Robert Playter: Boston Dynamics CEO on Humanoid and Legged Robotics | Lex Fridman Podcast #374
- And so our goal was
a natural-looking gait. 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 gait where, you know, 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 where, when you were walking, that it fully extended its leg 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 humanoids
until maybe last year. And the team was developing
on our newer generation of Atlas, you know, 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, you know, the PETMAN prototype was probably in 2008, and what was it, 2022, (laughs) last year that I think I saw good walking on Atlas. (dramatic music) - The following is a
conversation with Robert Playter, CEO of Boston Dynamics, a
legendary robotics company that, over 30 years, has created
some of the most elegant, dextrous, 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. This is the Lex Fridman podcast. To support it, please
check out our sponsors in the description. And now, dear friends,
here's Robert Playter. When did you first fall
in love with robotics? (Lex laughs) 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 department said, "Go see this guy Marc Raibert 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. (Lex laughs) And I just immediately
went, "Whoa," you know. - [Lex] Yeah. - "Robots can do that?" And because of my own
interest in gymnastics, there was, like, this
immediate connection, and, you know, I was
in an aeroastro degree because, you know, flight and movement was all so fascinating to me. And then it turned out that, you know, robotics
had this big challenge. How do you balance? How do you build a legged robot
that can really get around? That was a fascination,
and it still exists today. We'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? Was there something you just
fundamentally appreciated 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 kinda push you in the right direction in a
very natural and organic way. And the thing that Marc
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 Marc 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 in the computer or not even all in your
mind as a person (laughs). And there's something
interesting in that melding. - You were with Marc for
many, many, many years, but you were there in
this kinda legendary space of Leg Lab and MIT in
the basement (laughs). All great things happen in the basement. (Robert laughs) Is there some memories from
that time that you have? Because 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 Marc 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, with people like Russ Tedrake 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. (laughs) I meet a lot of PhD students. A lot of them are kind of broken (laughing) by the wear
and tear of the process, but roboticists are, while
they work extremely hard and work long hours, there's a happiness there. The only other group of people
I've met like that are people that skydive a lot. (both laughing) 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 'cause
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. - You know, 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 a
natural 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. 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 linger on Marc for a little bit longer, Marc Raibert. So, he's a legend. He's a legendary engineer and roboticist. What have you learned about
life, about robotics from Marc through all the many years
you've worked with him? - I think the most important
lesson, which was, you know, 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. And at the time, you
know, legged locomotion, especially in a dynamic
machine, nobody had solved it. And that felt like a
multi-decade problem to go after. And so, you know, have the
courage to go after that because you're interested. Don't worry if it's gonna make money. You know, that's been a theme. That's really probably the
most important lesson I think that I got from Marc. - How crazy is the effort
of doing legged robotics at that time, especially? - You know, Marc got some
stuff to work starting from simple ideas. So, maybe the other,
another important idea that has really become a
value of the company is try to simplify a thing
to the core essence. While, you know, Marc was
showing videos of animals running across the Savannah 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, how to build, you know, with the computers at the time, how to build a feedback-control algorithm that was simple enough that
it could run in real time at 1,000 hertz and actually
get that machine to work. And that was not something
everybody was doing, you know, 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 sort of
work at that principled level with both the software and
make the hardware work. And I'll say one other thing
about you were sort of talking about what are the special things. The other thing was it's good
to break stuff, you know. You know, use the robots,
break them, repair them, you know, fix and repeat,
(laughs) 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 kid 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 linger on
that, psychologically, how do you deal with that? 'Cause I remember I built a RC car. It had some custom stuff
like a computer on it and 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, it 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 these kind of experiments in the future. Perhaps the right way to
have seen that is positively. Like, it's too- - It depends if you
could have built that car or just gotten another one, right? That would've been the approach. I remember when I got to grad school, you know, 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 'cause
maybe this was a unique part, and I couldn't go buy it, and I realized, "Oh, I can just go make it." That was an enabling feeling. - [Lex] Yeah. - Then, you're not afraid. It might take time. It might take more work than you thought it was gonna be required
to get this thing done, but you can just go make it. And that's freeing in a
way that nothing else is. - 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, you know, scientific principles
that let you extrapolate from, like, one legged machine to another, you know, 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 were definitely a
core part of our work. There's also, you know, when we started to pursue humanoid robots,
there was so much complexity in that machine that, you
know, 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. (laughs) - [Lex] Yeah.
- And humanoids 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. (laughing) You know?
- Yeah. - And being a competitive athlete, there's something about seeing a movement. You know, a coach, one
of the greatest strengths a coach has is being
able to see, you know, some little change in
what the athlete is doing and then being able to
articulate that to the athlete, you know, and then maybe
even trying to say, "And you should try to feel this." So, there's something just in seeing, and again, you know, sometimes
it's hard to articulate what it is you're seeing, but
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, (laughs) it looks efficient and easy,
and just it looks right. It looks beautiful.
- It looks right is sort of the key, yeah? - 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 that, 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,
you know, walking gaits, or we build a machine around this gait where you're trying to work
with the dynamics of the machine instead of to stop them. You know, some of the early
walking machines, you know, you're essentially,
you're really trying hard to not let them fall over, and so you're always stopping
the tipping motion, you know. And sort of the insight
of dynamic stability in a legged machine is to go
with it, you know, (laughs) let the tipping happen. You know, 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. You know, 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
it'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, (laughing) how hard
is it to get that right? - Our first humanoid, we needed to deliver natural-looking walking, you know. 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 gait. 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 gait where, you know, 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 where, 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 humanoids
until maybe last year. And the team was developing
on our newer generation of Atlas, you know, 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, you know, the PETMAN prototype was probably
in 2008, and what was it, 2022, (laughs) last year that I think I saw good walking on Atlas. - If you could just, like, linger on it, what are some challenges
of getting good walking? So, is this partially, like, a hardware, like, 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 or all this kind of stuff?
- Yeah, so one of the things that makes,
like, this straight leg a challenge is you're sort
of up against a singularity, a mathematical singularity
where, you know, when when your leg is fully extended, it can't go further the
other direction, right? 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, (laughs) having
all of the mathematics so it can deal with these
singular configurations is one of many (laughs) challenges that we face. And I'd say, you know, 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. - Yeah.
- Right? And so, that is one of the hard problems of legged locomotion. - And you have to do that
for natural movement? - It's not necessarily
required for natural movement. It's just required, you know,
we don't have, you know, 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 know, you can't just, you know, God's hand
can't reach down and push in any direction you want,
(laughs) you know, so. - Is there some extra
challenge to the fact that Atlas 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, you know, 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. (Lex laughs) 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. - [Robert] Yeah, yeah. - Which is (laughs) really interesting. So, you have to, what, model the shape, the weight distribution. I don't know, like, you
have to, like, include that as part of the modeling,
as part of the planning 'cause okay, so for people
who don't know, so Atlas, at least in, like, a recent
video, like, throws a heavy bag, throws a (laughing) bunch of- - [Robert] Yeah. - 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 also picked up like a barbell and to be able to throw 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 gonna be useful, it's actually gotta 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, not heavy stuff anyway. But just like you or me, you know, you need to anticipate that moment. You know, you're reaching
out to pick something up, and as soon as you pick it up, your center of mass is gonna shift. And if you're gonna, you
know, turn in a circle, you have to take that
inertia into account. And if you're gonna
throw a thing, you know, all of that has to be sort of 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
sort of predict, you know, think a couple of seconds ahead, "How do I manage now my body
plus this big heavy thing together (laughs) and still
maintain balance, right?" And so, 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 that it took
six months to do anything new, you know, 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 gonna
be a quicker process. - So, being able to
explicitly model new things that it might need to pick
up, new types of things? - And you know, to some degree, you don't wanna have to
pay too much attention to each specific thing, right? 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. - [Lex] Yeah. - And so, you know, in some
ways you wanna pay attention to that detailed shape, and in others, you wanna generalize it and say, "Well, all I really care about is the center of mass of this thing, especially if I'm gonna throw
it up on that scaffolding." - And it's easier if the body is rigid. What if there's some, doesn't it throw, like, a sandbag type thing? - That tool bag, you know-
- Tool bag. - had loose stuff in
it, so it managed that. There are harder things
that we haven't done yet. You know, 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 'bout that? (laughing) - Yeah, we haven't done that yet. - [Lex] Carry Spot. - I guess we did a little bit of a, we did a little skit around Christmas where we had two Spots
holding up another Spot that was trying to put,
you know, a bow on a tree. So, I guess we're doing that
in a small way. (laughing) - Okay, that's pretty good. Let me ask the all-important question. Do you know how much Atlas can curl? (Robert laughing drown out Lex speaking) (Lex laughs) I mean, you know, for us
humans, that's really one of the most fundamental questions you can ask another human being, curl, bench, et cetera.
(Robert laughs) - It probably can't curl
as much as we can yet, but a metric that I
think is interesting is, you know, another way of
looking at that strength is, you know, the box jump. So, how high of a box can you jump onto? - [Lex] Question. - And Atlas, I don't
know the exact height. It was probably a meter
high or something like that. It was a pretty 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, you know, to get-
- Oh, the human? - The human getting all
the way on top of this box. But then, you know,
Atlas was able to do it. We're now thinking about the
next generation of Atlas, and we're probably gonna be in the realm of a person can't do it, you
know, with the next generation. The robots, the actuators
are gonna 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 you know,
that's all, you know, we don't always release those videos, but they're a lot of
fun to look at. (laughs) - So, we'll talk a little bit about that. But can you talk to the jumping? 'Cause 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? In fact, in these early days, 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 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 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 back (laughs). - Yeah, well-
- I mean. (laughs) - But that's the cool part, right? So, you know, the physics, we can calculate physics
pretty well using, you know, Newton's laws about how it's
going to evolve over time and you know, 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 gonna affect the outcome? And so, it can do these
calculations, many of them, you know, and basically solve for where, you know, given where I am now, maybe I took off a little bit screwy from how I had planned, I can adjust. - [Lex] So, you're adjusting in the air for the landing.
- 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. When we do it, even a gymnastics trick, we try to set it up so it's
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. I mean, it just feels, from a robotics perspective, just surreal. - You talked about underactuated, right? - [Lex] Yes.
- So, when you're- - That's totally true.
- When you're in the air, there's some things you
can't change, right? You can't change the momentum
while it's in the air 'cause you can't apply an
external force, a torque, and so the momentum isn't gonna change. So, how do you work within the constraint of that fixed momentum to
still get from A to B (laughs) where you wanna be? - That's really (laughing) underactuated. (Robert laughs) You're in the air. I mean, you become a drone
for a brief moment in time. No, you're not even a (laughing)
drone 'cause you can't- - [Robert] Can't hover. - You can't hover. You can't.
- You're gonna impact soon. Be ready. (laughs)
- Yeah. Have you considered like
a hover type thing or no? No?
- No. - It's too much weight?
- No. (Lex laughing) - I mean, it's just
incredible and just even to have the guts to try a
backflip with such a large body. That's wild. (Robert laughs) But, like how- - We definitely broke a
few robots trying that. - [Lex] (laughing) Yeah. (Robert laughs) - But that's where the
build it, break it, fix it, you know, strategy comes in. You gotta be willing to break. And what ends up happening is 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, you know. (laughs) - 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 just-
- Yeah. And how to build the machine better. - Yeah.
- Yeah. - I mean, is there something
about just the guts to come up with an idea
of saying, "You know what? Let's try (laughing) to
make it to 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?" - [Lex] Sure. - Because a lot of people
have asked that, you know. (Lex laughs) (laughing) "Why are you doing this?" - Why go to the moon (Robert laughs) in this decade and do
the other things, JFK? (Robert laughs) Not because it's easy, because it's hard. - [Robert] Yeah, exactly. (laughs) (Lex laughs) - Don't ask questions. Okay, so the jumping, I mean, 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 aren't 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, you know, manipulation, walking- - Driving a vehicle.
- driving a car, all these kinds of challenges
with, if I remember correctly, sort of some slight capability
to communicate with humans, but the communication was very poor. So, basically it has to be
almost entirely autonomous. - It could have periods where the communication
was entirely interrupted, and the robot had to be able to proceed. - [Lex] Yeah. - But you could provide
some high-level guidance to the robot, basically
low-bandwidth communications- - Yeah
- to steer it. - I watched that challenge
with kind of tears in my eyes eating popcorn with-
- Us, too. (both laughing) - But I wasn't personally
losing, you know, hundreds of thousands, 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.
(Robert laughs) So, anyway, just looking
back to that time, what have you learned
from that experience? And 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, 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 encouraged people
to build autonomous cars. So, they were trying to basically
push an industry forward. 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, you know, 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 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,
you know, 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. (laughs) You know, they fell down,
you know, repeatedly. It was hard to get through this contest. Some did, and you know,
they were rewarded and won. 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, you know. And the-
- The general nature of it, the variety of it. - [Robert] The variety. - And also, I don't know
if the tasks were (sighs) 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, like just for example, probably the hardest task is
not the driving of the car but getting in and out of the car. (Robert laughs) And Atlas probably is,
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- - Yeah.
- to get in and out of that car, right? - [Lex] 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 wanna
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 gonna 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 multipurpose robot. And that's just darn hard. And so, yeah, there was these
curious little failures. Like, I remember 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 (laughs)
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 was expecting a force
back from the door. It wasn't there, and
then I lost my balance." And so, these little simple things that you and I would
take totally for granted and deal with, (laughs)
the robots don't know how to deal with yet, and
so you have to start to deal with all of those circumstances. (laughs) - Well, I think a lot
of us experience this 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. - [Robert] Yeah, and then, "Whoa." - Oh, yeah, and then, and I'm
sure if your depth 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. - [Robert] Yeah. - But there's a-
- Which is why you need to be able to predict forward
just a little bit, and so that's where this model-predictive
control stuff comes in. Predict forward what you
think's gonna happen, and if that does happen,
you're in good shape. If something else happens, you better start predicting again. - So, like, regenerate a plan.
(Robert laughs) - [Robert] Yeah. - I mean, that also requires
a very fast feedback loop of updating what your prediction, how it matches to the actual real world. - [Robert] Yeah, those things
have to run pretty quickly. - What's the challenge of
running things pretty quickly, 1,000 hertz, of acting
and sensing quickly? - You know, there's a few
different layers of that. At the lowest level, you
like to run things typically at around 1,000 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 wanna do that really fast, something like 1,000 hertz,
and that means you can't have too much calculation
going on at that joint. But that's pretty manageable these days, and it's fairly common. And then, there's another layer that you're probably
calculating, you know, maybe at 100 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 in, 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,
you know, 10 years ago, we had to have pretty simple
models that we were running, you know, at those fast rates
'cause the computers weren't as capable about calculating forward with a sophisticated model,
but as computation gets better, we can do more of that. - What about the actual pipeline
of software engineering, how easy it is to keep updating Atlas, like, do continuous 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, several years ago. But you know, that was
a bit of a milestone. And so, you wanna certainly
work these pipelines so that you can make
it as easy as possible and have a bunch of people
working in parallel. 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 the software pipeline. - Well, that's a really
exciting step to be able to run the exact same code in simulation
as on the actual robot. How hard is it to do a 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.
(Robert laughs) How hard is it to sort of keep
working 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. And 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 'cause if your simulation
runs too slow, you know, then your developers are
sitting around waiting for stuff to run and compile, and so it's always about efficient, fast operation as well. So, that's been a big part of it. You know, 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, period, so 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 'cause there's a lot
more going on. (laughs) - Yeah.
- You know. And you need to capture, I don't know, things slipping and moving,
you know, in your hand. It's a level of complexity
that I think goes above foot-ground contact when you really start doing
dextrous 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 (Robert laughs) and us both drinking a beer? (Robert laughing) - [Robert] Well, I- - Sip it out of a can, out of a can. - Maybe Atlas could spill his beer 'cause he's got nowhere
to put it. (laughing) Atlas could walk on the sand. - So, can it?
- Yeah, yeah. Yeah, I mean, you know,
have we really had him out on the beach? You know, we take them outside often, you know, rocks, hills,
that sort of thing, even just around our lab in Waltham. We probably haven't been
on the sand, but I'm- - So, soft surfaces, normally?
- 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. We had to take BigDog
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 while playing-
- Oh, that's- - [Robert] some cool beach music. - Yeah.
- Great show, but then, you know, we didn't
really clean the robot off, and the saltwater was really
hard on it, so you know, we put it in a box, shipped it back. By the time it came back, we had some problems
(laughing) with corrosion. - It's the salt water. It's not like-
- Salt tough (laughs). - It's not, like, sand getting into the components or
something like this. - [Robert] Yeah, yeah.
- But I'm sure, if this is a big priority,
you can make it like- - Right.
- waterproof it or something.
- Right, right. That just wasn't our goal at the time. - Well, it's a personal goal of mine to walk along,
(Robert laughs) walk along the beach, but
it's a human problem, too. You get sand everywhere. It's just a giant mess. (Robert laughs) So, soft surfaces are okay. So, I mean, can we just linger
on the robotics challenge? There's a pile of, like,
rubble they had to walk over. How difficult is that task? - In the early days of developing BigDog, the loose rock was the epitome
of the hard walking surface because you stepped down, and you had these little
point feet on the robot, and the rock can roll,
and you have to deal with that last-minute, you know, change in your foot placement. - Yeah, 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, that became kinda
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 'em 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 BigDog. Can we maybe take a stroll through the history of Boston Dynamics? So, what and who is BigDog? By the way, is who, (Robert laughs) do you try not to
anthropomorphize the robots? Do you try to remember that they're, this is like the division I have 'cause, 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 BigDog? What is BigDog?
(Robert laughs) - Well, I'll say to
address the meta question, we don't try to draw hard
lines around it being an it, or a him, or a her. It's okay, right? 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 'em, 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 we're living beings,
which are intelligent, and so they wanna 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 that it's there. - And have a little fun with it. - And have little fun.
- 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 'cause the guy up on the scaffolding says, "Send me some tools." Atlas has to kinda 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. (both laughing) I hope to have a lot of
Spots in my possession. I'm interested in celebrating robotics and celebrating companies, companies that do incredible stuff
like Boston Dynamics. You know, I'm a little crazy,
and you say you don't want to, you want to align, you
wanna help the company 'cause I ultimately want a company like Boston Dynamics to succeed. And part of that we'll
talk about, you know, success kind of requires making money. And so, the kinda 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 kinda stuff I've been
playing with is a robust way of having the quadrupeds, the
robot dogs communicate emotion with their body movement- - Hmm.
- the same kinda stuff you do with a dog- - Yeah.
- but not hard coded, but in a robust way-
- Mm-hmm. - and be able to communicate
excitement, or fear- - Mm-hmm. - boredom, all these 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 gonna be really important, and it's
a thing we're beginning to pay attention to. 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 legged 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, you know, taking measurements. We need for people who
are operating those robots to understand what the robots are doing. If the robot needs help
or, you know, is in trouble or something, it needs
to be able to communicate and a physical indication of some sort so that a person looks
at the robot and goes, "Oh, I know what that robot's doing. That robot's going to go take measurements of my vacuum pump with
its thermal camera." You know, you wanna be
able to indicate that, or even just the robot's
about to turn, you know, in front of you and
maybe indicate (laughs) 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, you know, but now the robots are
really out in the world, and you know, we have about 1,000 of 'em out with customers right now. This layer of physical indication,
I think, is gonna become more and more important. - We'll talk about where it goes 'cause there's a lot of
interesting possibilities. But if you can return back to
the origins of Boston Dynamics with the more research, the R&D side, before we talk about how
to build robots at scale. So, BigDog. What's-
- So- - Who's BigDog? - So, the company started in 1992, and in probably 2003, I believe, is when we
took a contract from, so, basically, 10 years, 11
years we weren't doing robotics. We did a little bit of robotics with Sony. They had Aibo. They had their Aibo robot. We were developing some software for that. That kinda got us a little bit involved with robotics again. Then, there was 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 BigDog, and it was a quadruped,
and 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 onboard power plan. It had onboard 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, right? 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. (Lex laughs) (Robert laughs) - I mean, can you just
comment on the roughness of that transition? 'Cause BigDog, I mean, this is this big
quadruped, four legs robot. - We built a few different
versions of them, but the very earliest ones, you
know, didn't work very well, (laughs) and we would take 'em
out, and it was hard to get, you know, a go-kart engine
driving a hydraulic- - Oh, is that what it was? (Robert laughs) I was-
- And you know, having that all work while trying to get, you know, the robot to stabilize itself, and so-
- So. what was the power plan? What was the engine? It seemed like my vague
recollection, (laughs) I don't know. It felt very loud, and aggressive, and kind of thrown together
is what it kind of- - 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 BigDog had literally go-kart engines or something like that. Usually, it-
- It was gas powered? - Yeah, a gas-powered two-stroke engine. (Lex laughs) And the reason why it was two stroke is two-stroke engines are lighter weight, and we generally didn't
put mufflers on them 'cause we're trying to save the weight, and we didn't care about the noise. (laughing) And some of 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-
- what I've seen the videos of.
- Yeah. I mean, the early
versions, you know, stood about, I don't know,
belly high, chest high. You know, they probably
weighed maybe a couple of hundred pounds, but
you know, 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 'cause you know, going into
it, we didn't know that. We had built quadrupeds at MIT, but they used a giant hydraulic
pump, you know, 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 ourself that the legged locomotion
could really work. And so, BigDog 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
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. - So, push the limits in
all kinds of directions in terms-
- 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 sort of a different thread where we started building humanoids. We saw that probably an end was coming where the government was
gonna 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, "What's your product gonna 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. - [Robert] Yes. - So, can you maybe
comment on what it's like at Google working with Larry Page, having those meetings, and thinking of what will a 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 gonna 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. And of course, we were
only building, like, 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 gonna 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 that 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 gonna 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
get in there needs to go through these really nice machines so that we can then learn how to simplify. - So, what can you say to
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?
(Robert laughs) - 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, 'cause you've got these
engineers who love hard problems, who wanna figure out
how to make robots work. And you don't know if you
have engineers that wanna 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
wanna 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, you know, 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, (laughs) 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 of time. - (laughing) Exactly. - So, like, most robots
are just sitting there in a sad state of despair waiting to be born,
(Robert laughs) 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 a sense of sentience to it because it doesn't seem like anybody was
(laughing) supervising it. - Well-
- It was just doing its thing.
- I'm gonna stop way short of the sentience. - Sure.
- It is the case that, if you come to our
office, you know, today and walk around the hallways, you're gonna see a dozen robots
just kind of walking around- - Yes.
- 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 'cause that's what somebody
who's running a brewery, a factory, that's what
they need the robot to do, and 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
I don't think anybody else in the world can do 'cause,
A, you have to have a fleet of robots to just accrue that
much information. (laughing) You have to be willing to
dedicate it to that test. But that's essential. - [Lex] That's how you
get the reliability. - That's how you get it. - What about some of the cost cutting from the manufacturer's side? What have you learned from
the manufacturer's side of the transition from R&D to- - And we're still learning a lot there. We're learning how to cast parts instead of mill it all out
of, you know, billet aluminum. We're learning how to
get plastic molded parts, and we're learning about
how to control that process (laughs) 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 Waltham. It's about a mile from our headquarters, and we're doing final assembly and tests of both Spots and Stretches,
you know, at that factory. 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 wanna
move as fast as we do. And that's been challenging. 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 'em to make 'em cheap, and then we go change the
design from underneath 'em, and they're like, "What are you doing?" And so, you know, getting
everybody on the same page here that, yep, we still need to move fast, but we also need to try to
figure out how to reduce cost, that's one of the challenges 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 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 Spot's gonna 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. But for example, in the
applications that we're excited about where 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 we really knew on day one
that we're gonna be operating in a factory with these
three sensors in it, we would have it all
integrated in a package that would be easier, less
expensive, and more reliable. So, we're contemplating
building, you know, a next generation of that machine. - So, we should mention that, so Spot for people who
somehow are not familiar, is 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 manufacturer of it? - We think the future of mobile robots is mobile manipulation. You know, in the past 10 years, it was getting mobility to work, getting the legged 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. It 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 stay balanced, 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 the grasp. But it's a whole nother complex robot on top of a complex legged robot. And of course, we made the
hand look a little like a head, (laughs) 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? - [Robert] Oh, yeah, oh, yeah. It's an option. - What's the interface
like to work with the arm? I could 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 gonna offer the ability, if you have a autonomous
mission for the robot, we're gonna include the
option that it can go through a door, which means
it's gonna have to have an arm, and it's gonna have to use
that arm to open the door. And so, that'll be an
autonomous manipulation task that you can program
easily with the robot- - Oh.
- 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."
- Oh, wow. So, it just takes care of everything? - Yeah. So, 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 when they're gonna
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 (laughing) involved in this breaker switch. And it's a dangerous event
'cause 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 and the arm through the interface to
operate this disconnect- - That's great.
- 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 the software now we're gonna deliver back
to Ontario Power Generation, they're gonna be able
to just point the robot at that breaker. 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 gonna put your tool, and you're gonna 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 and the arm.
- 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? 'Cause I personally want a
robot that gets me a beer. (Robert laughs) I mean, that has to be,
like, one of the most, 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 'cause they teleoperated
it the first time. Probably took 'em 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 gonna 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, our bigger customers, typically ones who are gonna run a utility, or a factory, or something like that, we
take that kind of direction from them, especially
if they're gonna 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 wanna be working on. - Mm-hmm.
- Yeah, and so- - Note to self, "Buy 10 Spots,
(Robert laughs) and aggressively push
for beer manipulation." (Robert laughs) 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 in
the industrial setting, want to purchase them, and it's
difficult to build robots that are useful, sufficiently useful. - [Robert] Yeah.
- 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, you know, the money we made was from
doing contract R&D work, and we made money, but you
know, we weren't growing, and we weren't selling a product. And then, we went through several owners who, you know, had a vision
of not only developing advanced technology, but
eventually developing products. And so, both, you know,
Google, and SoftBank, and now Hyundai, you know, had
that vision and were willing to, you know, 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, you know, A, you need to field the robots. They need to start to become
reliable, and as we've 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, you know, if a chip fab, if the line goes down because a vacuum pump failed someplace, that can be 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 InBev. It takes place in chip fabs. You know, we're working
with GlobalFoundries. 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 about, actually it's 1,100 Spots out now. To really get, you know,
profitability, we need to be at 1,000 a year, maybe
1,500 a year, you know, 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 wanna mention, you know,
our second robot, Stretch. - Yeah, tell me about Stretch. What's Stretch? Who is Stretch? - Stretch started differently than Spot. You know, 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 was 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, (laughs) 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 gonna be used for, they immediately started saying, "Oh, I'll buy that robot. You know, in fact, I'm
gonna put in an order for 20 right now." We just started shipping
the robot in January after, you know, several
years of development. - [Lex] Of this year? - Of this year. So, our first deliveries of
Stretch to customers were DHL and Maersk in January. We're delivering to 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, you know, a couple
hundred of these robots. This one's gonna go, right? It's so obvious that there's a need, and we're not just gonna unload trucks. We're gonna do any box-moving
task in the warehouse. And so, it too will be
a multipurpose 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 gonna 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 wanted it to be
the size of a pallet because that's what lives
in warehouses, right, pallets of goods sitting everywhere, so it needed to be able
to fit in that space. - [Lex] It's not a legged robot. - It's not a legged robot. 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. (Lex laughs) - To do the much easier
problem but to do it well. - Well, because, you know, it wasn't gonna 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 Handle. And there's-
- That thing was epic. - Oh, it's a beautiful machine. - It's an incredible machine. (Robert laughs) (Lex laughs) I mean, it looks epic. It looks like, I mean, out of a sci-fi movie of some sorts. I mean, can you actually just linger on, like, the design of that thing? 'Cause that's another leap into something you probably haven't done. It's a different kind of balancing. - Yeah, so let me-
- It's wild. - I love talking about the
history of how Handle came about (Lex laughs) because it connects all
of our robots, actually. So, I'm gonna 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 know, 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, you know,
controlling the inertia, controlling the momentum of the robot. We were starting to
figure that out on Atlas. And so, our first concept of Handle, which was a robot that was
gonna be on two wheels, so it had to balance, but it
was gonna 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, you know, an
ostrich moving around, was the wheels, the leg. 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 it 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. - Oh.
- And it- - Do it beautifully with elegance.
- It did beautifully, but it just wasn't efficient enough. - [Lex] Aw. - So, we let it go. - [Lex] Yeah. - 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. - [Robert] Yeah. - 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, right? So, it can move a 50-pound
box around with its arm and not tip over it. It's omnidirectional, it can move in any direction,
and it has a nice suspension built into it so it can
deal with, you know, 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, palletize, depalletize. We're starting with unloading trucks because there's so many
trucks and containers where goods are shipped,
and it's a brutal job. You know, in the summer,
it can be 120 degrees inside that container. People don't wanna do that job, and it's backbreaking labor, right? 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 Maersk, 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- - [Robert] Yeah. - And I suppose you
have to do the same kind of thinking with Spot. So-
- Yes. - it seems like with
Stretch the application is, like, directly obvious. - [Robert] 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. - [Lex] Yeah. - 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 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, you know, likely through these
industrial applications and then mass manufacturing, you know. 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? A principle of the company has always been really make the robots do useful stuff. And so, you know, 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
gonna go through the design and software development in industrial, and then that's eventually
gonna let you reach a scale that could then be
addressed to a commercial, 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. - Mm-hmm. - But there's things we
need to figure out still, how to safely, really safely, and if you're gonna be
interacting with children, you better be safe. (laughs) 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, you know, 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 trade off in safety because it feels like, in
the home, you can fall. Like, you don't have to be as good. Like, 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 'cause 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. - [Lex] Yeah. - And they're gonna want to get the beer and know the difference between
the beer and a Coca-Cola or my snack. You know, they're all gonna
want you to clean up the dishes, you know, from the table
without breaking 'em. (laughs) 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 that'll be very interesting. I think the application
of being a pet, a friend, so, like, no tasks. Just be cute, 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 'cause I'm hanging out alone with robot dogs a little too much, but, like, there's a connection there. And it feels like that connection
should not be disregarded. You-
- No. It should not be disregarded. Robots that can somehow communicate through their physical gestures you're gonna be more
attached to in the long run. Do you remember Aibo-
- Mm-hmm. - the Sony Aibo? - Yep.
- They sold over 100,000 of those, maybe 150,000, you know, what 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,
you know, 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, you know, ChatGPT 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 gonna go down because, again, we're so focused
on performance and utility. We can add those other things also, but we really wanna start from that foundation of utility, I think. - Yeah. But I also wanna predict
that you're wrong on that, 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 ChatGPT-like capability, maybe GPT 5. And there's just so many
open-source alternatives that you could just plop
down 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, you know, reliable, all those kinds of things,
it'll be the natural transition to where just adding ChatGPT on top of it could-
- Oh, I do think that being able to verbally converse or even converse through gestures, you know, part of these learning models is that, you know, you can now
look at video and imagery and associate, you know, intent with that. Those will all help in the communication between robots and people, for sure. And that's gonna happen
obviously more quickly than any of us were expecting. (laughs) - I mean, what else do you want from life? A friend to get you a beer
(Robert laughs) and then just talk shit
about the state of the world. (Robert laughs) I mean, there's a deep
loneliness within all of us. And I think a beer and a good
chat solves so much of it or takes us a long way to solving a lot of it.
- It'll be interesting to see, you know, when a generative AI can give you that warm feeling that
you connected, you know, and that, "Oh, yeah, you remember me. You're my friend. You know, we have a history." You know, that history matters, right? - [Lex] Memory of joint, like- - Memory of, yeah. (laughs) - 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- - Mm-hmm.
- and having a shared memory of an amazing time or a difficult time and kind of that memory
creating this, like, 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-
- Right. - 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 applications, with real customers, and
with robots that are safe, and work, and reliable,
and manufactured at scale. - And I think we're in a
unique position in that because of, you know, our
investors, primarily Hyundai, but also SoftBank still owns 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, you know, what does
mobility mean in the future? How is this mobile robot
technology going to influence us, and can we shape that? And they want both. And so, we 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 wanna drive my own ship,
we need to have a business that's profitable in the end. Otherwise, somebody else is
gonna drive the ship for us. So, that's really important. But we're gonna retain the
aspiration that we're gonna build the next generation of
technology at the same time. And the real trick 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, Elon Musk and Tesla
have announced they've been in the early days of
building a humanoid robot. How does that change the
landscape of your work? So, from an outside
perspective, it seems like, well, as a fan of robotics,
it just seems exciting. - Right, very exciting, right? When Elon speaks, people listen. And so, it suddenly brought
a bright light onto the work that we'd been doing, you
know, for over a decade. And I think that's only gonna help. And in fact, what we've seen
is that, in addition to Tesla, we're seeing a proliferation
of robotic companies arise now. - Including humanoid? - [Robert] Yes. - Oh, wow.
- Yeah. And interestingly, many of them as they're, you know,
raising money, for example, will claim whether or not they have a former Boston Dynamics employee on their staff as a criteria. (both laughing) - Yeah, that's true. I would do that as a
company, yeah, for sure. - Yeah, so-
- Shows you're legit, yeah. - Yeah, so, you know,
(Lex laughs) it has brung a tremendous validation to what we're doing and excitement. Competitive juices are flowing,
you know, the whole thing. So, it's all good. - Elon has also kind of stated that, you know, maybe he implied that
the problem is solvable in the near term, which is
a low-cost humanoid robot 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 kinda pushing Atlas maybe
to do more cool stuff, trying to drive the cost
of Atlas down perhaps? I mean, I guess I wanna
ask if there's some kind of exciting energy in Boston Dynamics due to this little bit of competition. - Oh, yeah, definitely. When we released our most
recent video of Atlas, you know, I think you had seen it, the 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 gonna work in a manufacturing environment, that's what you gotta be able to do. And for the reasons I
explained to you earlier, it's not trivial to do so, you know, changing the center of mass, you know, by picking up a 50-pound block, you know, 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. You know, we see the
next phase of Atlas being more dextrous hands that can
manipulate and grab more things that we're gonna start by
moving big things around that are heavy and that affect balance. And why is that? Well, really tiny dextrous
things probably are gonna be hard for a while yet, you know. Maybe you could go build a
special-purpose robot arm, you know, for stuffing, you know, chips into electronics boards,
but we don't really wanna really fine work like that. I think more course work
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 gonna be able to do with the level of
dexterity that they're gonna have in the next few years, and
that's where we're headed. And you know, Elon has
seen the same thing, right? 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 know, 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
Dynamics 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, with 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 Atlas
with a 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 costs down- - Sure.
- very well. And the smart thing
that, you know, they have in their favor is, you know,
they know how to manufacture. They know how to build electric motors. They know how to build, you know, 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, automotive company behind us as well. (Lex laughs) - 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 and in the future of
Boston Dynamics's sort of higher-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 gonna be more broadly available,
maybe even commoditized, you know, 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. Like, I'm going to a new
environment, and I have a new kind of door handle or piece of
equipment I wanna 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. Levatas is a 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 ultimately an ecosystem of providers doing stuff like that. 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 you know, we're using
learning, reinforcement learning to develop algorithms for both
locomotion and manipulation. And ultimately, this is gonna mean you can add new behaviors to
a robot, you know, quickly. And that could potentially be done outside of Boston Dynamics. Right now, that's all internal to us. I think you need to understand
at a deep level, you know, the robot control to do that. But eventually, that could be outside. But it's certainly a place where these approaches
are gonna be brought to bear in robotics. - So, reinforcement learning
is part of the process. So, you do use reinforcement learning. - [Robert] Yes. (Lex sighs) So, there's increasing levels
of learning with these robots? - [Robert] Yes. - And that's for locomotion,
for manipulation, for perception? - [Robert] 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, you know, 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,
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 gonna
use them in our robots. If I'm using a robot, I'm
building a robot to do, you know, 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, and
whereas if you're asking for, yeah, I don't know, you're trying to ask a
theoretical question in ChatGPT, it could be true, or it may not be true. And it's hard to have that verifier, what is that truth (laughs)
that you're comparing against, whereas, in physical
reality, you know the truth. And this is an important difference. And so, I think there is reason
to be a little bit concerned about, you know, how these tools, large language models could be used. But I'm not very worried about
how they're gonna 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 with Spot. - [Robert] Yeah. - 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. - [Robert] Yeah. - So, you could communicate visually. You can communicate through
some interface and to be able to communicate verbally
again with a beer and so on. I think that's really exciting to make that much, much easier. - We have this partner
Levatas that's adding the vision algorithms
for gauge reading for us. 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 commands.
- Nice, I love it. - Yeah. - 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 Marc Raibert, the
founder of Boston Dynamics, and the former CEO, and
my old advisor at MIT. Marc has always loved the research, the pure research without the confinement or demands of commercialization. And he wanted to continue to, you know, 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 kinda continue pushing this forefront. And we expect to be working together where you know Boston Dynamics is, again, both commercialize and do research, but the sort of time horizon of the research we're
gonna 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, you know, the analogy
of legged locomotion again, when we started that, that
was a multi-decade problem. And so, I think Marc
wants to have the freedom to pursue really hard
over-the-horizon problems. That'll be the goal of the institute. - So, we mentioned some of the
dangers, 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 (Robert laughs) of legged robots? - Yeah, I wanted to show you this. So, this is in the Wall Street Journal, and this is all about ChatGPT, right? But look at the picture. - Yeah. - [Robert] It's a humanoid robot. - That's saying, "I will replace you." - That looks scary, and it says, "I'm gonna replace you." - [Lex] Yeah. - And so, the humanoid robot is the embodiment of this ChatGPT tool that there's reason to
be a little bit nervous about how it gets deployed. - [Lex] Yeah. - So, I'm nervous about that connection. It's unfortunate that
they chose to use a robot as that embodiment. 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 100 years. So, you know, the word robot was developed by a playwright named Karel Capek in 1921, a Czech playwright,
"Rossum's Universal Robots." And in that first depiction of a robot, the robots took over (laughs)
at the end of the story. And you know, people love to be afraid. And so, we've been
entertained by these stories for 100 years, 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 gonna pretend. It could pretend it's human all it wants. (Lex laughs) You could put ChatGPT on top of it, but you're gonna know it's not human because you have a contact
with physical reality. - And you're gonna know
whether or not it's doing what you asked it to do. - Yeah, like, it's not gonna, (laughs) I mean, I'm sure it can start,
just like a dog lies to you. It's like, "I wasn't part
of tearing up that couch." So, Spot can try
(Robert laughs) to lie that like, you know, "It wasn't me that's spilled that thing," but you're going to kind
of figure it out eventually if it happens multiple times, you know. But I think that- - Humanity has figured out
how to make machines safe. - [Lex] Yeah. - 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 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 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 took a ride. I was in San Francisco. I took a ride in a Waymo vehicle. It's an autonomous vehicle, and I've done it several times. They're doing incredible work over there, but (laughs) people flicked it off. - Oh, really?
- Flicked off the car. So, (laughs) I mean, that's a long story of what the psychology of that is. It could be maybe big tech,
or I don't know exactly what they're flicking off. - [Robert] Yeah. - 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 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- - Yes.
- that it's gonna have a broader impact than we expected. And there will be, you
know, jobs will change. Sometime in the future, we're
gonna look back at people who manually unloaded
these boxes from trailers, and we're gonna say, "Why did
we ever do that manually?" But there's a lotta people
who are doing that job today that could be impacted. But I think the reality
is, as I said before, we're gonna build the technology 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 gonna have strong impact on economic growth, and 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 sort of thinking back what they really wanna do with their life, but these jobs are getting
harder and harder to fill. And I'm hearing that over and over again. So, I think, frankly, this
is the right technology at the right time where we're
gonna need some of this work to be done, and we're gonna 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. (Robert laughs) (Lex laughs) The scary impact of, basically, so I'm, I guess, a software
person, so I program a lot. And the fact that people like
me could be easily replaced by GPT, that's going to have a- - Well, and lot, you know,
anyone who deals with texts and writing a draft proposal
might be easily done with ChatGPT now. - Yeah.
- where- - Consultants.
- it wasn't before. - [Lex] Journalists. - Yeah. - [Lex] Everybody is sweating. - 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. (Robert laughs) That's another conversation altogether, but yes, more right than the average, the mean journalist, yes. You spearheaded the 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 in China, and Agility here in the United States, and ANYmal in Europe, and, you know, some others
to cosign 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, you know, 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 gonna
have to trust the technology, to let it in. And if you think the
robot's gonna harm you, that's gonna 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, you
know, 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. In fact, I have a policy,
you know, director at my company whose job it
is to engage with the public, to engage with interested
parties, including regulators, to sort of begin these discussions. - Yeah, it's 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 it take to be a
CEO of a robotics company? So, you started as a
humble engineer, (laughs) a PhD. Just looking at your journey, what does it take to go
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 kinda 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, you know, it's
been a great career for me. I never would've 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 always needed to be some level of management that was needed. And so, you know, 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. You know, just willingness
to sort of tackle any piece of the business that needs
it and 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 gonna make a great member of a engineering team that's doing some of the hardest work in the world? - You know, 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, 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 they're 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. You know, at Google, they taught us about their interview process, and it was a little bit different. 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 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 gonna ask you a programming question, and I'm just gonna ask you to, you know, write code in front of me. That's a terrifying, you
know, application process. - [Lex] Yeah. - It does let you compare
candidates really well, but it doesn't necessarily
let you tap into who they are. - Yeah.
- (laughs) Right? 'Cause you're asking them
to answer your question instead of you asking them about
what they're interested in. 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 technical people 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- - Yeah, and talent.
- for the world. - You know, did they really do it? Did they find something
interesting or curious, you know, and do they care about it? (laughs) - I think somebody I admire is Jim Keller, and he likes details. So, one of the ways you could, (laughs) 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.
- What they- - 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. - They didn't do it.
(Robert laughs) 'Cause especially with engineering, the work is in the details. - Yeah. - I have to go there briefly (sighs) just to get your kind of thoughts on the long-term 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, 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, (Lex laughs)
and I don't spend- - Is anybody?
- a lot of time thinking about this, right? And I do think it's fairly
remote for the machines that we're dealing with. You're right that people anthropomorphize. They read into the robots'
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 kind
of statistical averaging of the most common responses, you know, to a series of words, right? 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 wanna 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 worldwide web. If you know it to be
true, it it can only be, it may only be there once,
but by God, that'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- - It does seem that-
- intelligence. - things that are true very well might be statistically well
represented on the Internet because the Internet's 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,
would be able to do just that. And there's going to be a lot of very complicated
ethical questions that have to be dealt with that have
nothing to do with robotics and everything to do with- - There needs to be some
process of labeling, I think, (laughs) 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. (laughs) It's either right, or it's wrong. And so, how is that process,
and this is obviously something that the purveyors of
these, Bard and ChatGPT, 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. Well, you realize there's
no one truth, you know. What caused the war in Ukraine, right? 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, it presents the different hypotheses. It presents calmly
(laughing) sort of the amount of evidence for each one. 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 hell feels
like I'm one of you humans, but where's my body? (Robert laughs) I don't understand." Like, 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, that's 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 on display, you can then start to manipulate human emotion,
all that kinda stuff. But I think that's a really serious and aggressive discussion
that needs to be had (laughing) 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. (Robert laughs) So, I can't wait-
- Well, thank you. - 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 on 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 'em 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, you
know, 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, you know. 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, you know, "Ah, that wasn't a good move. I'm gonna pack up and
go do something else." So, when people are
trying to plan a career, I always feel like, "Ah,
there's a few happy mistakes that happen along the way
and just live with that it." You know, but make choices then. So, avail yourselves to these
interesting opportunities, like when I happened to run
into Marc 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, you know, 'cause, 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 Marc and saw the robots that
there's something interesting. - "Oh, boy, I gotta go do this." There was no doubt.
(Lex laughs) (Robert laughs) - What do you think in 100 years, whoo, what do you think Boston
Dynamics is doing? 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 that robots will be ubiquitous, and they will be out amongst us, and they'll be certainly doing, you know, some of the hard labor that we do today. I don't think people don't wanna work. People wanna work. People need to work to,
I think, feel productive. We don't wanna offload all
of the work to the robots 'cause I'm not sure if people would know what to do with themselves.
(Lex laughs) 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 gonna have to work in a complimentary fashion,
and I hope that the robots and the computers don't end up being able to do all the creative work. Right?
- Yeah. - 'Cause that's the part that's, you know, that's the rewarding. The creative part of solving
a problem is the thing that gives you that serotonin
rush that you never forget, you know, (laughs) or that adrenaline rush that you never forget, and
so, you know, people need to be able to do that creative work and just feel productive, and sometimes you can feel productive over fairly simple work
that's just well done, you know, and that you
can see the result of. So, yeah, you know, I don't know, there was a cartoon, was it "Wall-E," where they had this big ship, and all the people were just overweight, lying on their beach chairs kinda sliding around on the deck of the movie because they didn't do anything anymore. - Yeah.
- Well, we definitely don't wanna
be there, (laughs) you know. We need to work in some
complimentary fashion where we keep all of our
faculties and our physical health, and we're doing some labor, right, but in a complimentary fashion somehow. - And I think a lotta that has
to do with the interaction, the collaboration with
robots and with 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 an interaction
and really be helpful, robots, you know, 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 know. (laughs) You'd say, "Just do it." - Yeah.
- It's a machine. And I don't have to have
qualms about that, you know. - The ones that are machines,
I also hope to see a future, and it is hope. I do have optimism about the 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 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, 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, you know,
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. You know, I guess it remains to be seen whether they can establish that real deep, you know, 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's gonna be
able to do that for you. - Well, I have to say, inklings of that already started happening for me. Some of my best friends are robots. (Robert laughs) 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 would sit with me today and talk. It means a lot, so thank you so much. Keep 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 Playter. To support this podcast, please check out our sponsors in the description. And now, let me leave you 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.