Lex Fridman Podcast: #374 – Robert Playter: Boston Dynamics CEO on Humanoid and Legged Robotics

The following is a conversation with Robert Plater, CEO of Boston Dynamics, a legendary robotic

company that over 30 years has created some of the most elegant, dexterous, and simply amazing

robots ever built, including the humanoid robot atlas and the robot dog spot. One or both of

whom you've probably seen on the internet, either dancing, doing backflips, opening doors,

or throwing around heavy objects. Robert has led both the development of Boston Dynamics humanoid

robots and their physics-based simulation software. He has been with the company from the very beginning,

including its roots at MIT, where he received his PhD in aeronautical engineering. This was,

in 1994, at the legendary MIT Leg Lab. He wrote his PhD thesis on robot gymnastics,

as part of which he programmed a bipedal robot to do the world's first 3D robotic somersault.

Robert is a great engineer, roboticist, and leader, and Boston Dynamics,

to me, as a roboticist, is a truly inspiring company. This conversation was a big honor and

pleasure, and I hope to do a lot of great work with these robots in the years to come.

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Running a business, as this podcast reveals, from Robert Plater and Boston Dynamics is really hard.

It's not just about the design of the systems. It's not just about the engineering, the software,

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all the failure upon failure upon failure in the early stages in the middle stages of getting these

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shove in whatever you want into the ice cream and blend it and it tastes delicious,

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and now, dear friends, here's Robert Plater.

When did you first fall in love with robotics? Let's start with love and robots.

Well, love is relevant, because I think the fascination, the deep fascination is really

about movement, and I was visiting MIT looking for a place to get a PhD, and I wanted to do

some laboratory work, and one of my professors in the Aero departments said,

go see this guy, Mark Rabert, down in the basement of the AI lab, and so I walked down there and saw

him, he showed me his robots, and he showed me this robot doing a somersault, and I just immediately

went, whoa, you know, robots can do that, and because of my own interest in gymnastics, there

was this immediate connection, and I was interested in, I was in an aeroastro degree, because flight

and movement was also fascinating to me, and then it turned out that robotics had this big

challenge, how do you balance, how do you build a legged robot that can really get around,

and that was a fascination, and it still exists today. You're still working on

perfecting motion in robots. What about the elegance and the beauty of the movement itself?

Is there something maybe grounded in your appreciation of movement from your gymnastics

days? Did you, was there something you just fundamentally appreciate about the elegance

and beauty of movement? You know, we had this concept in gymnastics of letting your body do

what it wanted to do when you get really good at gymnastics. Part of what you're doing is putting

your body into a position where the physics and the body's inertia and momentum will kind of push

you in the right direction in a very natural and organic way, and the thing that Mark was doing,

you know, in the basement of that laboratory was trying to figure out how to build machines to

take advantage of those ideas. How do you build something so that the physics of the machine

just kind of inherently wants to do what it wants to do? And he was building these springy

pogo stick type. You know, his first cut at legged locomotion was a pogo stick where it's

bouncing and there's a spring mass system that's oscillating, has its own sort of natural frequency

there, and sort of figuring out how to augment those natural physics with also intent. How do

you then control that but not overpower it? It's that coordination that I think creates

real potential. We could call it beauty. You know, you could call it, I don't know, synergy.

People have different words for it. But I think that that was inherent from the beginning. That

was clear to me that that's part of what Mark was trying to do. He asked me to do that in my

research work. So, you know, that's where it got going. So part of the thing that I think I'm calling

elegance and beauty in this case, which was there, even with the pogo stick is maybe the

efficiency. So letting the body do what it wants to do, trying to discover the efficient movement.

It's definitely more efficient. It also becomes easier to control in its own way because the

physics are solving some of the problem itself. It's not like you have to do all this calculation

and overpower the physics. The physics naturally, inherently want to do the right thing. There can

even be, you know, feedback mechanisms, stabilizing mechanisms that occur simply by virtue of the

physics of the body. And it's, you know, not all, not all in the computer or not even all in your

mind as a person. And I, there's something interesting in that melding. You were with Mark

for many, many, many years. You were there in this kind of legendary space of a leg lab in

MIT in the basement. All great things happen in the basement. Is there some memories,

is there some memories from that time that you have because it's so, it's such cutting-edge

work in robotics and artificial intelligence? The memories, the distinctive lessons I would

say I learned in that time period and that I think Mark was a great teacher of was it's

okay to pursue your interests, your curiosity, do something because you love it. You'll do it a lot

better if you love it. That is a lasting lesson that I think we apply at the company still

and really is a core value. So the interesting thing is I got to,

with people like Russ Tedrick and others, like the students that work at those robotics labs

are like some of the happiest people I've ever met. I don't know what that is. I meet a lot of

PhD students. A lot of them are kind of broken by the wear and tear of the process. But roboticists

are, while they work extremely hard and work a long hours, there's a, there's a happiness there.

The only other group of people I've met like that are people that skydive a lot.

For some reason, there's a deep fulfilling happiness. Maybe from like a long period of struggle to get

a thing to work and it works and there's a magic to it. I don't know exactly because it's so

fundamentally hands-on and you're bringing a thing to life. I don't know what it is, but they're happy.

We see, our attrition at the company is really low. People come and they love the pursuit.

And I think part of that is that there's perhaps an actual connection to it. It's a little bit

easier to connect when you have a robot that's moving around in the world and part of your

goal is to make it move around in the world. You can identify with that. And this is on a,

this is one of the unique things about the kinds of robots we're building is this physical interaction

lets you perhaps identify with it. So I think that is a source of happiness. I don't think it's

unique to robotics. I think anybody also who is just pursuing something they love, it's easier

to work hard at it and be good at it and not everybody gets to find that. I do feel lucky in

that way and I think we're lucky as an organization that we've been able to build a business around

this and that keeps people engaged. So if it's all right, let's link our mark for a little

bit longer. Mark Raybert, so he's a legend. He's a legendary engineer and roboticist.

What have you learned about life about robotics from Mark through all the many years you worked

with him? I think the most important lesson, which was have the courage of your convictions and do

what you think is interesting. Be willing to try to find big, big problems to go after.

At the time, like at locomotion, especially in a dynamic machine, nobody had solved it and that

felt like a multi-decade problem to go after. So have the courage to go after that because you're

interested. Don't worry if it's going to make money. That's been a theme. So that's really

probably the most important lesson I think that I got from Mark.

How crazy is the effort of doing legged robotics at that time especially?

Mark got some stuff to work starting from the simple ideas. So maybe another important idea

that has really become a value of the company is try to simplify a thing to the core essence.

While Mark was showing videos of animals running across the savanna or

climbing mountains, what he started with was a pogo stick because he was trying to reduce the

problem to something that was manageable and getting the pogo stick to balance. Had in it

the fundamental problems that if we solved those, you could eventually extrapolate to

something that galloped like a horse. And so look for those simplifying principles.

How tough is the job of simplifying a robot? So I'd say in the early days, the thing that made

the researchers at Boston Dynamics special is that we worked on figuring out what that

central principle was and then building software or machines around that principle. And that was

not easy in the early days. And it took real expertise in understanding the dynamics of

motion and feedback control principles. With computers at the time, how to build a feedback

control algorithm that was simple enough that it could run in real time at a thousand hertz

and actually get that machine to work. And that was not something everybody was doing at that time.

Now the world's changing now. And I think the approaches to controlling robots are going to

change. And they're going to become more broadly available. But at the time, there weren't many

groups who could really work at that principled level with both the software and make the hardware

work. And I'll say one other thing about you're sort of talking about what are the special things.

The other thing was it's good to break stuff. Use the robots, break them, repair them,

fix and repeat, test, fix and repeat. And that's also a core principle that has become part of the

company. And it lets you be fearless in your work. Too often if you are working with a very

expensive robot, maybe one that you bought from somebody else or that you don't know how to fix,

then you treat it with git gloves and you can't actually make progress. You have to be able to

break something. And so I think that's been a principle as well. So just to link on that

psychologically, how do you deal with that? Because I remember I had built a RC car with that some

had some custom stuff like compute on it, all that kind of stuff, cameras. And because I

didn't sleep much, the code I wrote had an issue where it didn't stop the car and the car got confused

and at full speed at like 20, 25 miles an hour slammed into a wall. And I just remember sitting

there alone in a deep sadness, sort of full of regret, I think, almost anger. But also like

sadness because you think about, well, these robots, especially for autonomous vehicles,

like you should be taking safety very seriously, even in these kinds of things. But just no good

feelings. It made me more afraid probably to do this kind of experiments in the future.

Perhaps the right way to have seen that is positively. It depends if you could have built

that car or just gotten another one, right? That would have been the approach. I remember

when I got to grad school, I got some training about operating a lathe and a mill up in the

machine shop. And I could start to make my own parts. And I remember breaking some piece of

equipment in the lab and then realizing, because maybe this was a unique part and I couldn't go

by it. And I realized, oh, I can just go make it. That was an enabling feeling. Then you're not

afraid. It might take time. It might take more work than you thought it was going to be required

to get this thing done. But you can just go make it. And that's freeing in a way that nothing else

says. You mentioned the feedback control, the dynamics. Sorry for the romantic question, but

in the early days and even now, is the dynamics probably more appropriate for the early days?

Is it more art or science? There's a lot of science around it. And trying to develop scientific

principles that let you extrapolate from one legged machine to another. Develop a core

set of principles like a spring mass bouncing system. And then figure out how to apply that

from a one legged machine to a two or a four legged machine. Those principles are really

important. And we're definitely a core part of our work. There's also, when we started to pursue

humanoid robots, there was so much complexity in that machine that one of the benefits of

the humanoid form is you have some intuition about how it should look while it's moving.

And that's a little bit of an art, I think. Or maybe it's just tapping into a knowledge

that you have deep in your body and then trying to express that in the machine. But that's an

intuition that's a little bit more on the art side. Maybe it predates your knowledge. Before

you have the knowledge of how to control it, you try to work through the art channel. And

humanoid sort of make that available to you. If it had been a different shape, maybe you wouldn't

have had the same intuition about it. Yeah, so your knowledge about moving through the world

is not made explicit to you. That's why it's art. Yeah, it might be hard to actually articulate

exactly. There's something about, and being a competitive athlete, there's something about

seeing movement. A coach, one of the greatest strengths a coach has is being able to see

some little change in what the athlete is doing and then being able to articulate that to the

athlete. And then maybe even trying to say, and you should try to feel this. So there's something

just in seeing. And again, sometimes it's hard to articulate what it is you're seeing,

but there's a just perceiving the motion at a rate that is, again, sometimes hard to put into words.

Yeah, I wonder how it is possible to achieve sort of truly elegant movement. You have a movie like

Ex Machina, I'm not sure if you've seen it, but the main actress in that who plays the AI robot,

I think is a ballerina. I mean, just the natural elegance and the, I don't know,

eloquence of movement. It looks efficient and easy and just it looks right. It looks

right is sort of the key. And then you look at especially early robots, I mean, they're so

cautious in the way they move that it's not the caution that looks wrong. It's something

about the movement that looks wrong that feels like it's very inefficient, unnecessarily so. And

it's hard to put that into words, exactly. We think, and part of the reason why people are

attracted to the machines we build is because the inherent dynamics of movement are closer to right

because we try to use walking gates or we build a machine around this gate where you're trying

to work with the dynamics of the machine instead of to stop them. Some of the early walking machines,

you're essentially, you're really trying hard to not let them fall over. And so you're always

stopping the tipping motion. And sort of the insight of dynamic stability in a

machine is to go with it, let the tipping happen, let yourself fall, but then catch yourself

with that next foot. And there's something about getting those physics to be expressed in the machine

that people interpret as lifelike or elegant or just natural looking. And so I think if you get

the physics right, it also ends up being more efficient likely. There's a benefit that it

probably ends up being more stable in the long run. It could walk stably over a wider range of

conditions. And it's more beautiful and attractive at the same time.

So how hard is it to get the humanoid robot Atlas to do some of the things that's recently been

doing? Let's forget the flips and all of that. Let's just look at the running. Maybe you can

correct me, but there's something about running. I mean, that's not careful at all. That's you're

falling forward. You're jumping forward and are falling. So how hard is it to get that right?

Our first humanoid, we needed to deliver natural looking walking. We took a contract

from the army. They wanted a robot that could walk naturally. They wanted to put

a suit on the robot and be able to test it in a gas environment. And so they wanted the

motion to be natural. And so our goal was a natural looking gate. It was

surprisingly hard to get that to work. But we did build an early machine.

We called it Petman prototype. It was the prototype before the Petman robot. And it had a really

nice looking gate where it would stick the leg out. It would do heel strike first before it

rolled onto the toe. So you didn't land with a flat foot. You extended your leg a little bit.

But even then, it was hard to get the robot to walk when you were walking that it fully extended

its leg and essentially landed on an extended leg. And if you watch closely how you walk,

you probably land on an extended leg, but then you immediately flex your knee as you start to

make that contact. And getting that all to work well took such a long time. In fact,

I probably didn't really see the nice natural walking that I expected out of our human ways

until maybe last year. And the team was developing on our newer generation of Atlas some new techniques

for developing a walking control algorithm. And they got that natural looking motion

as sort of a byproduct of just a different process they were applying to developing the control.

So that probably took 15 years, 10 to 15 years to sort of get that from the Petman prototype

was probably in 2008 and what was it, 2022? Last year that I think I saw a good walking on Atlas.

If you could just link on it, what are some challenges of getting good walking? So is it

partially like a hardware actuator problem? Is it the control? Is it the artistic element of

just observing the whole system operating in different conditions together? I mean, is there

some kind of interesting quirks or challenges you can speak to like the heel strike? Yeah,

so one of the things that makes the like this straight leg a challenge is you're sort of up

against a singularity, a mathematical singularity where, you know, when your leg is fully extended,

it can't go further the other direction, right? There's only, you can only move in one direction.

And that makes all of the calculations around how to produce torques at that joint or positions

makes it more complicated. And so having all of the mathematics so it can deal with

these singular configurations is one of many challenges that we face. And I'd say in those

earlier days, again, we were working with these really simplified models. So we're trying to boil

all the physics of the complex human body into a simpler subsystem that we can more easily describe

in mathematics. And sometimes those simpler subsystems don't have all of that complexity of

the straight leg built into them. And so what's happened more recently is we're able to apply

techniques that let us take the full physics of the robot into account and deal with some of those

strange situations like the straight leg. So is there a fundamental challenge here that it's,

maybe you can correct me, but is it underactuated? Are you falling?

Underactuated is the right word, right? You can't push the robot in any direction you want to,

right? And so that is one of the hard problems of leg and locomotion.

And you have to do that for natural movement. It's not necessarily required for natural movement.

It's just required, we don't have a gravity force that you can hook yourself onto to apply

an external force in the direction you want at all times, right? The only external forces are

being mediated through your feet and how they get mediated depend on how you place your feet.

And you can't just, God's hand can't reach down and push in any direction you want.

So is there some extra challenge to the fact that Alice is such a big robot?

There is. The humanoid form is attractive in many ways, but it's also a challenge in many ways.

You have this big upper body that has a lot of mass and inertia. And throwing that inertia around

increases the complexity of maintaining balance. And as soon as you pick up something heavy in

your arms, you've made that problem even harder. And so in the early work, in the leg lab and in

the early days at the company, we were pursuing these quadruped robots, which had a kind of built-in

simplification. You had this big rigid body and then really light legs. So when you swing the legs,

the leg motion didn't impact the body motion very much. All the mass and inertia was in the body.

But when you have the humanoid, that doesn't work. You have big, heavy legs. You swing the

legs, it affects everything else. And so dealing with all of that interaction

does make the humanoid a much more complicated platform.

And I also saw that at least recently, you've been doing more explicit modeling of the stuff you

pick up. Really interesting. So you have to model the shape, the weight distribution.

I don't know. You have to include that as part of the modeling, as part of the planning.

For people who don't know, so Atlas, at least in a recent video, throws a heavy bag,

throws a bunch of stuff. So what's involved in picking up a thing, a heavy thing,

and when that thing is a bunch of different non-standard things, I think it's also picked up

like a barbell. And to be able to throw it in some cases, what are some interesting challenges there?

So we were definitely trying to show that the robot and the techniques we're applying to Atlas

let us deal with heavy things in the world. Because if the robot's going to be useful,

it's actually got to move stuff around. And that needs to be significant stuff.

That's an appreciable portion of the body weight of the robot. And we also think this

differentiates us from the other humanoid robot activities that you're seeing out there. Mostly

they're not picking stuff up yet. And not heavy stuff anyway. But just like you or me, you need

to anticipate that moment. You're reaching out to pick something up. And as soon as you pick it

up, your center of mass is going to shift. And if you're going to turn in a circle, you have to

take that inertia into account. And if you're going to throw a thing, you've got all of that has

to be included in the model of what you're trying to do. So the robot needs to have some idea or

expectation of what that weight is and then predict. Think a couple of seconds ahead,

how do I manage my body plus this big, heavy thing together and still maintain balance?

That's a big change for us. And I think the tools we've built are really allowing that to happen

quickly now. Some of those motions that you saw in that most recent video, we were able to create

in a matter of days. It used to be six months to do anything new on the robot. And now

we're starting to develop the tools that let us do that in a matter of days. And so we think

that's really exciting. That means that the ability to create new behaviors for the robot

is going to be a quicker process. So being able to explicitly model

new things that it might need to pick up, new types of things. And to some degree, you don't

want to have to pay too much attention to each specific thing. There's sort of a generalization

here. Obviously, when you grab a thing, you have to conform your hand, your end effector to the

surface of that shape. But once it's in your hands, it's probably just the mass and inertia

that matter. And the shape may not be as important. And so in some ways, you want to pay attention

to that detailed shape. And in others, you want to generalize it and say, well, all I really care

about is the center of mass of this thing, especially if I'm going to throw it up on that

scaffolding. And it's easier if the body is rigid. Doesn't it throw like a sandbag type thing?

That tool bag had loose stuff in it. So it managed that. There are harder things that we

haven't done yet. We could have had a big jointed thing or I don't know, a bunch of loose wire or

rope. What about carrying another robot? How about that? Yeah, we haven't done that yet.

I guess we did a little skit around Christmas where we had two spots holding up another spot

that was trying to put a bow on a tree. So I guess we're doing that in a small way.

Okay, that's pretty good. Let me ask the all-important question. Do you know how much

Atlas can curl? I mean, for us humans, that's really one of the most fundamental questions

you can ask another human being. Curl, bench. It probably can't curl as much as we can yet.

But a metric that I think is interesting is another way of looking at that strength is the

box jump. So how high of a box can you jump onto? Question. And Atlas, I don't know the exact height.

It was probably a meter high or something like that. It was a pretty tall jump that Atlas was

able to manage when we last tried to do this. And I have video of my chief technical officer

doing the same jump. And he really struggled. Oh, the human.

But the human getting all the way on top of this box. But then Atlas was able to do it.

We're now thinking about the next generation of Atlas. And we're probably going to be in the realm of

a person can't do it with the next generation. The robots, the actuators are going to get stronger

where it really is the case that at least some of these joints, some of these motions will be

stronger. And to understand how high it can jump, you probably had to do quite a bit of testing.

Oh, yeah. And there's lots of videos of it trying and failing. And that's, you know, that's all.

We don't always release those videos, but they're a lot of fun to look at.

So we'll talk a little bit about that. But can you talk to the jumping?

Because you talked about the walking, and it took a long time, many, many years to get the

walking to be natural. But there's also really natural looking, robust, resilient jumping. How

hard is it to do the jumping? Well, again, this stuff has really evolved rapidly in the last few

years. You know, the first time we did a somersault, you know, there was a lot of kind of manual

iteration. What is the trajectory? You know, how hard do you throw you? In fact, in these early

days, I actually would, when I'd see early experiments that the team was doing, I might make

suggestions about how to change the technique, again, kind of borrowing from my own intuition

about how backflips work. But frankly, they don't need that anymore. So in the early days,

you had to iterate kind of in almost a manual way, trying to change these trajectories of the arms

or the legs to try to get, you know, a successful backflip to happen. But more recently, we're

running these model predictive control techniques where we're able to, the robot essentially can

think in advance for the next second or two about how its motion is going to transpire. And you can,

you know, solve for optimal trajectories to get from A to B. So this is happening in a much more

natural way. And then we're really seeing an acceleration happen in the development of these

behaviors, again, partly due to these optimization techniques, sometimes learning techniques.

So it's hard in that there's a lot of mathematics behind it. But we're figuring that out.

So you can do model predictive control for, I mean, I don't even understand what that looks

like when the entire robot is in the air flying and doing a backflip. But that's the cool part,

right? So, you know, the physics, we can calculate physics pretty well using Newton's laws about

how it's going to evolve over time. And the sick trick, which was a front somersault with a half

twist is a good example, right? You saw the robot on various versions of that trick. I've seen it

land in different configurations, and it still manages to stabilize itself. And so,

you know, what this model predictive control means is, again, in real time, the robot is

projecting ahead, you know, a second into the future and sort of exploring options. And if I

move my arm a little bit more this way, how is that going to affect the outcome? And so it can do

these calculations, many of them, you know, and basically solve for, you know, given where I am

now, maybe I took off a little bit screwy from how I had planned, I can adjust. So you're adjusting

in the air? Adjust on the fly. So the model predictive control lets you adjust on the fly.

And of course, I think this is what, you know, people adapt as well. We, when we do it,

even a gymnastics trick, we try to set it up so it's as close to the same every time. But we

figured out how to do some adjustment on the fly. And now we're starting to figure out that the

robots can do this adjustment on the fly as well, using these techniques.

In the air. It's so, I mean, it just feels, from a robotics perspective, just surreal.

Well, that's sort of the, you talked about under-actuated, right? So when you're in the air,

there's something, there's some things you can't change, right? You can't change the momentum

while it's in the air, because you can't apply an external force or torque. And so

the momentum isn't going to change. So how do you work within the constraint of that fixed

momentum to still get from A to B? Where you want to be? That's really undirectured.

You're in the air. I mean, you become a drone for a brief moment in time. No, you're not even a

drone because you can't hover. You're going to impact soon. Be ready. Yeah. Are you considered

like a hover type thing or no? No, it's too much weight. I mean, it's just, it's just incredible.

It's just even to have the guts to try a backflip. It was such a large body. That's wild.

Well, we definitely broke a few robots trying. But that's where the build it,

break it, fix it, strategy comes in, got to be willing to break. And what ends up happening is

you end up, by breaking the robot repeatedly, you find the weak points and then you end up

redesigning it. So it doesn't break so easily next time. Through the breaking process,

you learn a lot, like a lot of lessons and you keep improving not just how to make the backflip

work, but everything. And how to build the machine better. Yeah. I mean, is there something about

just the guts to come up with an idea of saying, you know what, let's try to make it do a backflip?

Well, I think the courage to do a backflip in the first place and to not worry too much about the

ridicule of somebody saying, why the heck are you doing backflips with robots? Because a lot of

people have asked that. Why are you doing this? Why go to the moon in this decade and do the other

things JFK? Not because it's easy, because it's hard. Yeah, exactly. Don't ask questions. Okay,

so the jumping, I mean, it's just, there's a lot of incredible stuff. If we can just rewind a little

bit to the DARPA Robotics Challenge in 2015, I think, which was for people who are familiar

with the DARPA challenges, it was first with autonomous vehicles and there's a lot of interesting

challenges around that. And the DARPA Robotics Challenge was when humanoid robots were tasked

to do all kinds of manipulation, walking, driving a car, all these kinds of challenges

with, if I remember correctly, some slight capability to communicate with humans,

but the communication was very poor. So it basically has to be almost entirely autonomous.

It can have periods where the communication was entirely interrupted and the robot had to be

able to proceed. But you could provide some high level guidance to the robot, basically low band

with communications to steer it. I watched that challenge with kind of tears in my eyes

eating popcorn. But I wasn't personally losing, you know, hundreds of thousands of millions of

dollars and many years of incredible hard work by some of the most brilliant roboticists in the

world. So that was why the tragic, that's why tears came. So anyway, what have you,

just looking back to that time, what have you learned from that experience?

Maybe if you could describe what it was, sort of the setup for people who haven't seen it.

Well, so there was a contest where a bunch of different robots were asked to do a series of

tasks, some of those that you mentioned, drive a vehicle, get out, open a door, go identify a

valve, shut a valve, use a tool to maybe cut a hole in a surface and then crawl over some

stairs and maybe some rough terrain. So it was, the idea was have a general purpose robot that

could do lots of different things, had to be mobility and manipulation on board perception.

And there was a contest which DARPA likes at the time was running, sort of follow on to the

grand challenge, which was let's try to push vehicle autonomy along, right? They encourage

people to build autonomous cars. So they're trying to basically push an industry forward.

And we were asked, our role in this was to build a humanoid at the time it was our

sort of first generation Atlas robot. And we built maybe 10 of them. I don't remember the exact

number. And DARPA distributed those to various teams that sort of won a contest, showed that they

could program these robots and then use them to compete against each other. And then other

robots were introduced as well. Some teams built their own robots, Carnegie, Mellon, for example,

built their own robot. And all these robots competed to see who could sort of get through this maze

of the fastest. And again, I think the purpose was to kind of push the whole industry forward.

We provided the robot and some baseline software, but we didn't actually compete as a participant

where we were trying to drive the robot through this maze. We were just trying to support the other

teams. It was humbling because it was really a hard task. And honestly, the tears were because

mostly the robots didn't do it. They fell down repeatedly. It was hard to get through this

contest. Some did and they were rewarded in one. But it was humbling because of just how hard these

tasks weren't all that hard. A person could have done it very easily. But it was really hard to

get the robots to do it. The general nature of it, the variety of it. And also that I don't know

if the tasks were sort of the task in themselves, help us understand what is difficult and what

is not. I don't know if that was obvious before the contest was designed. So you kind of tried to

figure that out. And I think Atlas is really a general robot platform. And it's perhaps not

best suited for the specific tasks of that contest. For just, for example, probably the hardest task

is not the driving of the car, but getting in and out of the car. And Atlas probably,

you know, if you were to design a robot that can get into the car easily and get out easily,

you probably would not make Atlas, that particular car.

Yeah. The robot was a little bit big to get in and out of that car, right?

It doesn't fit. Yeah.

This is the curse of a general purpose robot, that they're not perfect at any one thing.

But they might be able to do a wide variety of things. And that is the goal at the end of the

day. You know, I think we all want to build general purpose robots that can be used for

lots of different activities, but it's hard. And the wisdom in building successful robots

up until this point have been go build a robot for a specific task and it'll do it very well.

And as long as you control that environment, it'll operate perfectly. But

robots need to be able to deal with uncertainty. If they're going to be useful to us in the future,

they need to be able to deal with unexpected situations. And that's sort of the goal of

a general purpose or multi-purpose robot. And that's just darn hard. And so some of,

you know, there's these curious little failures. Like I remember one of the, a robot, you know,

the first time you start to try to push on the world with a robot, you forget that the world

pushes back and will push you over if you're not ready for it. And the robot, you know,

reached to grab the door handle. I think it missed the grasp of the door handle,

was expecting that its hand was on the door handle. And so when it tried to turn the knob,

it just threw itself over. It didn't realize, oh, I had missed the door handle. I didn't have,

I didn't, I was expecting a force back from the door. It wasn't there. And then I lost my balance.

So these little simple things that you and I would take totally for granted and deal with

the robots don't know how to deal with yet. And so you have to start to deal with all of those

circumstances. Well, I think a lot of us experience this in even when sober, but drunk too.

Sort of, you pick up a thing and expect it to be, what is it, heavy? And it turns out to be light.

Oh yeah. And then so the same, and I'm sure if your depth of perception for whatever reason is

screwed up, if you're drunk or some other reason, and then you think you're putting your hand on

the table and you miss it, I mean, it's the same kind of situation. But there's-

Which is why you need to be able to predict forward just a little bit. And so that's where

this model of predictive control stuff comes in. Predict forward what you think is going to happen.

And then if that does happen, you're in good shape. If something else happens,

you better start predicting again. So re-generate a plan when you don't, I mean, that also requires

a very fast feedback loop of updating what your prediction matches to the actual real world.

Yeah, those things have to run pretty quickly.

What's the challenge of running things pretty quickly? A thousand hertz of acting and sensing

quickly? You know, there's a few different layers of that. You want, at the lowest level,

you like to run things typically at around a thousand hertz, which means that, you know,

at each joint of the robot, you're measuring position or force and then trying to control

your actuator, whether it's a hydraulic or electric motor, trying to control the force

coming out of that actuator. And you want to do that really fast, something like a thousand hertz.

And that means you can't have too much calculation going on at that joint.

But that's pretty manageable these days and is fairly common. And then there's another layer

that you're probably calculating, you know, maybe at a hundred hertz, maybe 10 times slower,

which is now starting to look at the overall body motion and thinking about the larger physics

of the robot. And then there's yet another loop that's probably happening a little bit slower,

which is where you start to bring, you know, your perception and your vision and things like that.

And so you need to run all of these loops sort of simultaneously. You do have to manage your

computer time so that you can squeeze in all the calculations you need in real time in a very

consistent way. And the amount of calculation we can do is increasing as computers get better,

which means we can start to do more sophisticated calculations. I can have a more complex model

doing my forward prediction. And that might allow me to do even better predictions as I get better

and better. And it used to be, again, we had, you know, 10 years ago, we had to have pretty simple

models that we were running, you know, at those fast rates because the computers weren't as capable

about calculating forward with a sophisticated model. But as, as computation gets better,

we can, we can do more of that. What about the actual pipeline of software

engineering? How easy is it to keep updating Atlas? Like to continue development on it?

So how many computers are on there? Is there a nice pipeline?

It's an important part of building a team around it, which means, you know, you need to also have

software tools, simulation tools, you know, so we have always made strong use of physics-based

simulation tools to do some of this calculation, basically test it in simulation before you put

it on the robot. But you also want the same code that you're running in simulation to be the same

code you're running on the hardware. And so even getting to the point where it was the same code,

going from one to the other, we probably didn't really get that working until, you know, a few

years, several years ago. But that was a, you know, that was a bit of a milestone. And so

you want to work, certainly work these pipelines so that you can make it as easy as possible and

have a bunch of people working in parallel, especially when, you know, we only have, you know,

four of the Atlas robots, the modern Atlas robots at the company. And, you know, we probably have,

you know, 40 developers there all trying to gain access to it. And so you need to share resources

and use some of these, some of the software pipeline.

Well, that's a really exciting step to be able to run the exact same code and simulation as on

the actual robot. How hard is it to do

realistic simulation, physics-based simulation of Atlas such that, I mean, the dream is like,

if it works in simulation, it works perfectly in reality. How hard is it to sort of keep

work on closing that gap? The root of some of our physics-based simulation tools really started

at MIT. And we built some good physics-based modeling tools there.

The early days of the company, we were trying to develop those tools as a commercial product.

So we continued to develop them. It wasn't a particularly successful commercial product,

but we ended up with some nice physics-based simulation tools so that when we started doing

legged robotics again, we had a really nice tool to work with. And the things we paid attention to

were things that weren't necessarily handled very well in the commercial tools you could buy

off the shelf like interaction with the world, like foot-ground contact. So trying to model those

contact events well in a way that captured the important parts of the interaction was a really

important element to get right and to also do in a way that was computationally feasible.

And could run fast because if your simulation runs too slow, then your developers are sitting

around waiting for stuff to run and compile. So it's always about efficient, fast operation as

well. So that's been a big part of it. I think developing those tools in parallel to the development

of the platform and trying to scale them has really been essential, I'd say, to us being able

to assemble a team of people that could do this. Yeah, how to simulate contact periods of foot-ground

contact but sort of for manipulation because don't you want to model all kinds of surfaces?

Yeah, so it will be even more complex with manipulation because there's a lot more going on

and you need to capture, I don't know, things slipping and moving in your hand.

It's a level of complexity that I think goes above foot-ground contact when you really start

doing dexterous manipulation. So there's challenges ahead still.

So how far are we away from me being able to walk with Atlas in the sand along the beach

and us both drinking a beer? Maybe Atlas could spill his beer because he's got nowhere to put it.

Atlas could walk on the sand. I mean, have we really had him out on the beach?

We take them outside often, rocks, hills, that sort of thing, even just around our lab in Waltham.

We probably haven't been on the sand but I don't doubt that we could deal with it.

We might have to spend a little bit of time to sort of make that work but we had to take

Big Dog to Thailand years ago and we did this great video of the robot

walking in the sand, walking into the ocean up to, I don't know, its belly or something like that

and then turning around and walking out, all walking, playing some cool beach music.

Great show but then we didn't really clean the robot off and the salt water was really hard on

it so we put it in a box, shipped it back. By the time it came back we had some problems

with corrosion. It's the salt water. It's not like sand getting into the components or something like

this but I'm sure if this is a big priority you can make it waterproof. That just wasn't our goal

at the time. Well, it's a personal goal of mine to walk along the beach but it's a human problem

too. You get sand everywhere, it's just a giant mess. So soft surfaces are okay. So I mean,

can we just link on the robotics challenge? There's a pile of rubble to walk over.

How difficult does that task? In the early days of developing Big Dog,

the loose rock was the epitome of the hard walking surface because you step down and then the rock

and you have these little point feet on the robot and the rock can roll and you have to deal with

that last minute change in your foot placement. So you step on the thing and that thing responds

to you stepping on it? Yeah and it moves where your point of support is and so it's really that

became kind of the essence of the test and so that was the beginning of us starting to build rock

piles in our parking lots and we would actually build boxes full of rocks and bring them into the

lab and then we would have the robots walking across these boxes of rocks because that became

the essential test. So you mentioned Big Dog. Can we maybe take a stroll through the history

about the dynamics? So what and who is Big Dog? By the way, is who, do you try not to anthropomorphize

the robots? Do you try not to, do you try to remember that they're, this is like the division

I have because for me it's impossible. For me there's a magic to the being that is a robot.

It is not human but it is the same magic that a living being has when it moves about the world,

is there in the robot. So I don't know what question I'm asking but should I say what

or who I guess? Who is Big Dog? What is Big Dog? Well I'll say to address the medic question,

we don't try to draw hard lines around it being an it or a him or a her.

It's okay, right? People, I think part of the magic of these kinds of machines is by nature of

their organic movement of their dynamics, we tend to want to identify with them. We tend to look at

them and sort of attribute maybe feeling to that because we've only seen things that move like this

that were alive. And so this is an opportunity. It means that you could have feelings for a machine

and you know people have feelings for their cars, you know they get attracted to them, attached to

them. So that's inherently could be a good thing as long as we manage what that interaction is.

So we don't put strong boundaries around this and ultimately think it's a benefit but it's also

can be a bit of a curse because I think people look at these machines and they attribute a level

of intelligence that the machines don't have. Why? Because again, they've seen things move

like this that were living beings which are intelligent. And so they want to attribute

intelligence to the robots that isn't appropriate yet even though they move like an intelligent

being. But you try to acknowledge that the anthropomorphization is there and try to,

first of all, acknowledge it's there. And have a little fun with it. You know our most recent

video, it's just kind of fun, you know, to look at the robot. We started off the video with Atlas

kind of looking around for where the bag of tools was because the guy up on the scaffolding says,

send me some tools. And Atlas has to kind of look around and see where they are.

And there's a little personality there. That is fun. It's entertaining. It makes our jobs

interesting. And I think in the long run can enhance interaction between humans and robots

in a way that isn't available to machines that don't move that way.

This is something to me personally is very interesting. I happen to have a lot of legged

robots. I hope to have a lot of spots in my possession. I'm interested in celebrating

robotics and celebrating companies. And I also don't want to companies that do incredible

stuff like Boston Dynamics. And there's a, you know, I'm a little crazy. And you say you don't

want to, you want to align, you want to help the company because I ultimately want a company that

Boston Dynamics to succeed. And part of that we'll talk about, you know, success kind of requires

making money. And so the kind of stuff I'm particularly interested in may not be the

thing that makes money in the short term. I can make an argument that will in the long term. But

the kind of stuff I've been playing with is a robust way of having the quadruped as the robot

dogs communicate in motion with their body movement. The same kind of stuff you do with

the dog, but not hard-coded, but in a robust way. And be able to communicate excitement or fear,

boredom, all this kinds of stuff. And I think as a base layer of function of behavior to add on

top of a robot, I think that's a really powerful way to make the robot more usable for humans,

for whatever application. I think it's going to be really important. And it's a thing we're

beginning to pay attention to. We really want to start, a differentiator for the company has

always been, we really want the robot to work. We want it to be useful. Making it work at first

meant the luggage locomotion really works. It can really get around and it doesn't fall down.

But beyond that, now it needs to be a useful tool. And our customers are, for example,

factory owners, people who are running a process manufacturing facility. And the robot needs to

be able to get through this complex facility in a reliable way, taking measurements.

We need for people who are operating those robots to understand what the robots are doing.

If the robot gets into needs help or is in trouble or something, it needs to be able to

communicate. And a physical indication of some sort, so that a person looked at the robot and

goes, oh, I know what that robot's doing, the robot's going to go take measurements of my

vacuum pump with its thermal camera. You want to be able to indicate that. And we're even just,

the robots are about to turn in front of you and maybe indicate that it's going to turn. And so

you sort of see and can anticipate its motion. So this kind of communication is going to become

more and more important. It wasn't sort of our starting point. But now the robots are really

out in the world and we have about 1,000 of them out with customers right now.

This layer of physical indication, I think, is going to become more and more important.

We'll talk about where it goes because there's a lot of interesting possibilities. But if we're

going to return back to the origins of Boston Dynamics, so that the more research, the R&D

side before we talk about how to build robots at scale, it's a big dog.

So the company started in 1992 and probably 2003, I believe, is when we

took a contract from DARPA, so basically 10 years, 11 years, we weren't doing robotics.

We did a little bit of robotics with Sony. They had IBO, their IBO robot. We were developing

some software for that that kind of got us a little bit involved with robotics again.

Then there's this opportunity to do a DARPA contract where they wanted to build

a robot dog. And we won a contract to build that. And so that was the genesis of big dog.

And it was a quadruped. It was the first time we built a robot that had everything on board.

You could actually take the robot out into the wild and operate it. So it had an on-board power

plan. It had on-board computers. It had hydraulic actuators that needed to be cooled,

so we had cooling systems built in. Everything integrated into the robot.

And that was a pretty rough start. It was 10 years that we were not a robotics company.

We were a simulation company. And then we had to build a robot in about a year.

So that was a little bit of a rough transition.

Can you just comment on the roughness of that transition?

Big dog. I mean, this is this big quadruped four legs robot.

We built a few different versions of them. But the first one, the very earliest ones,

didn't work very well. We would take them out and it was hard to get a go-kart engine

driving a hydraulic power. And having that all work while trying to get the robot to

stabilize itself. And so what was the power plan? What was the engine? It seemed like my vague

recollection. I don't know. It felt very loud and aggressive and kind of thrown together.

Oh, it absolutely was, right? We weren't trying to design the best robot hardware at the time.

And we wanted to buy an off-the-shelf engine. And so many of the early versions of big dog had

literally go-kart engines or something like that.

Are those gas-powered?

Like a gas-powered two-stroke engine. And the reason why it was two-stroke is

two-stroke engines are lighter weight. And we generally didn't put mufflers on them

because we're trying to save the weight. And we didn't care about the noise.

And so these things were horribly loud. But we're trying to manage weight because managing

weight in a legged robot is always important because it has to carry everything.

That said, that thing was big. Well, I've seen the videos of it.

Yeah. I mean, the early versions stood about, I don't know, belly high, chest high.

You know, they probably weighed maybe a couple of hundred pounds. But over the course of probably

five years, we were able to get that robot to really manage a remarkable level of rough terrain.

So, you know, we started out with just walking on the flat and then we started walking on rocks

and then inclines and then mud and then slippery mud. And, you know, by the end of that program,

we were convinced that legged locomotion in a robot could actually work because, you know,

going into it, we didn't know that. We had built quadrupeds at MIT. But they used a giant hydraulic

pump in the lab. They used a giant computer that was in the lab. They were always tethered to the

lab. This was the first time something that was sort of self-contained, you know,

walked around in the world and balanced. And the purpose was to prove to ourselves that the

legged locomotion could really work. And so, Big Dog really cut that open for us. And it was the

beginning of what became a whole series of robots. So, once we showed to DARPA that you could make

a legged robot that could work, there was a period at DARPA where robotics got really hot and there

was lots of different programs. And, you know, we were able to build other robots. We built other

quadrupeds to hand like LS3 designed to carry heavy loads. We built Cheetah, which was designed

to explore what are the limits to how fast you can run. You know, we began to build sort of a

portfolio of machines and software that let us build not just one robot, but a whole family of

robots. To push the limits in all kinds of directions. Yeah. And to discover those principles.

You know, you asked earlier about the art and science of legged locomotion. We were able to

develop principles of legged locomotion so that we knew how to build a small legged robot or a

big one. So, leg length, you know, was now a parameter that we could play with. Payload was

a parameter we could play with. So, we built the LS3, which was an 800-pound robot designed to carry

a 400-pound payload. And we learned the design rules, basically developed the design rules.

How do you scale different robot systems to, you know, their terrain, to their walking speed,

to their payload? So, when was spot born? Around 2012 or so. So, again, almost 10 years into sort

of a run with DARPA where we built a bunch of different quadrupeds. We had a sort of a different

thread where we started building humanoids. We saw that probably an end was coming where the

government was going to kind of back off from a lot of robotics investment. And in order to maintain

progress, we just deduced that, well, we probably need to sell ourselves to somebody who wants to

continue to invest in this area. And that was Google. And so, at Google, we would meet regularly

with Larry Page. And Larry just started asking us, you know, well, what's your product going to be?

And, you know, the logical thing, the thing that we had the most history with that we wanted to

continue developing was a quadruped. But we knew it needed to be smaller. We knew it couldn't have

a gas engine. We thought it probably couldn't be hydraulically actuated. So, that began the process

of exploring if we could migrate to a smaller, electrically actuated robot. And that was really

the genesis of spot. So, not a gas engine and the actuators are electric? Yes. So, can you maybe

comment on what it's like at Google with working with Larry Page, having those meetings and thinking

of what will the robot look like that could be built at scale, like starting to think about a

product? Larry always liked the toothbrush test. He wanted products that you used every day.

What they really wanted was, you know, a consumer level product, something that would work in your

house. We didn't think that was the right next thing to do because to be a consumer level product

cost is going to be very important. Probably needed to cost a few thousand dollars. And we were

building these machines that cost hundreds of thousands of dollars, maybe a million dollars

to build. Of course, we were only building two, but we didn't see how to get all the way to this

consumer level product. In a short amount of time. In a short amount of time. And he suggested that

we make the robots really inexpensive. And part of our philosophy has always been build the best

hardware you can. Make the machine operate well so that you're trying to solve, you know, discover

the hard problem that you don't know about. Don't make it harder by building a crappy machine,

basically. Build the best machine you can. There's plenty of hard problems to solve that are

going to have to do with, you know, underactuated systems and balance. And so we wanted to build

these high quality machines still. And we thought that was important for us to continue learning

about really what was the important parts of the make robots work. And so there was a little bit

of a philosophical difference there. And so ultimately, that's why we're building robots

for the industrial sector now, because the industry can afford a more expensive machine,

because, you know, their productivity depends on keeping their factory going. And so if spot costs,

you know, $100,000 or more, that's not such a big expense to them. Whereas at the consumer level,

no one's going to buy a robot like that. And I think we might eventually get to a consumer level

product that will be that cheap. But I think the path to getting there needs to go through

these really nice machines so we can then learn how to simplify. So what can you say to the almost

the engineering challenge of bringing down cost of a robot? So that presumably when you try to

build a robot at scale, that also comes into play when you're trying to make money on a robot,

even in the industrial setting. But how interesting, how challenging

of a thing is that, in particular, probably new to an R&D company.

Yeah, I'm glad you brought that last part up. The transition from an R&D company to a commercial

company, that's the thing you worry about, you know, because you've got these engineers who love

hard problems, who want to figure out how to make robots work. And you don't know if you have

engineers that want to work on the quality and reliability and cost that is ultimately required.

And indeed, you know, we have brought on a lot of new people who are inspired by those problems.

But the big takeaway lesson for me is we have good people, we have engineers who want to solve

problems. And the quality and cost and manufacturability is just another kind of problem.

And because they're so invested in what we're doing, they're interested in and will go work on

those problems as well. And so I think we're managing that transition very well. In fact,

I'm really pleased that, I mean, it's a huge undertaking, by the way, right? So,

even having to get reliability to where it needs to be, we have to have fleets of robots

that we're just operating 24-7 in our offices to go find those rare failures and eliminate them.

It's just a totally different kind of activity than the research activity where you get it to work,

you know, the one robot you have to work in a repeatable way, you know, at the high stakes

demo. It's just very different. But I think we're making remarkable progress, I guess.

So, one of the cool things I got a chance to visit Boston Dynamics and, I mean,

one of the things that's really cool is to see a large number of robots moving about.

Because I think one of the things you notice in the research environment at MIT, for example,

I don't think anyone ever has a working robot for a prolonged period.

Exactly. So, like, most robots are just sitting there in a sad

state of despair, waiting to be born, brought to life for a brief moment of time.

I just remember there's a spot robot just had like a cowboy hat on and was just walking randomly

for whatever reason. I don't even know. But there's a kind of sense of sentience to it,

because it doesn't seem like anybody was supervising it. It was just doing its thing.

I'm going to stop way short of the sentence. It is the case that if you come to our office,

you know, today and walk around the hallways, you're going to see a dozen robots just kind

of walking around all the time. And that's really a reliability test for us. So, we have

these robots programmed to do autonomous missions, get up off their charging dock,

walk around the building, collect data at a few different places and go sit back down.

And we want that to be a very reliable process, because that's what somebody who's running a

brewery, a factory, that's what they need the robot to do. So, we have to dog food our own

robot. We have to test it in that way. And so, on a weekly basis, we have robots that are accruing

something like 1,500 or maybe 2,000 kilometers of walking and, you know, over 1,000 hours of

operation every week. And that's something that almost, I don't think anybody else in the world

can do, because, hey, you have to have a fleet of robots to just accrue that much information.

You have to be willing to dedicate it to that test. And so, that's, but that's essential.

That's how you get the reliability. That's how you get it.

What about some of the cost cutting from the, from the manufacturer side? What have you learned

from the manufacturer side of the transition from R&D? And we're still, we're still learning

a lot there. We're learning how to cast parts instead of mill it all out of, you know, bill

it aluminum. We're learning how to get plastic molded parts. And we're learning about how to

control that process so that you can build the same robot twice in a row. There's a lot to learn

there. And we're only partway through that process. We've set up a manufacturing facility

in Wolfen. It's about a mile from our headquarters. And we're doing final assembly and test of both

spots and stretches, you know, at that factory. And, and it's hard because, to be honest, we're

still iterating on the design of the robot. As we find failures from these reliability tests,

we need to go engineer changes. And those changes need to now be propagated to the manufacturing

line. And that's a hard process, especially when you want to move as fast as we do. And that's been

challenging. And it makes it, you know, the folks who are working supply chain, who are trying to

get the cheapest parts for us, kind of requires that you buy a lot of them to make them cheap.

And then we go change the design from underneath them. And they're like, what are you doing? And so,

you know, getting everybody on the same page here that it, yep, we still need to move fast,

but we also need to try to figure out how to reduce costs. That's one of the challenges of,

of this migration we're going through. And over the past few years,

challenges to the supply chain. I mean, I imagine you've been a part of a bunch of

stressful meetings. Yeah, things got more expensive and harder to get. And yeah, so it's,

it's all been a challenge. Is there still room for simplification? Oh, yeah, much more. And, you

know, these are really just the first generation of these machines. We're already thinking about

what the next generation of spots going to look like. Spot was built as a platform. So you could

put almost any sensor on it. You know, we provided data communications, mechanical connections,

power connections. And, but for example, in the applications that we're excited about,

where you're, you're monitoring these factories for their health, there's probably a simpler

machine that we could build that's really focused on that use case. And that's the difference between

the general purpose machine or the platform versus the purpose built machine. And so even though,

even in the factory, we'd still like the robot to do lots of different tasks. If it's, if we really

knew on day one that we're going to be operating in a factory with these three sensors in it,

we would have it all integrated in a package that would be easier, more less expensive and more

reliable. So we're contemplating building, you know, a next generation of that machine.

So we should mention that the spot for people who are somehow not familiar. So it's a yellow robotic

dog and has been featured in many dance videos. It also has gained an arm. So what can you say

about the arm that spot has, about the challenges of this design and the manufacture of it?

We think the future of mobile robots is mobile manipulation. That's where, you know, in the

past 10 years, it was getting mobility to work, getting the leg and locomotion to work. If you

ask, what's the hard problem in the next 10 years, it's getting a mobile robot to do useful

manipulation for you. And so we wanted Spot to have an arm to experiment with those problems.

And the arm is almost as complex as the robot itself, you know, and it's an attachable payload.

It has, you know, several motors and actuators and sensors that has a camera in the end of its

hand. So, you know, you can sort of see something and the robot will control the motion of its hand

to go pick it up autonomously. So in the same way the robot walks and balances, managing its own

foot placement to say balance, we want manipulation to be mostly autonomous where the robot, you

indicate, okay, go grab that bottle and then the robot will just go do it using the camera in its

hand and then sort of closing in on that, the grasp. But it's, it's a whole nother complex robot on

top of a complex legged robot. And so, and of course, we made it the hand look a little like a

head, you know, because again, we want it to be sort of identifiable. In the last year,

a lot of our sales have been people who already have a robot now buying an arm to add to that robot.

Oh, interesting. And so the arm is for sale.

Oh, yeah. Oh, yeah. It's an option.

What's the, what's the interface like to work with arm? Like, is it pretty, so are they designed

primarily, I guess just ask that question in general about robots from Boston Dynamics,

is it designed to be easily and efficiently operated remotely by a human being? Or is there

also the capability to push towards autonomy? We want both. In the next version of the software

that we release, which will be version 3.3, we're going to offer the ability of, if you have an

autonomous mission for the robot, we're going to include the option that it can go through a door,

which means it's going to have to have an arm and it's going to have to use that arm to open the

door. And so that'll be an autonomous manipulation task that just, you can program easily with the

robot strictly through, you know, we have a tablet interface. And so on the tablet, you know,

you sort of see the view that spot sees, you say, there's the door handle, you know, the hinges are

on the left and it opens in, the rest is up to you. Take care of it. So it just takes care of

everything. Yeah. So we want, and for a task like opening doors, you can automate most of that. And

we've automated a few other tasks. We had a customer who had a high powered breaker switch,

essentially, it's an electric utility Ontario power generation. And they have to, when they're

going to disconnect, you know, their power supply, right, that could be a gas generator, could be a

nuclear power plant, you know, from the grid, you have to disconnect this breaker switch. Well,

as you can imagine, there's, you know, hundreds or thousands of amps and volts involved in this

breaker switch. And it's a dangerous event, because occasionally you'll get what's called an arc flash

as you just do this disconnect, the power, the sparks jump across and people die doing this.

And so Ontario power generation used our spot in the arm through the interface to

operate this disconnect in an interactive way. And they showed it to us. And we were so excited

about it and said, you know, I bet we can automate that task. And so we got some examples of that

breaker switch. And I believe in the next generation of software, now we're going to

deliver back to Ontario power generation, they're going to be able to just point the robot

at that breaker. They'll be out, they'll indicate that's the switch. There's sort of two actions

you have to do. You have to flip up this little cover, press a button, then get a ratchet, stick

it into a socket and literally unscrew this giant breaker switch. So there's a bunch of different

tasks. And we basically automated them so that the human says, okay, there's the switch, go do that

part. That right there is the socket where you're going to put your tool and you're going to open

it up. And so you can remotely sort of indicate this on the tablet. And then the robot just does

everything in between. And it does everything, all the coordinated movement of all the different

actuators that includes the body. Yeah, maintains its balance. It walks itself, you know, into

position. So it's within reach. And the arm is in a position where it can do the whole task.

So it manages the whole body. So how does one become a big enough customer to request features?

Because I personally want a robot that gets me a beer. I mean, that has to be like one of the most

requests, I suppose in the industrial setting, that's a non-alcoholic beverage of picking up

objects and bringing the objects to you. We love working with customers who have challenging

problems like this. And this one in particular, because we felt like what they were doing,

A, it was a safety feature. B, we saw that the robot could do it because they tele-operated it

the first time, probably took them an hour to do it the first time, right? But the robot was clearly

capable. And we thought, oh, this is a great problem for us to work on to figure out how to

automate a manipulation task. And so we took it on, not because we were going to make a bunch

of money from it in selling the robot back to them, but because it motivated us to go solve

what we saw as the next logical step. But many of our customers, in fact, we try to,

our bigger customers, typically ones who are going to run a utility or a factory or something like

that, we take that kind of direction from them. And if they're, especially if they're going to

buy 10 or 20 or 30 robots and they say, I really need it to do this, well, that's exactly the

right kind of problem that we want to be working on. And so note the self, buy 10 spots and aggressively

push for beer manipulation. I think it's fair to say it's notoriously difficult to make a lot of

money as a robotics company. How can you make money as a robotics company? Can you speak to that?

It seems that a lot of robotics companies fail. It's difficult to build robots. It's difficult to

build robots at a low enough cost where customers, even the industrial setting want to purchase them.

And it's difficult to build robots that are useful, sufficiently useful. So what can you speak to?

And Boston Dynamics has been successful for many years of finding a way to make money.

Well, in the early days, of course, the money we made was from doing contract R&D work.

And we made money, but we weren't growing and we weren't selling a product. And then we went

through several owners who had a vision of not only developing advanced technology,

but eventually developing products. And so both Google and SoftBank and now Hyundai

had that vision and were willing to provide that investment.

Now, our discipline is that we need to go find applications that are broad enough that you

could imagine selling thousands of robots because it doesn't work if you don't sell thousands or

tens of thousands of robots. If you only sell hundreds, you will commercially fail. And that's

where most of the small robot companies have died. And that's a challenge because

A, you need to field the robots. They need to start to become reliable. And as we said,

that takes time and investment to get there. And so it really does take visionary investment to

get there. But we believe that we are going to make money in this industrial monitoring space

because if a chip fab, if the line goes down because a vacuum pump failed some place, that

can be in a very expensive process. It can be a million dollars a day in lost production. Maybe

you have to throw away some of the product along the way. And so the robot, if you can prevent that

by inspecting the factory every single day, maybe every hour if you have to,

there's a real return on investment there. But there needs to be a critical mass of this task.

And we're focusing on a few that we believe are ubiquitous in the industrial production

environment. And that's using a thermal camera to keep things from overheating, using an acoustic

imager to find compressed air leaks, using visual cameras to read gauges, measuring vibration.

These are standard things that you do to prevent unintended shutdown of a factory.

And this takes place in a beer factory. We're working with AB INVEV. It takes place in chip

fabs. We're working with global foundries. It takes place in electric utilities and nuclear

power plants. And so the same robot can be applied in all of these industries. And as I said, we have

actually it's 1,100 spots out now to really get profitability. We need to be at 1,000 a year,

maybe 1,500 a year for that sort of part of the business. So it still needs to grow,

but we're on a good path. So I think that's totally achievable.

So the application should require crossing that 1,000 robot barrier?

It really should. Yeah. I want to mention our second robot, Stretch.

Tell me about Stretch. What's Stretch? Who is Stretch?

Stretch started differently than Spot. Spot, we built because we had

decades of experience building quadrupeds. We had it in our blood. We had to build

a quadruped product, but we had to go figure out what the application was. And we actually

discovered this factory patrol application, basically preventative maintenance, by seeing what

our customers did with it. Stretch is very different. We started knowing that there was

warehouses all over the world. There's shipping containers moving all around the world full

of boxes that are mostly being moved by hand. By some estimates, we think there's a trillion boxes,

cardboard boxes shipped around the world each year. And a lot of it's done manually.

It became clear early on that there was an opportunity for a mobile robot in here to move

boxes around. And the commercial experience has been very different between Stretch and with Spot.

As soon as we started talking to people, potential customers about what Stretch was going to be used

for, they immediately started saying, oh, I'll buy that robot. In fact, I'm going to put in

an order for 20 right now. We just started shipping the robot in January after several years of

development this year. So our first deliveries of Stretch to customers were DHL and Merisk in

January. We're delivering a gap right now. And we have about seven or eight other customers,

all who've already agreed in advance to buy between 10 and 20 robots. And so we've already got

commitments for a couple hundred of these robots. This one's going to go, right? It's so obvious

that there's a need. And we're not just going to unload trucks. We're going to do any box

moving task in the warehouse. And so it too will be a multi-purpose robot. And we'll eventually

have it doing palletizing or depalletizing or loading trucks or unloading trucks. There's

definitely thousands of robots. There's probably tens of thousands of robots of this in the future.

So it's going to be profitable. Can you describe what Stretch looks like?

It looks like a big, strong robot arm on a mobile base. The base is about the size of a

pallet. And we want it to be the size of a pallet because that's what lives in warehouses, right?

Palettes of goods sitting everywhere. So we needed to be able to fit in that space.

But it's not a legged robot. It's not a legged robot. And so it was our first...

It was actually a bit of a commitment from us, a challenge for us to build a non-balancing robot.

To do the much easier problem.

But to do... Well, because it wasn't going to have this balance problem. And in fact,

the very first version of the logistics robot we built was a balancing robot. And that's called

Handel. And that thing was epic. Oh, it's a beautiful machine.

It's an incredible machine. So it was... I mean, it looks epic. It looks like out of

a sci-fi movie of some sort. Can you actually just linger on the design of that thing? Because

that's another leap into something you probably haven't done. It's a different kind of balancing.

Yeah. So I love talking about the history of how Handel came about. Because it connects all of

our robots, actually. So I'm going to start with Atlas. When we had Atlas getting fairly far along,

we wanted to understand... I was telling you earlier, the challenge of the human form is that

you have this mass up high. And balancing that inertia, that mass up high, is its own unique

challenge. And so we started trying to get Atlas to balance standing on one foot, like on a balance

beam, using its arms like this. And you can do this, I'm sure. I can do this, right? Like,

if you're walking a tightrope, how do you do that balance? So that's sort of controlling the inertia,

controlling the momentum of the robot. We were starting to figure that out on Atlas.

And so our first concept of Handel, which was a robot that was going to be on two wheels,

so it had the balance, but it was going to have a big long arm so it could reach a box at the top

of a truck. And it needed yet another counterbalance, a big tail, to help it balance while it was using

its arm. So the reason why this robot sort of looks epic, some people said it looked like

an ostrich or maybe an ostrich moving around, was the wheels, it has legs so it can extend its legs.

So it's wheels on legs, we always wanted to build wheels on legs, it had a tail and had this arm,

and they're all moving simultaneously and in coordination to maintain balance,

because we had figured out the mathematics of doing this momentum control, how to maintain that

balance. And so part of the reason why we built this two-legged robot was we had figured this thing

out, we wanted to see it in this kind of machine, and we thought maybe this kind of machine would

be good in a warehouse, and so we built it. And it's a beautiful machine, it moves in a graceful

way like nothing else we've built, but it wasn't the right machine for a logistics application,

we decided it was too slow and couldn't pick boxes fast enough basically.

And it was doing beautifully with elegance.

Beautifully, but it just wasn't efficient enough. So we let it go. But I think we'll come back to

that machine eventually. The fact that it's possible, the fact that you showed that you could

do so many things at the same time in coordination, and so beautifully, there's something there.

That was a demonstration of what is possible. Basically, we made a hard decision, and this

was really kind of a hard-nosed business decision. It indicated us not doing it just for the beauty

of the mathematics or the curiosity, but no, we actually need to build a business that

can make money in the long run. And so we ended up building stretch, which has a big heavy base

with a giant battery in the base of it, that allows it to run for two shifts, 16 hours worth of

operation. And that big battery sort of helps it stay balanced. So you can move a 50-pound box

around with its arm and not tip over. It's omnidirectional, it can move in any direction,

so it has a nice suspension built into it so it can deal with gaps or things on the floor

and roll over it. But it's not a balancing robot. It's a mobile robot arm that can work

to carry or pick or place a box up to 50 pounds anywhere in the warehouse.

Take a box from point A to point B anywhere. Yeah. Palatize, depalatize. We're starting

with unloading trucks because there's so many trucks and containers that were goods are shipped,

and it's a brutal job. In the summer, it can be 120 degrees inside that container.

People don't want to do that job. And it's backbreaking labor. Again, these can be up to 50

pound boxes. And so we feel like this is a productivity enhancer. And for the people who

used to do that job, unloading trucks, they're actually operating the robot now. And so by

building robots that are easy to control and it doesn't take an advanced degree to manage,

you can become a robot operator. And so as we've introduced these robots to both DHL

and Merisk and Gap, the warehouse workers who were doing that manual labor are now the robot

operators. And so we see this as ultimately a benefit to them as well. Can you say how much

stretch costs? Not yet. But I will say that when we engage with our customers,

they'll be able to see a return on investment in typically two years.

Okay. So that's something that you're constantly thinking about how. And I suppose you have to

do the same kind of thinking with spot. So it seems like with stretch, the application is

like directly obvious. Yeah, it's a slam dunk. Yeah. And so you have a little more flexibility.

Well, I think we know the target. We know what we're going after. And with Spot,

it took us a while to figure out what we were going after. Well, let me return to that question

about maybe the conversation you were having a while ago with Larry Page, maybe looking to the

longer future of social robotics, of using Spot to connect with human beings, perhaps in the home.

Do you see a future there? If we were to sort of hypothesize or dream about a future where

a spot like robots are in the home as pets, a social robot? We definitely think about it.

And we would like to get there. We think the pathway to getting there is likely through these

industrial applications and then mass manufacturing. Let's figure out how to build the robots,

how to make the software so that they can really do a broad set of skills that's going to take

real investment to get there. Performance first, right? The principle of the company has always

been really make the robots do useful stuff. And so the social robot companies that try to start

someplace else by just making a cute interaction, mostly they haven't survived.

And so we think the utility really needs to come first. And that means you have to solve

some of these hard problems. And so to get there, we're going to go through the design

and software development in industrial and then that's eventually going to let you reach a scale

that could then be addressed to a consumer level market. And so yeah, maybe we'll be able to build

a smaller spot with an arm that could really go get your beer for you. But there's things we need

to figure out still, how to safely, really safely. And if you're going to be interacting with children,

you better be safe. And right now we count on a little bit of standoff distance between the robot

and people so that you don't pinch a finger in the robot. So you've got a lot of things you need

to go solve before you jump to that consumer level product. Well, there's a kind of tradeoff and safety

because it feels like in the home, you can fall. You don't have to be as good at...

You're allowed to fail in different ways, in more ways, as long as it's safe for the humans.

So it just feels like an easier problem to solve because it feels like in the factory,

you're not allowed to fail. That may be true. But I also think the variety of things,

a consumer level robot would be expected to do will also be quite broad.

They're going to want to get the beer and know the difference between the beer and the Coca-Cola

or my snack. They're all going to want you to clean up the dishes

from the table without breaking them. Those are pretty complex tasks. And so there's still work

to be done there. So to push back on that, here's what application I think they'll be very interesting.

I think the application of being a pet, a friend. So no tasks, just be cute.

Because they're not cute, not cute. A dog is more than just cute. A dog is a friend,

is a companion. There's something about just having interacted with them and maybe because

I'm hanging out alone with the robot dogs a little too much. But there's a connection there.

And it feels like that connection should not be disregarded.

No, it should not be disregarded. Robots that can somehow communicate through

their physical gestures are you're going to be more attached to in the long run.

Do you remember Ibo, the Sony Ibo? They sold over 100,000 of those, maybe 150,000.

You know, it probably wasn't considered a successful product for them.

They suspended that eventually. And then they brought it back, Sony brought it back.

And people definitely treated this as a pet, as a companion.

And I think that will come around again. Will you get away without having any other utility?

Maybe in a world where we can really talk to our simple little pet because

JetGPT or some other generative AI has made it possible for you to really talk in what seems

like a meaningful way. Maybe that'll open the social robot up again. That's probably not a path

we're going to go down because, again, we're so focused on performance and utility.

We can add those other things also, but we really want to start from that foundation of utility,

I think. Yeah. But I also want to predict that you're wrong on that. So which is that

the very path you're taking, which is creating a great robot platform will very easily take a leap

to adding a JetGPT-like capability, maybe GPT-5. And there's just so many open source

alternatives that you could just plop that on top of spot. And because you have this robust platform

and you're figuring out how to mass manufacture it and how to drive the cost down

and how to make it reliable, all those kinds of things, it'll be a natural transition

to where just adding JetGPT on top of it will create- Oh, I do think that being able to verbally

converse or even converse through gestures, part of these learning models is that you can now look

at video and imagery and associate intent with that. Those will all help in the communication

between robots and people, for sure. And that's going to happen, obviously, more quickly than any

of us were expecting. I mean, what else do you want from life? Friend to get your beer and then

just talk shit about the state of the world. I mean, where's a deep loneliness within all of us,

and I think a beer and a good chat solves so much of it, or it takes us a long way to solving a lot.

It'll be interesting to see when a generative AI can give you that warm feeling that you connected

and that, oh, yeah, you remember me, you're my friend, we have a history. That history matters,

memory of joint, having witnessed, that's what friendship, that's what connection,

that's what love is in many cases. Some of the deepest friendships you have is

having gone through a difficult time together and having a shared memory of an amazing time or a

difficult time and that memory creating this foundation based on which you can then experience

the world together. The silly, the mundane stuff of day to day is somehow built on a foundation

of having gone through some shit in the past. And the current systems are not personalized in that

way, but I think that's a technical problem, not some kind of fundamental limitation. So

combine that with an embodied robot like Spot, which already has magic in its movement. I think

it's a very interesting possibility of where that takes us. But of course, you have to build that

on top of a company that's making money with real application with real customers

and with robots that are safe and at work and reliable and manufactured scale.

And I think we're in a unique position in that because of our investors primarily

Hyundai, but also SoftBanks alone is 20% of us. They're not totally fixated on driving us to

profitability as soon as possible. That's not the goal. The goal really is a longer term vision

of creating what does mobility mean in the future? How is this mobile robot technology

going to influence us? Can we shape that? And they want both. And so we are, as a company,

are trying to strike that balance between let's build a business that makes money.

I've been describing that to my own team as self-destination. If I want to drive my own ship,

we need to have a business that's profitable in the end, otherwise somebody else is going to drive

the ship for us. So that's really important. But we're going to retain the aspiration that

we're going to build the next generation of technology at the same time. And the real

trek will be if we can do both. Speaking of ships, let me ask you about a competitor.

And somebody who's become a friend. So you almost can Tesla have announced have been

in the early days of building a human robot. How does that change the landscape of your work?

So from the outside perspective, it seems like, as a fan of robotics, it just seems exciting.

Very exciting. When Elon speaks, people listen. And so it suddenly brought a bright light onto

the work that we've been doing for over a decade. And I think that's only going to help. And in

fact, what we've seen is that, in addition to Tesla, we're seeing a proliferation of robotic

companies arise now. Clean and humanoid? Yes. Oh, wow. Yeah. And interestingly, many of them,

as they're raising money, for example, will claim whether or not they have a former Boston

Dynamics employee on their staff as a criteria. Yeah, that's true. I would do that. It's a company,

yeah, for sure. And shows you're legit. Yeah. So it has brought a tremendous validation to what

we're doing and excitement. Competitive juices are flowing, the whole thing. So it's all good.

Elon has also kind of stated that maybe he implied that the problem is solvable in your term,

which is a low cost humanoid robot that's able to do, that's a relatively general use case robot.

So I think Elon is known for sort of setting these kinds of incredibly ambitious goals,

maybe missing deadlines, but actually pushing not just the particular team he leads,

but the entire world to like accomplishing those. Do you see Boston Dynamics in the near

future being pushed in that kind of way? Like this excitement of competition kind of

pushing Atlas maybe to do more cool stuff, trying to drive the cost of Atlas down,

perhaps? Or I mean, I guess I want to ask if there's some kind of exciting energy in Boston

Dynamics due to this a little bit of competition. Oh yeah, definitely. When we released our most

recent video of Atlas, I think you'd seen it scaffolding and throwing the box of tools around

and then doing the flip at the end. We were trying to show the world that not only can we do this

parkour mobility thing, but we can pick up and move heavy things. Because if you're going to work

in a manufacturing environment, that's what you got to be able to do. And for the reasons I explained

to you earlier, it's not trivial to do so. Changing the center of mass by picking up a 50-pound

block for a robot that weighs 150 pounds, that's a lot to accommodate. So we're trying to show

that we can do that. So it's totally been energizing. We see the next phase of Atlas being

more dexterous hands that can manipulate and grab more things, that we're going to start by moving

big things around that are heavy and that affect balance. And why is that? Well, really tiny dexterous

things probably are going to be hard for a while yet. Maybe you could go build a special purpose

robot arm for stuffing chips into electronics boards, but we don't really want to do

really fine work like that. I think more coursework, where you're using two hands to pick up and

balance an unwieldy thing, maybe in a manufacturing environment, maybe in a construction environment,

those are the things that we think robots are going to be able to do with the level of dexterity

that they're going to have in the next few years. And that's where we're headed. And I think,

Elon has seen the same thing. He's talking about using the robots in a manufacturing environment.

We think there's something very interesting there about having a two-armed robot. Because when

you have two arms, you can transfer a thing from one hand to the other, you can turn it around,

you can reorient it in a way that you can't do it if you just have one hand on it. And so there's

a lot that extra arm brings to the table. So I think in terms of mission, you mentioned Boston

really wants to see what's the limits of what's possible. And so the cost comes second. Or it's

a component, but first figure out what are the limitations. I think, Elon, he's really driving

the cost down. Is there some inspiration, some lessons you see there of the challenge of driving

the cost down, especially with the Atlas, with the humanoid robot? Well, I think the thing that

he's certainly been learning by building car factories is what that looks like in scaling.

By scaling, you can get efficiencies that drive cost down very well. And the smart thing that

they have in their favor is they know how to manufacture, they know how to build electric

motors, they know how to build computers and vision systems. So there's a lot of overlap

between modern automotive companies and robots. But hey, we have a modern robotic, I mean,

that automotive company behind us as well. So bring it on. Who's doing pretty well, right?

The electric vehicles from Hyundai are doing pretty well. I love it. So we've talked about some of the

low level control, some of the incredible stuff that's going on and basic perception.

But how much do you see currently in the future of Boston Dynamics sort of more high level machine

learning applications? Do you see customers adding on those capabilities or do you see

Boston Dynamics doing that in-house? Some kinds of things we really believe are probably going to be

more broadly available, maybe even commoditized, using a machine learning like a vision algorithm.

So a robot can recognize something in the environment. That ought to be something you

can just download. I'm going to a new environment, I have a new kind of door handle or piece of

equipment I want to inspect, you ought to be able to just download that. And I think people,

besides Boston Dynamics, will provide that. And we've actually built an API that lets people add

these vision algorithms to spot. And we're currently working with some partners who are

providing that. Levitas is an example of a small provider who's giving us software for reading

gauges. And actually another partner in Europe, Reply, is doing the same thing.

So we see that, we see ultimately an ecosystem of providers doing stuff like that. And I think

ultimately, you might even be able to do the same thing with behaviors. So this technology will

also be brought to bear on controlling the robot, the motions of the robot. And we're using learning,

reinforcement learning to develop algorithms for both locomotion and manipulation. And ultimately,

this is going to mean you can add new behaviors to a robot quickly. And that could potentially be

done outside of Boston Dynamics right now. And that's all internal to us. I think you need to

understand at a deep level, the robot control to do that. But eventually, that could be outside.

But it's certainly a place where these approaches are going to be brought to bear in robotics.

So reinforcement learning is part of the process. So you do use reinforcement learning?

Yes. So there's increasing levels of learning with these robots?

Yes. And that's for both for locomotion, for manipulation and for perception?

Yes. Well, what do you think in general about all the exciting advancements of

transformer neural networks most beautifully illustrated through the large language models

like GPT-4? Like everybody else, we're all, I'm surprised at how far they've come.

I'm a little bit nervous about the, there's anxiety around them, obviously, for, I think,

good reasons, right? Disinformation is a curse that's an unintended consequence of social media

that could be exacerbated with these tools. So if you use them to deploy disinformation,

it could be a real risk. But I also think that the risks associated with these kinds of models

don't have a whole lot to do with the way we're going to use them in our robots. If I'm using a

robot, I'm building a robot to do a manual task of some sort. I can judge very easily. Is it doing

the task I asked it to? Is it doing it correctly? There's sort of a built-in mechanism for judging.

Is it doing the right thing? Did it successfully do the task?

Yeah, physical reality is a good verifier.

It's a good verifier. That's exactly it. Whereas if you're asking for, yeah, I don't know,

trying to ask a theoretical question in chat GPT, it could be true or it may not be true. And

it's hard to have that verifier. What is that truth that you're comparing against? Whereas in

physical reality, you know the truth. And this is an important difference. And so I'm not,

I think there is reason to be a little bit concerned about how these tools,

large language models could be used. But I'm not very worried about how they're going to be used.

Well, how learning algorithms in general are going to be used on robotics. It's really a

different application that has different ways of verifying what's going on.

Well, the nice thing about language models is that I ultimately see, I'm really excited about the

possibility of having conversations at the spot. There's no, I would say negative consequences to

that, but just increasing the bandwidth and the variety of ways you can communicate with this

particular robot. So you could communicate visually, you can communicate through some

interface and to be able to communicate verbally again with the beer and so on.

I think that's really exciting to make that much, much easier.

We have this partner, Levitas, that's adding the vision algorithms for daydreaming for us.

They just, just this week, I saw a demo where they hooked up, you know, a language tool to

spot and they're talking to spot to give a chance. Can you tell me about the Boston Dynamics AI

Institute? What is it and what is its mission?

So it's a separate organization, the Boston Dynamics Artificial Intelligence Institute.

It's led by Mark Raybird, the founder of Boston Dynamics and the former CEO

and my old advisor at MIT. Mark has always loved the research, the pure research,

without the confinement or demands of commercialization. And he wanted to continue to pursue

that unadulterated research and so suggested to Hyundai that he set up this institute and they

agree that it's worth additional investment to kind of continue pushing this forefront.

And we expect to be working together where Boston Dynamics can both commercialize and do

research, but the sort of time horizon of the research we're going to do is, you know, in the

next, let's say five years, you know, what can we do in the next five years? Let's work on those

problems. And I think the goal of the AI Institute is to work even further out. Certainly, the analogy

of luggage locomotion again, when we started that, that was a multi-decade problem. And so I

think Mark wants to have the freedom to pursue really hard over the horizon problems. And that's,

that'll be the goal of the Institute. So we mentioned some of the dangers of some of the

concerns about large language models. That said, you know, there's been a long running

fear of these embodied robots. Why do you think people are afraid of Lincoln robots?

Yeah, I wanted to show you this. So this is in the Wall Street Journal. And this is all about

chat GPT, right? But look at the picture. Yeah. It's a humanoid robot. That's saying, I will

replace it. That's saying, it looks scary and it says, I'm going to replace you. And so the

humanoid robot is sort of, is the embodiment of this chat GPT tool that there's reason to be a

little bit nervous about how it gets deployed. So I'm nervous about that connection. It's

unfortunate that they chose to use a robot as that embodiment for, as you and I just said,

there's big differences in this. But people are afraid because we've been

taught to be afraid for over a hundred years. So, you know, the word robot was developed by a

playwright named Carol Chappek in 1921 to check a playwright for Rossum's Universal Robots.

And in that first depiction of a robot, the robots took over the end of the story.

And, you know, people love to be afraid. And so we've been entertained by these stories for a

hundred years. But I, and I think that's as much why people are afraid as anything else,

as we've been sort of taught that this is the logical progression through fiction.

I think it's fiction. I think what people more and more will realize,

just like you said, that the threat, like say you have a super intelligent AI embodied

in a robot, that's much less threatening because it's visible. It's verifiable. It's right there

in physical reality. And we humans know how to deal with physical reality. I think it's much

scarier when you have arbitrary scaling of intelligent AI systems in the digital space

that they could pretend to be human. So robot, spot is not going to be pretend,

it could pretend it's human all at once. You could tell you, you could put your

LGBT on top of it, but you're going to know it's not human because you have a contact

with physical reality. And you're going to know whether or not it's doing what you asked it to

do. Yeah. Like, it's not going to, like if it, I mean, I'm sure you can start just like a dog

lies to you. Like I didn't, I wasn't part of tearing up that couch. So spot can try to lie that,

like, you know, it wasn't me that spilled that thing, but you're going to kind of figure it out

eventually. It's, if it happens multiple times, you know, but I think that humanity has figured

out how to make machines safe. And there's, you know, the regulatory environments and certification

protocols that we've developed in order to figure out how to make machines safe. We don't know,

we, and don't have that experience with software that can be propagated worldwide in an instant.

And so I think we needed to develop those protocols and those tools. And so

that's work to be done, but I don't think the fear of that and that work should necessarily impede

our ability to now get robots out. Because again, I think, I think we can judge when a robot's being

safe. So, and again, just like in that image, there's a fear that robots will take our jobs. I

just, I took a ride, I was in San Francisco, I took a ride in the Waymo vehicles and the Thomas

vehicle. And I was on it several times. They're doing incredible work over there. But people

flicked it off the car. So I mean, that's a long story of what the psychology of that is. It could

be maybe big tech or what I don't know exactly what they're flicking off. But there is an element

of like these robots are taking our jobs or irreversibly transforming society such that it

will have economic impact and the little guy will be, would lose a lot, would lose their well-being.

Is there something to be said about the fear that robots will take our jobs?

You know, at every significant technological transformation, there's been fear of, you know,

an automation anxiety that it's going to have a broader impact than we expected.

And there will be jobs will change. Sometime in the future, we're going to look back at people

who manually unloaded these boxes from trailers and we're going to say, why did we ever do that

manually? But there's a lot of people who are doing that job today that it could be impacted.

But I think the reality is, as I said before, we're going to build the technologies so that

those very same people can operate it. And so I think there's a pathway to upskilling and operating

just like, look, we used to farm with hand tools and now we farm with machines and nobody has really

regretted that transformation. And I think the same can be said for a lot of manual labor that

we're doing today. And on top of that, you know, look, we're entering a new world where demographics

are going to have strong impact on economic growth. And the, you know, the advanced, the first world

is losing population quickly. In Europe, they're worried about hiring enough people just to keep

the logistics supply chain going. And, you know, part of this is the response to COVID and everybody's

thinking back what they really want to do with their life. But these jobs are getting harder

and harder to fill. And I just, I'm hearing that over and over again. So I think, frankly,

this is the right technology at the right time where we're going to need some of this work

to be done. And we're going to want tools to enhance that productivity.

And the scary impact, I think, again, GPT comes to the rescue in terms of being much more terrifying.

The scary impact of, basically, so I'm a, I guess, a software person. So I program a lot. And

the fact that people like me can be easily replaced by GPT, that's going to have...

Well, and a lot, you know, anyone who deals with texts and writing a draft proposal might be easily

done with chat GPT now, where it wasn't before. Consultants, journalists, everybody is quite...

But on the other hand, you also want it to be right. And they don't know how to make it right yet.

But it might make a good starting point for you to iterate.

Boy, do I have to talk to you about modern journalism. That's another conversation all

together. But yes, more right than the average, the mean journalist, yes.

You spearheaded the anti-weaponization letter Boston Dynamics has. Can you describe

what that letter states and the general topic of the use of robots in war?

We authored a letter and then got several leading robotics companies around the world,

including, you know, Unitree and China and Agility here in the United States and

Animal in Europe and some others, to co-sign a letter that said we won't put weapons on our

robots. And part of the motivation there is, you know, as these robots start to become

commercially available, you can see videos online of people who've gotten a robot and

strapped a gun on it and shown that they can operate the gun remotely while driving the robot

around. And so having a robot that has this level of mobility and that can easily be configured

in a way that could harm somebody from a remote operator is justifiably a scary thing.

And so we felt like it was important to draw a bright line there and say we're not going to allow

this for, you know, reasons that we think ultimately it's better for the whole industry

if it grows in a way where robots are ultimately going to help us all and make our lives more

fulfilled and productive. But by goodness, you're going to have to trust the technology

to let it in. And if you think the robot's going to harm you, that's going to impede the growth

of that industry. So we thought it was important to draw a bright line and then publicize that.

And our plan is to begin to engage with lawmakers and regulators.

Let's figure out what the rules are going to be around the use of this technology

and use our position as leaders in this industry and technology to help force that issue.

And so we are, in fact, I have a policy director at my company whose job it is to engage

with the public to engage with interested parties and including regulators to sort of begin these

discussions. Yes, and a really important topic and it's an important topic for people that worry

about the impact of robots on our society with autonomous weapon systems. So I'm glad you're

sort of leading the way in this. You are the CEO of Boston Dynamics. What's the take to be a CEO

of a robotics company? So you started as a humble engineer, PhD, just looking at your journey.

What does it take to go from being, from building the thing to leading a company? What are some

of the big challenges for you? Courage, I would put front and center for multiple reasons.

I talked earlier about the courage to tackle hard problems. So I think there's courage required not

just of me but of all of the people who work at Boston Dynamics. I also think we have a lot of

really smart people. We have people who are way smarter than I am. And it takes a kind of courage

to be willing to lead them and to trust that you have something to offer to somebody who

probably is maybe a better engineer than I am. Adaptability. It's been a great career for me.

I never would have guessed I'd stayed in one place for 30 years. And the job has always changed.

I didn't really aspire to be CEO from the very beginning, but it was the natural progression

of things. There was always needed to be some level of management that was needed. And so

when I saw something that needed to be done that wasn't being done, I just stepped in to go do it.

And oftentimes, because we were full of such strong engineers, oftentimes that was

in the management direction, or it was in the business development direction,

or organizational hiring. Geez, I was the main person hiring at Boston Dynamics for probably

20 years. So I was the head of HR basically. So I just willingness to tackle any piece of the

business that needs it and then be willing to shift. Is there something you could say to

what it takes to hire a great team? What's a good interview process? How do you know

the guy or gal are going to make a great member of an engineering team that's doing some of the

hardest work in the world? We developed an interview process that I was quite fond of.

It's a little bit of a hard interview process because the best interviews, you ask somebody

about what they're interested in and what they're good at. And if they can describe to you

something that they worked on and you saw they really did the work, they solved the problems

and you saw their passion for it. And you could ask, but what makes that hard is you have to ask

a probing question about it. You have to be smart enough about what they're telling you,

their expert at, to ask a good question. And so it takes a pretty talented team to do that.

But if you can do that, that's how you tap into, ah, this person cares about their work.

They really did the work. They're excited about it. That's the kind of person I want at my company.

At Google, they taught us about their interview process and it was a little bit different.

Um, you know, we evolved the process at Boston Dynamics where it didn't matter if you were an

engineer or you were an administrative assistant or a financial person or a technician. You gave

us a presentation. You came in and you gave us a presentation. You had to stand up and talk in

front of us. And I just thought that was great to tap into those things I just described to you.

At Google, they taught us, and I think I understand why, right? They're hiring tens of

thousands of people. They need a more standardized process. So they would sort of err on the other

side where they would ask you a standard question. I'm going to ask you a programming question,

and I'm just going to ask you to write code in front of me. That's a terrifying, you know,

application process. Yeah. It does let you compare candidates really well, but it doesn't

necessarily let you tap in to who they are, right? Because you're asking them to answer your question

instead of you asking them about what they're interested in. Um, but frankly, that process

is hard to scale. And even at Boston Dynamics, we're not doing that with everybody anymore,

but we are still doing that with, you know, the tech, the technical people. But we, because we

too now need to sort of increase our rate of hiring. Not everybody's giving a presentation

anymore. But you're still ultimately trying to find that basic seed of passion before and

after the world. Yeah. You know, did they really do it? Did they find something interesting or

curious, you know, and do they care about it? I think somebody admires Jim Caller, and he

likes details. So one of the ways you could, if you get a person to talk about what they're

interested in, how many details, like how much of the whiteboard can you fill out? Yeah. Well,

I think you figure out, did they really do the work if they know some of the details? Yes. And

if they have to wash over the details, well, then they didn't do it. Especially with engineering,

the work is in the details. Yeah. I have to go there briefly just to get your kind of thoughts

in the longterm future of robotics. There's been discussions on the GPT side on the large

language model side of whether there's consciousness inside these language models.

And I think there's fear, but I think there's also excitement, or at least the wide world

of opportunity and possibility in embodied robots having something like, let's start with emotion,

love towards other human beings, and perhaps the display real or fake of consciousness.

Is this something you think about in terms of long term future? Because as we've talked about,

people do anthropomorphize these robots. It's difficult not to project some level of,

I use the word sentience, some level of sovereignty, identity, all the things we think

as human. That's what anthropomorphization is. We project humanness onto mobile, especially

legged robots. Is that something almost from a science fiction perspective you think about,

or do you try to avoid ever, try to avoid the topic of consciousness altogether?

I'm certainly not an expert in it, and I don't spend a lot of time thinking about this, right?

And I do think it's fairly remote for the machines that we're dealing with.

Our robots, you're right, the people anthropomorphized, they read into the robot's intelligence

and emotion that isn't there because they see physical gestures that are similar to things

they might even see in people or animals. I don't know much about how these large language

models really work. I believe it's a statistical averaging of the most common responses to a

series of words. It's sort of a very elaborate word completion. And I'm dubious that that has

anything to do with consciousness. And I even wonder if that model of

sort of simulating consciousness by stringing words together that are statistically associated

with one another, whether or not that kind of knowledge, if you want to call that knowledge,

would be the kind of knowledge that allowed a sentient being to grow or evolve. It feels to

me like there's something about truth or emotions that's just a very different kind of knowledge

that is absolute. The interesting thing about truth is it's absolute, and it doesn't matter

how frequently it's represented in the World Wide Web. If you know it to be true, it may only

be there once, but by God it's true. And I think emotions are a little bit like that too. You know

something, you know, and I just think that's a different kind of knowledge than the way these

large language models derive sort of simulated intelligence. It does seem that the things that

are true very well might be statistically well represented on the internet because the internet

is made up of humans. So I tend to suspect that large language models are going to be able to

simulate consciousness very effectively. And I actually believe that current GPT-4,

when fine-tuned correctly, they'll be able to do just that. And that's going to be a lot of very

complicated ethical questions that have to be dealt with. They have nothing to do with robotics

and everything to do with... There needs to be some process of labeling, I think, what is true

because there is also disinformation available on the web. And these models are going to consider

that kind of information as well. And again, you can't average something that's true and

something that's untrue and get something that's moderately true. It's either right or it's wrong.

And so how is that process... And this is obviously something that the purveyors of these

barred and chat GPT that... I'm sure this is what they're working on.

Well, if you interact on some controversial topics with these models, they're actually

refreshingly nuanced. They present... Because when you realize there's no one truth,

what caused the war in Ukraine. Any geopolitical conflict. You can ask any kind of question,

especially the ones that are politically tense, divisive and so on. GPT is very good at presenting.

Here's the... It presents the different hypotheses. It presents calmly the amount of evidence for

each one. It's very... It's really refreshing. It makes you realize that truth is nuanced. And

it does that well. And I think with consciousness, it would very accurately say, well, it sure as

health feels like I'm one of you humans, but where's my body? I don't understand... You're

going to be confused. The cool thing about GPT is it seems to be easily confused in the way we are.

Like, you wake up in a new room and you ask, where am I? It seems to be able to

do that extremely well. It'll tell you one thing, like a fact about when a war started.

And when you correct it, say, well, this is not consistent. It'll be confused. It'll be, yeah,

you're right. It'll have that same element, childlike element with humility of trying to figure

out its way in the world. And I think that's a really tricky area to sort of figure out with us

humans of what we want to allow AI systems to say to us. Because then if there's elements of

sentience that are being on display, you can then start to manipulate human emotion, all that kind

of stuff. But I think that's something that's a really serious and aggressive discussion that

needs to be had on the software side. I think, again, embodiment robotics are actually saving us

from the arbitrary scaling of software systems versus creating more problems. But that said,

I really believe in that connection between human and robot. There's magic there. And I think

there's also, I think a lot of money to be made there. And Boston Dynamics is leading the world in

the most elegant movement done by robots. So I can't wait to what maybe other people that built

on top of Boston Dynamics robots or Boston Dynamics by itself. So you had one wild career,

one place and one set of problems, but incredibly successful. Can you give advice to young folks

today in high school, maybe in college, looking out into this future, where so much robotics and AI

seems to be defining the trajectory of human civilization? Can you give them advice on

how to have a career they can be proud of, or how to have a life they can be proud of?

Well, I would say follow your heart and your interest. Again, this was an organizing principle,

I think, behind the leg lab at MIT that turned into a value at Boston Dynamics, which was

follow your curiosity, love what you're doing. You'll have a lot more fun and you'll be a lot

better at it as a result. I think it's hard to plan. Don't get too hung up on planning too far

ahead. Find things that you like doing and then see where it takes you. You can always change

direction. You will find things that, that wasn't a good move. I'm going to back up and go do something

else. So when people are trying to plan a career, I always feel like, yeah, there's a few happy

mistakes that happen along the way and just live with that. But make choices then. So avail

yourselves to these interesting opportunities, like when I happen to run into Mark down in the lab,

the basement of the AI lab, but be willing to make a decision and then pivot if you see

something exciting to go at. Because if you're out and about enough, you'll find things like that

that get you excited. So there was a feeling when you first met Mark and saw the robots that

there's something interesting. Oh boy, I got to go do this. There was no doubt.

What do you think in a hundred years? What do you think Boston Dynamics is doing? What do you

think is the role even bigger? What do you think is the role of robots in society? Do you think

we'll be seeing billions of robots everywhere? Do you think about that long-term vision?

Well, I do think that I think the robots will be ubiquitous and they will be out amongst us.

And they'll be certainly doing some of the hard labor that we do today.

I don't think people don't want to work. People want to work. People need to work

to, I think, feel productive. We don't want to offload all of the work to the robots,

because I'm not sure if people would know what to do with themselves. And I think just

self-satisfaction and feeling productive is such an ingrained part of being human that we need to

keep doing this work. So we're definitely going to have to work in a complementary fashion. And

I hope that the robots and the computers don't end up being able to do all the creative work,

right? Because that's the part that's the rewarding. The creative part of solving a problem

is the thing that gives you that serotonin rush that you never forget, or that adrenaline rush

that you never forget. And so people need to be able to do that creative work and just feel

productive. And sometimes that you can feel productive over fairly simple work, but it's

just well done, and that you can see the result of. So there was a cartoon, was it Wally, where they

had this big ship and all the people were just overweight, lying on their beach chairs, sliding

around on the deck of the movie, because they didn't do anything anymore. Well, we definitely

don't want to be there. We need to work in some complementary fashion where we keep all of our

faculties and our physical health, and we're doing some labor, right, but in a complementary

fashion somehow. And I think a lot of that has to do with the interaction, the collaboration with

robots and with the AI systems. I'm hoping there's a lot of interesting possibilities there.

I think that could be really cool, right? If you can work in interaction and really be helpful

robots, you can ask a robot to do a job you wouldn't ask a person to do, and that would be a real

asset. You wouldn't feel guilty about it. You'd say, just do it. It's a machine, and I don't have

to have qualms about that. The ones that are machines, I also hope to see a future, and it is

hope. I do have optimism about that future where some of the robots are pets, have an emotional

connection to us humans. And because one of the problems that humans have to solve is this kind

of a general loneliness. The more love you have in your life, the more friends you have in your life,

I think that makes a more enriching life helps you grow. And I don't fundamentally see why some

of those friends can't be robots. There's an interesting long running study. Maybe it's in

Harvard. They just nice report article written about it recently. They've been studying this group

of a few thousand people now for 70 or 80 years. And the conclusion is that companionship and

friendship are the things that make for a better and happier life. And so I agree with you. And

I think that could happen with a machine that is probably simulating intelligence. I'm not

convinced there will ever be true intelligence in these machines, sentience, but they could simulate

it and they could collect your history. And I guess it remains to be seen whether they can

establish that real deep. When you sit with a friend and they remember something about you and

bring that up and you feel that connection, it remains to be seen if a machine is going to

be able to do that for you. Well, I have to say it's inklings of that already started happening

for me, some of my best friends and robots. And I have you to thank for leading the way in the

accessibility and the ease of use of such robots and the elegance of their movement.

Robert, you're an incredible person. Boston Dynamics is an incredible company. I've just

been a fan for many, many years for everything you stand for, for everything you do in the world.

If you're interested in great engineering robotics, go join them, build cool stuff. I'll

forever celebrate the work you're doing. And it's just a big honor that you sit with me today and

talk means a lot. So thank you so much. You're doing great work. Thank you, Lex. I'm honored to

be here and I appreciate it. It was fun. Thanks for listening to this conversation with Robert

Plater. To support this podcast, please check out our sponsors in the description. And now

let me leave you with some words from Alan Turing in 1950, defining what is now termed the Turing

Test. A computer would deserve to be called intelligent if it could deceive a human into

believing that it was human. Thank you for listening and hope to see you next time.

Machine-generated transcript that may contain inaccuracies.