, but I am also a big believer.

Do you have an emotional connection to Atlas?

Like do you miss him?

I mean, yes, I don’t know if I more so

than if I had a different science project

that I’d worked on super hard, right?

But yeah, I mean, the robot,

we basically had to do heart surgery on the robot

in the final competition because we melted the core.

Yeah, there was something about watching that robot

hanging there.

We know we had to compete with it in an hour

and it was getting its guts ripped out.

Those are all historic moments.

I think if you look back like a hundred years from now,

yeah, I think those are important moments in robotics.

I mean, these are the early days.

You look at like the early days

of a lot of scientific disciplines.

They look ridiculous, they’re full of failure,

but it feels like robotics will be important

in the coming a hundred years.

And these are the early days.

So I think a lot of people are,

look at a brilliant person such as yourself

and are curious about the intellectual journey they’ve took.

Is there maybe three books, technical, fiction,

philosophical that had a big impact on your life

that you would recommend perhaps others reading?

Yeah, so I actually didn’t read that much as a kid,

but I read fairly voraciously now.

There are some recent books that if you’re interested

in this kind of topic, like AI Superpowers by Kai Fu Lee

is just a fantastic read.

You must read that.

Yuval Harari is just, I think that can open your mind.

Sapiens.

Sapiens is the first one, Homo Deus is the second, yeah.

We mentioned it in the book,

Homo Deus is the second, yeah.

We mentioned The Black Swan by Taleb.

I think that’s a good sort of mind opener.

I actually, so there’s maybe a more controversial

recommendation I could give.

Great, we love controversy.

In some sense, it’s so classical it might surprise you,

but I actually recently read Mortimer Adler’s

How to Read a Book, not so long, it was a while ago,

but some people hate that book.

I loved it.

I think we’re in this time right now where,

boy, we’re just inundated with research papers

that you could read on archive with limited peer review

and just this wealth of information.

I don’t know, I think the passion of what you can get

out of a book, a really good book or a really good paper

if you find it, the attitude, the realization

that you’re only gonna find a few that really

are worth all your time, but then once you find them,

you should just dig in and understand it very deeply

and it’s worth marking it up and having the hard copy

writing in the side notes, side margins.

I 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.

And similarly, I’m just giving more than three now,

I’m sorry if I’ve exceeded my quota.

But on that topic just real quick is,

so basically finding a few companions to keep

for the rest of your life in terms of papers and books

and so on and those are the ones,

like not doing, what is it, FOMO, fear of 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 start to understand

when you really find something,

which a book that resonates with you

might not be the same book that resonates with me,

but when you really find one that resonates with you,

I think the dialogue that happens and that’s what,

I loved that Adler was saying, I think Socrates and Plato

say the written word is never gonna capture

the beauty of dialogue, right?

But Adler says, no, no, a really good book

is a dialogue between you and the author

and it crosses time and space and I don’t know,

I think it’s a very romantic,

there’s a bunch of like specific advice,

which you can just gloss over,

but the romantic view of how to read

and really appreciate it is so good.

And similarly, teaching,

yeah, I thought a lot about teaching

and so Isaac Asimov, great science fiction writer,

has also actually spent a lot of his career

writing nonfiction, right?

His memoir is fantastic.

He was passionate about explaining things, right?

He wrote all kinds of books

on all kinds of topics in science.

He was known as the great explainer

and I do really resonate with his style

and just his way of talking about,

by communicating and explaining to something

is really the way that you learn something.

I think about problems very differently

because of the way I’ve been given the opportunity

to teach them at MIT.

We have questions asked, the fear of the lecture,

the experience of the lecture

and the questions I get and the interactions

just forces me to be rock solid on these ideas

in a way that 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 you give lectures?

I think it’s great.

I do think that something’s changed right now,

which is, right now we’re giving lectures over Zoom.

I mean, giving seminars over Zoom and everything.

I’m trying to figure out, I think it’s a new medium.

I’m trying to figure out how to exploit it.

Yeah, I’ve been quite cynical

about human to human connection over that medium,

but I think that’s because it 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 is an opportunity

to give that differently.

I can deliver content directly into your browser.

You have a WebGL engine right there.

I can throw 3D content into your browser

while you’re listening to me, right?

And I can assume that you have at least

a powerful enough laptop or something to watch Zoom

while I’m doing that, while I’m giving a lecture.

That’s a new communication tool

that I didn’t have last year, right?

And I think robotics can potentially benefit a lot

from teaching that way.

We’ll see, it’s gonna be an experiment this fall.

It’s interesting.

I’m thinking a lot about it.

Yeah, and also like the length of lectures

or the length of like, there’s something,

so like I guarantee you, 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 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.

As short as possible, like clip,

it can be a part of a longer thing,

but like just like really beautifully condense an idea.

There’s a lot of opportunity there

that’s easier to do in remote with, I don’t know,

with editing too.

Editing is an interesting thing.

Like what, you know, most professors don’t get,

when they give a lecture,

they don’t get to go back and edit out parts,

like 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 actually,

it can make something special of a lecture.

I’ve seen that in myself and in others too,

because I edit other people’s lectures to extract clips.

It’s like, there’s certain tangents that are like,

that lose, they’re not interesting.

They’re mumbling, they’re just not,

they’re not clarifying, they’re not helpful at all.

And once you remove them, it’s just, I don’t know.

Editing can be magic.

It takes a lot of time.

Yeah, it takes, it depends like what is teaching,

you have to ask.

Yeah, yeah.

Cause I find the editing process is also beneficial

as for teaching, but also for your own learning.

I don’t know if, have you watched yourself?

Yeah, sure.

Have you watched those videos?

I mean, not all of them.

It could be painful to see like how to improve.

So do you find that, I know you segment your podcast.

Do you think that helps people with the,

the attention span aspect of it?

Or is it the segment like sections like,

yeah, we’re talking about this topic, whatever.

Nope, nope, that just helps me.

It’s actually bad.

So, and you’ve been incredible.

So I’m learning, like I’m afraid of conversation.

This is even today, I’m terrified of talking to you.

I mean, it’s something I’m trying to remove for myself.

There’s a guy, I mean, I’ve learned from a lot of people,

but really there’s been a few people

who’s been inspirational to me in terms of conversation.

Whatever people think of him,

Joe Rogan has been inspirational to me

because 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 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.

That’s like a safety blanket

that I’m trying to let go of more and more.

Cool.

So that’s, people love just regular conversation.

That’s what they, the structure is like, whatever.

I would say, I would say maybe like 10 to like,

so there’s a bunch of, you know,

there’s probably a couple of thousand PhD students

listening to this right now, right?

And they might know what we’re talking about.

But there is somebody, I guarantee you right now,

in Russia, some kid who’s just like,

who’s just smoked some weed, is sitting back

and just enjoying the hell out of this conversation.

Not really understanding.

He kind of watched some Boston Dynamics videos.

He’s just enjoying it.

And I salute you, sir.

No, but just like, there’s so much variety of people

that just have curiosity about engineering,

about sciences, about mathematics.

And also like, I should, I mean,

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,

trying to figure out what to pursue.

And these conversations can really spark

the direction of their life.

And in terms of robotics, I hope it does,

because I’m excited about the possibilities

of what robotics brings.

On that topic, 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?

It could be in robotics.

Robotics, it could be in life in general.

It could be career.

It could be a relationship advice.

It could be running advice.

Just like they’re, that’s one of the things I see,

like we talked to like 20 year olds.

They’re like, how do I do this thing?

What do I do?

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.

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,

robotics leaders, whatever,

are gonna be the ones that can do more

and they can think very deeply and critically.

And that’s a harder thing to learn.

I think one path to learning that is through mathematics,

through engineering.

I would encourage people to start math early.

I mean, I didn’t really start.

I mean, I was always in 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 really started the life that I’m living now.

But yeah, I really want kids 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, you don’t have to know everything.

You’re never gonna know everything.

So think deeply and stick with it.

Enjoy the ride, but just make sure you’re not,

yeah, just make sure you’re stopping

to think about why things work.

And it’s true, it’s easy to lose yourself

in the distractions of the world.

We’re overwhelmed with content right now,

but you have to stop and pick some of it

and really understand it.

Yeah, on the book point,

I’ve read 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.

It somehow connects, so I was born in the Soviet Union.

So it connects to me into 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 corrupting nature of power

and greed and just somehow I just,

that book has taught me more about life

than like anything else.

Even though it’s just like a silly childlike book

about pigs, I don’t know why,

it just connects and inspires.

The same, there’s a few technical books too

and algorithms that just, yeah, you return to often.

I’m with you.

Yeah, there’s, and I’ve been losing that

because of the internet.

I’ve been like going on, I’ve been going on archive

and blog posts and GitHub and the new thing

and you lose your ability to really master an idea.

Right.

Wow.

Exactly right.

What’s a fond memory from childhood?

When baby Russ Tedrick.

Well, I guess I just said that at least my current life

began when I got to MIT.

If I have to go farther than that.

Yeah, what was, was there a life before MIT?

Oh, absolutely, but let me actually tell you

what happened when I first got to MIT

because that I think might be relevant here,

but I had taken a computer engineering degree at Michigan.

I enjoyed it immensely, learned a bunch of stuff.

I liked computers, I liked programming,

but when I did get to MIT and started working

with Sebastian Sung, theoretical physicist,

computational neuroscientist, the culture here

was just different.

It demanded more of me, certainly mathematically

and in the critical thinking.

And I remember the day that I 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.

And I think that’s gonna happen to everybody

who’s doing this kind of stuff.

I think I expected you to ask me the meaning of life.

I think that somehow I think that’s gotta be part of it.

Doing hard things?

Yeah.

Did you consider quitting at any point?

Did you consider this isn’t for me?

No, never that.

I was working hard, but I was loving it.

I think there’s this magical thing

where I’m lucky to surround myself with people

that basically almost every day I’ll see something,

I’ll be told something or something that I realize,

wow, I don’t understand that.

And if I could just understand that,

there’s something else to learn.

That if I could just learn that thing,

I would connect another piece of the puzzle.

And I think that is just such an important aspect

and being willing to understand what you can and can’t do

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, Russ.

You’ve been an inspiration to me since I showed up at MIT.

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 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 Russ Tedrick and thank you to our sponsors,

Magic Spoon Cereal, BetterHelp and ExpressVPN.

Please consider supporting this podcast

by going to magicspoon.com slash Lex

and using code Lex at checkout.

Go into betterhelp.com slash Lex

and signing up at expressvpn.com slash Lex pod.

Click the links, buy the stuff, get the discount.

It really is the best way to support this podcast.

If you enjoy this thing, subscribe on YouTube,

review it with five stars and up a podcast,

support on Patreon or connect with me on Twitter

at Lex Friedman spelled somehow without the E

just F R I D M A N.

And now let me leave you with some words

from Neil deGrasse Tyson talking about robots in space

and the emphasis we humans put

on human based space exploration.

Robots are important.

If I don my pure scientist hat,

I would say just send robots.

I’ll stay down here and get the data.

But nobody’s ever given a parade for a robot.

Nobody’s ever named a high school after a robot.

So when I don my public educator hat,

I have to recognize the elements of exploration

that excite people.

It’s not only the discoveries and the beautiful photos

that come down from the heavens.

It’s the vicarious participation in discovery itself.

Thank you for listening and hope to see you next time.