And so our goal was a natural looking gait.
It was surprisingly hard to get that to work.
And we, 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
and it would do heel strike first
before it rolled onto the toe
so you didn't land with a flat foot.
You extended your leg a little bit.
But even then it was hard to get the robot to walk
when you were walking that it fully extended its leg.
And 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 last year
that I think I saw a good walking on Atlas.
The following is a conversation with Robert Plater,
CEO of Boston Dynamics, a legendary robotics company
that over 30 years has created some of the most elegant,
dexterous and simply amazing robots ever built,
including the humanoid robot Atlas and the robot dog Spot.
One or both of whom you've probably seen on the internet,
either dancing, doing backflips, opening doors,
or throwing around heavy objects.
Robert has led both the development
of Boston Dynamics humanoid robots
and their physics based simulation software.
He has been with the company from the very beginning,
including its roots at MIT, where he received his PhD
in aeronautical engineering.
This was in 1994 at the legendary MIT leg lab.
He wrote his PhD thesis on robot gymnastics
as part of which he programmed a bipedal robot
to do the world's first 3D robotic somersault.
Robert is a great engineer roboticist and leader
and Boston Dynamics to me as a roboticist
is a truly inspiring company.
This conversation was a big honor and pleasure
and I hope to do a lot of great work with these robots
in the years to come.
This is the Lex Friedman podcast.
To support it, please check out our sponsors
in the description.
And now, dear friends, here's Robert Plater.
When did you first fall in love with robotics?
Let's start with love and robots.
Well, love is relevant because I think the fascination,
the deep fascination is really about movement.
And I was visiting MIT looking for a place to get a PhD
and I wanted to do some laboratory work.
And one of my professors in the aero department said,
go see this guy, Mark 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.
And I just immediately went, whoa, you know?
Robots can do that.
And because of my own interest in gymnastics,
there was like this immediate connection.
And I was interested in, I was in an aero-astro degree
because flight and movement was all so fascinating to me.
And then it turned out that robotics had this big challenge.
How do you balance?
How do you build a legged robot that can really get around?
And that was a fascination and it still exists today.
You were still working on perfecting motion in robots.
What about the elegance
and the beauty of the movement itself?
Is there something maybe grounded in your appreciation
of movement from your gymnastics days?
Did you, was there something you just fundamentally
appreciate about the elegance and beauty of movement?
You know, we had this concept in gymnastics
of letting your body do what it wanted to do.
When you get really good at gymnastics,
part of what you're doing is putting your body
into a position where the physics
and the body's inertia and momentum
will kind of push you in the right direction
in a very natural and organic way.
And the thing that Mark was doing, you know,
in the basement of that laboratory
was trying to figure out how to build machines
to take advantage of those ideas.
How do you build something
so that the physics of the machine
just kind of inherently wants to do what it wants to do?
And he was building these springy pogo stick type,
you know, his first cut at legged locomotion
was a pogo stick where it's bouncing
and there's a spring mass system that's oscillating,
has its own sort of natural frequency there.
And sort of figuring out how to augment
those natural physics with also intent,
how do you then control that, but not overpower it.
It's that coordination that I think creates real potential.
We could call it beauty.
You know, you could call it, I don't know, synergy.
People have different words for it.
But I think that that was inherent from the beginning.
That was clear to me that that's part
of what Mark was trying to do.
He asked me to do that in my research work.
So, you know, that's where it got going.
So part of the thing that I think I'm calling elegance
and beauty in this case, which was there,
even with the pogo stick is maybe the efficiency.
So letting the body do what it wants to do,
trying to discover the efficient movement.
It's definitely more efficient.
It also becomes easier to control in its own way
because the physics are solving some of the problem itself.
It's not like you have to do all this calculation
and overpower the physics.
The physics naturally, inherently
want to do the right thing.
There can even be, you know, feedback mechanisms,
stabilizing mechanisms that occur simply by virtue
of the physics of the body.
And it's, you know, not all in the computer
or not even all in your mind as a person.
And there's something interesting in that melding.
You were with Mark for many, many, many years,
but you were there in this kind of legendary space
of a leg lab at MIT in the basement.
All great things happen in the basement.
Is there some memories,
is there some memories from that time that you have?
Because it's so, it's such cutting edge work
in robotics and artificial intelligence.
The memories, the distinctive lessons I would say
I learned in that time period
and that I think Mark was a great teacher of
was it's okay to pursue your interests, your curiosity.
Do something because you love it.
You'll do it a lot better if you love it.
That is a lasting lesson
that I think we apply at the company still.
And really is a core value.
So the interesting thing is I get to
with people like Ross Tedrick and others,
like the students that work at those robotics labs
are like some of the happiest people I've ever met.
I don't know what that is.
I meet a lot of PhD students.
A lot of them are kind of broken
by the wear and tear of the process.
But roboticists are, while they work extremely hard
and work long hours,
there's a happiness there.
The only other group of people I've met like that
are people that skydive a lot.
Like for some reason there's a deep fulfilling happiness.
Maybe from like a long period of struggle
to get a thing to work and it works
and there's a magic to it, I don't know exactly.
Because it's so fundamentally hands on
and you're bringing a thing to life.
I don't know what it is, but they're happy.
We see, our attrition at the company is really low.
People come and they love the pursuit.
And I think part of that is that
there's perhaps an actual connection to it.
It's a little bit easier to connect
when you have a robot that's moving around in the world
and part of your goal is to make it move around in the world
you can identify with that.
And this is 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 worked with him?
I think the most important lesson
which was have the courage of your convictions
and do what you think is interesting.
Be willing to try to find big, big problems to go after.
And at the time, legged locomotion,
especially in a dynamic machine, nobody had solved it
and that felt like a multi-decade problem to go after.
And so have the courage to go after that
because you're interested.
Don't worry if it's gonna make money.
That's been a theme.
So 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 the simple ideas.
So maybe another important idea
that has really become a value of the company
is try to simplify a thing to the core essence.
And while Marc was showing videos of animals
running across the savanna or climbing mountains,
what he started with was a pogo stick
because he was trying to reduce the problem
to something that was manageable
and getting the pogo stick to balance.
Had in it the fundamental problems that if we solved those,
you could eventually extrapolate
to something that galloped like a horse.
And so look for those simplifying principles.
How tough is the job of simplifying a robot?
So I'd say in the early days,
the thing that made the researchers
at Boston Dynamics special is that we worked
on figuring out what that central principle was
and then building software or machines
around that principle.
And that was not easy in the early days.
And it took real expertise in understanding
the dynamics of motion and feedback control principles.
How to build, and 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 a thousand hertz and actually get that machine to work.
And that was not something everybody was doing
at that time.
Now the world's changing now.
And I think the approaches to controlling robots
are going to change.
And they're going to become more broadly available.
But at the time, there weren't many groups
who could really 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.
Use the robots, break them, repair them,
fix and repeat, test, fix and repeat.
And that's also a core principle
that has become part of the company.
And it lets you be fearless in your work.
Too often, if you are working with a very expensive robot,
maybe one that you bought from somebody else
or that you don't know how to fix,
then you treat it with kit 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?
Because I remember I built a RC car
where it had some custom stuff,
like compute 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, slammed into a wall.
And I just remember sitting there alone in a deep sadness,
sort of full of regret, I think, almost anger,
but also like sadness because you think about,
well, these robots, especially for autonomous vehicles,
like you should be taking safety very seriously,
even in these kinds of things.
But just no good feelings.
It made me more afraid probably
to do this kind of experiments in the future.
Perhaps the right way to have seen that is positively.
It depends if you could have built that car
or just gotten another one, right?
That would have been the approach.
I remember when I got to grad school,
I got some training about operating a lathe and a mill
up in the machine shop,
and I could start to make my own parts.
And I remember breaking some piece of equipment in the lab
and then realizing, because maybe this was a unique part
and I couldn't go buy it.
And I realized, oh, I can just go make it.
That was an enabling feeling.
Then you're not afraid.
Yeah, 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 scientific principles
that let you extrapolate from one-legged machine
to another, develop a core set of principles
like a spring mass bouncing system,
and then figure out how to apply that
from a one-legged machine to a two-
or a four-legged machine.
Those principles are really important,
and were definitely a core part of our work.
There's also, when we started to pursue humanoid robots,
there was so much complexity in that machine
that one of the benefits of the humanoid form
is you have some intuition about how it should look
while it's moving.
And that's a little bit of an art, I think.
Or maybe it's just tapping into a knowledge
that you have deep in your body,
and then trying to express that in the machine.
But that's an intuition that's a little bit more
on the art side.
Maybe it predates your knowledge.
Before you have the knowledge of how to control it,
you try to work through the art channel.
And 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.
There's something about, and being a competitive athlete,
there's something about seeing movement.
A coach, one of the greatest strengths a coach has
is being able to see some little change
in what the athlete is doing,
and then being able to articulate that to the athlete.
And then maybe even trying to say,
and you should try to feel this.
So there's something just in seeing.
And again, sometimes it's hard to articulate
what it is you're seeing.
But there's 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 truly elegant movement.
You have a movie like Ex Machina,
I'm not sure if you've seen it,
but the main actress in that,
who plays the AI robot, I think is a ballerina.
I mean, just the natural elegance
and the, I don't know, eloquence of movement.
It looks efficient and easy, and just, it looks right.
It looks 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, and part of the reason
why people are attracted to the machines we build
is because the inherent dynamics of movement
are closer to right.
Because we try to use walking gates,
or we build a machine around this gate
where you're trying to work with the dynamics of the machine
instead of to stop them.
Some of the early walking machines,
you're essentially, you're really trying hard
to not let them fall over,
and so you're always stopping the tipping motion.
And sort of the insight of dynamic stability
in a legged machine is to go with it,
let the tipping happen, let yourself fall,
but then catch yourself with that next foot.
And there's something about getting those physics
to be expressed in the machine
that people interpret as lifelike,
or elegant, or just natural looking.
And so I think if you get the physics right,
it also ends up being more efficient, likely.
There's a benefit that it probably ends up being
more stable in the long run.
It could walk stably over a wider range of conditions.
And it's more beautiful.
And attractive at the same time.
So how hard is it to get the humanoid robot Atlas
to do some of the things 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 how hard is it to get that right?
Our first humanoid,
we needed to deliver natural looking walking.
We took a contract from the army.
They wanted a robot that could walk naturally.
They wanted to put a suit on the robot
and be able to test it in a gas environment.
And so they wanted the motion to be natural.
And so our goal was a natural looking gate.
It was surprisingly hard to get that to work.
But we did build an early machine.
We called it Petman Prototype.
It was the prototype before the Petman robot.
And it had a really nice looking gate
where it would stick the leg out.
It would do heel strike first
before it rolled onto the toe
so you didn't land with a flat foot.
You extended your leg a little bit.
But even then, it was hard to get the robot to walk
when you were walking that it fully extended its leg
and essentially landed on an extended leg.
And if you watch closely how you walk,
you probably land on an extended leg
but then you immediately flex your knee
as you start to make that contact.
And getting that all to work well took such a long time.
In fact, I probably didn't really see
the nice natural walking that I expected
out of our humanoids until maybe last year.
And the team was developing on our newer generation
of Atlas some new techniques
for developing a walking control algorithm.
And they got that natural looking motion
as sort of a byproduct of just a different process
they were applying to developing the control.
So that probably took 15 years, 10 to 15 years
to sort of get that from, you know,
the Petman Prototype was probably in 2008
and what was it, 2022.
Last year that I think I saw a good walking on Atlas.
If you could just like linger on it,
what are some challenges of getting good walking?
So is it partially like a hardware actuator problem?
Is it the control?
Is it the artistic element of just observing
the whole system operating
in different conditions together?
I mean is there some kind of interesting quirks
or challenges you can speak to?
Like the heel strike or all this kind of?
Yeah, so one of the things that makes
this straight leg a challenge
is you're sort of up against a singularity.
A mathematical singularity where, you know,
when your leg is fully extended,
it can't go further the other direction, right?
There's only, you can only move in one direction.
And that makes all of the calculations
around how to produce torques at that joint
or positions makes it more complicated.
And so having all of the mathematics
so it can deal with these singular configurations
is one of many challenges that we face.
And I'd say in those earlier days, again,
we were working with these really simplified models.
So we're trying to boil all the physics
of the complex human body into a simpler subsystem
that we can more easily describe in mathematics.
And sometimes those simpler subsystems
don't have all of that complexity
of the straight leg built into them.
And so what's happened more recently
is we're able to apply techniques
that let us take the full physics of the robot into account
and deal with some of those strange situations
like the straight leg.
So is there a fundamental challenge here that it's,
maybe you can correct me, but is it underactuated?
Are you falling?
Underactuated is the right word, right?
You can't push the robot in any direction you want to, right?
And so that is one of the hard problems of legged locomotion.
And you have to do that for natural movement.
It's not necessarily required for natural movement.
It's just required, we don't have a gravity force
that you can hook yourself onto to apply an external force
in the direction you want at all times, right?
The only external forces are being mediated
through your feet and how they get mediated
depend on how you place your feet.
And you can't just, God's hand can't reach down
and push in any direction you want, you know, so.
Is there some extra challenge to the fact
that Alice is such a big robot?
There is.
The humanoid form is attractive in many ways,
but it's also a challenge in many ways.
You have this big upper body
that has a lot of mass and inertia.
And throwing that inertia around
increases the complexity of maintaining balance.
And as soon as you pick up something heavy in your arms,
you've made that problem even harder.
And so in the early work, in the leg lab
and in the early days at the company,
we were pursuing these quadruped robots,
which had a kind of built-in simplification.
You had this big rigid body and then really light legs.
So when you swing the legs,
the leg motion didn't impact the body motion very much.
All the mass and inertia was in the body.
But when you have the humanoid, that doesn't work.
You have big heavy legs, you swing the legs,
it affects everything else.
And so dealing with all of that interaction
does make the humanoid a much more complicated platform.
And I also saw that at least recently
you've been doing more explicit modeling
of the stuff you pick up.
Yeah, yeah.
Which is very, really interesting.
So you have to, what, model the shape,
the weight distribution.
I don't know, like you have to include that
as part of the modeling, as part of the planning.
Because, okay, so for people who don't know,
so Atlas, at least in like a recent video,
like throws a heavy bag, throws a bunch of stuff.
So what's involved in picking up a thing, a heavy thing,
and when that thing is a bunch of different
non-standard things, I think it also picked up
like a barbell, and to be able to throw it in some cases,
what are some interesting challenges there?
So we were definitely trying to show
that the robot and the techniques
we're applying to Atlas let us deal
with heavy things in the world.
Because if the robot's 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,
and not heavy stuff anyway.
But just like you or me,
you need to anticipate that moment.
You're reaching out to pick something up,
and as soon as you pick it up,
your center of mass is gonna shift.
And if you're gonna turn in a circle,
you have to take that inertia into account.
And if you're gonna throw a thing,
you've got 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 then sort of predict,
think a couple of seconds ahead,
how do I manage now my body
plus this big heavy thing together
to get, 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 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 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.
And so in some ways, you wanna pay attention
to that detailed shape.
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 had loose stuff in it.
So it managed that.
There are harder things that we haven't done yet.
We could've had a big jointed thing
or I don't know, a bunch of loose wire or rope.
What about carrying another robot?
How about that?
Yeah, we haven't done that yet.
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 a bow on a tree.
So I guess we're doing that in a small way.
Okay, that's pretty good.
Let me ask the all important question.
Do you know how much Atlas can curl?
Have you?
I mean, you know, this, for us humans,
that's really one of the most fundamental questions
you can ask another human being.
Curl, bench.
It probably can't curl as much as we can yet.
But a metric that I think is interesting
is another way of looking at that strength
is the box jump.
So how high of a box can you jump onto?
Good question.
And Atlas, I don't know the exact height.
It was probably a meter high or something like that.
It was a pretty tall jump that Atlas was able to manage
when we last tried to do this.
And I have video of my chief technical officer
doing the same jump.
And he really struggled, you know.
Oh, the human.
But the human, getting all the way on top of this box.
But then Atlas was able to do it.
We're now thinking about the next generation of Atlas.
And we're probably gonna be in the realm
of a person can't do it 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 can jump,
you probably had to do quite a bit of testing.
Oh yeah, and there's lots of videos of it
trying and failing.
And that's all, we don't always release those videos,
but they're a lot of fun to look at.
So we'll talk a little bit about that.
But can you talk to the jumping?
Cause you talked about the walking.
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.
The first time we did a somersault,
there was a lot of kind of manual iteration.
What is the trajectory?
How hard do you throw you?
In fact, in these early days,
I actually would, when I'd see early experiments
that the team was doing,
I might make suggestions about how to change the technique.
Again, kind of borrowing from my own intuition
about how backflips work.
But frankly, they don't need that anymore.
So in the early days, you had to iterate
kind of in almost a manual way,
trying to change these trajectories of the arms or the legs
to try to get a successful backflip to happen.
But more recently, we're running
these model predictive control techniques
where we're able to,
the robot essentially can think in advance
for the next second or two
about how its motion is going to transpire.
And you can 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
and behind it, but we're figuring that out.
So you can do model predictive control for,
I mean, I don't even understand what that looks like
when the entire robot is in the air,
flying and doing a backflip.
I mean.
But that's the cool part, right?
So the physics, we can calculate physics pretty well
using Newton's laws about how it's going to evolve over time
and the road, 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 what this model predictive control means is,
again, in real time, the robot is projecting ahead
a second into the future and sort of exploring options.
And if I move my arm a little bit more this way,
how is that going to affect the outcome?
And so it can do these calculations, many of them,
and basically solve where, given where I am now,
maybe I took off a little bit screwy from how I had planned,
I can adjust.
So you're adjusting in the air.
Adjust on the fly.
So the model predictive control lets you adjust on the fly.
And of course, I think this is what people adapt as well.
When we do it, even a gymnastics trick,
we try to set it up so it's as close to the same every time,
but we figured out how to do some adjustment on the fly
and now we're starting to figure out that the robots
can do this adjustment on the fly as well,
using these techniques.
In the air.
It's so, I mean, it just feels,
from a robotics perspective, just surreal.
Well, that's sort of the,
you talked about underactuated, right?
So 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
because you can't apply an external force or torque.
And so the momentum isn't going to change.
So how do you work within the constraint
of that fixed momentum to still get from A to B
where you want to be?
That's really underactuated.
You're in the air.
I mean, you become a drone for a brief moment in time.
No, you're not even a drone because you can't.
Can't hover.
You can't hover.
You're going to impact soon.
Be ready.
Yeah.
Are you considered like a hover type thing or no?
No, it's too much weight.
I mean, it's just incredible.
It's just even to have the guts to try a backflip
with such a large body.
That's wild.
Well, we definitely broke a few robots trying to do that.
But that's where the build it, break it, fix it
strategy comes in.
You got to be willing to break.
And what ends up happening is you end up
by breaking the robot repeatedly, you find the weak points
and then you end up redesigning it
so it doesn't break so easily next time.
Through the breaking process, you learn a lot,
like a lot of lessons and you keep improving
not just how to make the backflip work, but everything.
And how to build the machine better.
Yeah.
I mean, is there something about just the guts
to come up with an idea of saying, you know what,
let's try to make it do a backflip?
Well, I think the courage to do a backflip
in the first place and to not worry too much
about the ridicule of somebody saying,
why the heck are you doing backflips with robots?
Because a lot of people have asked that, you know,
why are you doing this?
Why go to the moon in this decade
and do the other things JFK?
Yeah.
Not because it's easy, because it's hard.
Yeah, exactly.
Don't ask questions.
Okay, so the jumping.
I mean, it's just, there's a lot of incredible stuff.
If we can just rewind a little bit
to the DARPA Robotics Challenge in 2015, I think,
which was for people who are familiar
with the DARPA challenges,
it was first with autonomous vehicles
and there's a lot of interesting challenges around that.
And the DARPA Robotics Challenge was when humanoid robots
were tasked to do all kinds of, you know,
manipulation, walking, 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.
You could have periods where the communication
was entirely interrupted and the robot
had to be able to proceed.
But you could provide some high-level guidance
to the robot, basically low bandwidth communications
to steer it.
I watched that challenge with kind of tears in my eyes,
eating popcorn, it was hard.
Us too.
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.
So anyway, what have you,
just looking back to that time,
what have you learned from that experience?
I mean, maybe if you could describe what it was,
sort of the setup for people who haven't seen it.
Well, so there was a contest
where a bunch of different robots
were asked to do a series of tasks,
some of those that you mentioned,
drive a vehicle, get out, open a door,
go identify a valve, shut a valve,
use a tool to maybe cut a hole in a surface,
and then crawl over some stairs and maybe some rough terrain.
So it was, the idea was have a general purpose robot
that could do lots of different things,
had to be mobility and manipulation, on-board perception.
And there was a contest, which DARPA likes,
at the time was running, sort of follow on
to the grand challenge, which was let's try
to push vehicle autonomy along, right?
They encouraged people to build autonomous cars.
So they're trying to basically push an industry forward.
And we were asked, our role in this was to build a humanoid,
at the time it was our sort of first-generation
Atlas robot, and we built maybe 10 of them.
I don't remember the exact number.
And DARPA distributed those to various teams
that sort of won a contest,
showed that they could program these robots,
and then used 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 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 tiers were because
mostly the robots didn't do it.
They fell down repeatedly.
It was hard to get through this contest.
Some did, and they were rewarded 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.
At heart.
The general nature of it, the variety of it.
The variety.
And also, that I don't know if the tasks were
sort of, the tasks 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.
I think Atlas is really a general robot platform.
And it's perhaps not best suited
for the specific tasks of that contest.
For just, for example, probably the hardest task
is not the driving of the car,
but getting in and out of the car.
And Atlas probably, 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.
The robot was a little bit big
to get in and out of that car, right?
It doesn't fit, yeah.
This is the curse of a general purpose robot,
that they're not perfect at any one thing,
but they might be able to do a wide variety of things.
And that is the goal at the end of the day.
I think we all want to build general purpose robots
that can be used for lots of different activities,
but it's hard.
And the wisdom in building successful robots
up until this point have been,
go build a robot for a specific task
and it'll do it very well.
And as long as you control that environment,
it'll operate perfectly.
But robots need to be able to deal with uncertainty.
If they're 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
for a multipurpose robot.
And that's just darn hard.
And so there's these curious little failures.
Like I remember one of the, a robot,
the first time you start to try to push on the world
with a robot, you forget that the world pushes back
and will push you over if you're not ready for it.
And the robot reached to grab the door handle.
I think it missed the grasp of the door handle.
Was expecting that its hand was on the door handle.
And so when it tried to turn the knob,
it just threw itself over.
It didn't realize, oh, I had missed the door handle.
I didn't have, I didn't,
I was expecting a force back from the door.
It wasn't there.
And then I lost my balance.
So these little simple things that you and I
would take totally for granted and deal with,
the robots don't know how to deal with yet.
And so you have to start to deal
with all of those circumstances.
Well, I think a lot of us experienced this in,
even when sober, but drunk too,
sort of you pick up a thing and expect it to be,
what is it, heavy?
And it turns out to be light.
Yeah, and then you, whoa.
Oh yeah, and then, so the same,
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.
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 when you don't.
I mean, that also requires a very fast feedback loop
of updating what your prediction,
how it matches to the actual real world.
Yeah, those things have to run pretty quickly.
What's the challenge of running things pretty quickly?
A thousand hertz, of acting and sensing quickly?
You know, there's a few different layers of that.
You want, at the lowest level,
you like to run things typically at around a thousand hertz,
which means that 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 a thousand hertz.
And that means you can't have too much calculation
going on at that joint.
But that's pretty manageable these days
and it's fairly common.
And then there's another layer
that you're probably calculating,
you know, maybe at a hundred hertz, maybe 10 times slower,
which is now starting to look at the overall body motion
and thinking about the larger physics of the robot.
And then there's yet another loop
that's probably happening a little bit slower,
which is where you start to bring, you know,
your perception and your vision and things like that.
And so you need to run all of these loops
sort of simultaneously.
You do have to manage your computer time
so that you can squeeze in all the calculations you need
in real time in a very consistent way.
And the amount of calculation we can do
is increasing as computers get better,
which means we can start to do
more sophisticated calculations.
I can have a more complex model doing my forward prediction.
And that might allow me to do even better predictions
as I get better and better.
And it used to be, again, we had, you know, 10 years ago,
we had to have pretty simple models
that we were running, you know, at those fast rates
because the computers weren't as capable
about calculating forward with a sophisticated model.
But as 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, a few years, several years ago.
But that was a, you know, that was a bit of a milestone.
And so you want to work, certainly work these pipelines
so that you can make it as easy as possible
and have a bunch of people working in parallel,
especially when, you know, we only have, you know,
four of the Atlas robots,
the modern Atlas robots at the company.
And, you know, we probably have, you know,
40 developers there all trying to gain access to it.
And so you need to share resources
and use some of these, some of the software pipeline.
Well, that's a really exciting step
to be able to run the exact same code in simulation
as on the actual robot.
How hard is it to do
realistic simulation, physics-based simulation of Atlas,
such that, I mean, the dream is like,
if it works in simulation, it works perfectly in reality.
How hard is it to sort of keep 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.
So trying to model those contact events well
in a way that captured the important parts
of the interaction was a really important element
to get right and to also do in a way
that was computationally feasible and could run fast,
because if your simulation runs too slow,
then your developers are sitting around
waiting for stuff to run and compile.
So it's always about efficient, fast operation as well.
So that's been a big part of it.
I think developing those tools
in parallel to the development of the platform
and trying to scale them has really been essential,
I'd say, to us being able to assemble
a team of people that could do this.
Yeah, how to simulate contact, 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,
because there's a lot more going on, you know,
and you need to capture, I don't know,
things slipping and moving in your hand.
It's a level of complexity that I think
goes above foot-ground contact
when you really start doing dexterous manipulation.
So there's challenges ahead still.
So how far are we away from me being able
to walk with Atlas in the sand along the beach
and us both drinking a beer?
Yeah, out of a can, out of a can.
Maybe Atlas could spill his beer,
because he's got nowhere to put it.
Atlas could walk on the sand.
So can it?
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 a-
Salt surfaces.
I don't doubt that we could deal with it.
We might have to spend a little bit of time
to sort of make that work, but we did take,
we had to take Big Dog to Thailand years ago,
and we did this great video of the robot
walking in the sand, walking into the ocean,
up to, I don't know, its belly or something like that,
and then turning around and walking out,
all while playing some cool beach music.
Great show, but then, you know,
we didn't really clean the robot off,
and the salt water 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
with corrosion.
So it's the salt water, it's not like-
Salt stuff.
It's not like sand getting into the components
or something like this.
But I'm sure if this is a big priority,
you can make it like waterproof or something.
That just wasn't our goal at the time.
Well, it's a personal goal of mine,
to walk along the beach.
But it's a human problem, too.
You get sand everywhere, it's just a giant mess.
So soft surfaces are okay.
So, I mean, can we just linger on the robotics challenge?
There's a pile of, like, rubble there to walk over.
Is that, how difficult is that task?
In the early days of developing Big Dog,
the loose rock was the epitome of the hard walking surface,
because you step down, and then the rock,
and you have these little point feet on the robot,
and the rock can roll.
And you have to deal with that last minute change
in your foot placement.
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, it's really, that became kind of the essence
of the test.
And so, that was the beginning of us starting
to build rock piles in our parking lots,
and we would actually build boxes full of rocks
and bring them into the lab,
and then we would have the robots walking
across these boxes of rocks,
because that became the essential test.
So, you mentioned Big Dog, can we maybe take a stroll
through the history of Boston Dynamics?
So, what and who is Big Dog?
By the way, is who,
do you try not to anthropomorphize the robots?
Do you try not to, do you try to remember that they're,
this is like the division I have,
because for me, it's impossible.
For me, there's a magic to the being that is a robot.
It is not human, but it is,
the same magic that a living being has
when it moves about the world is there in the robot.
So, I don't know what question I'm asking,
but should I say what or who, I guess.
Who is Big Dog, what is Big Dog?
Well, I'll say, to address the 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?
People, I think part of the magic of these kinds of machines
is by nature of their organic movement, of their dynamics,
we tend to want to identify with them.
We tend to look at them and sort of attribute,
maybe feeling to that,
because we've only seen things that move like this
that were alive.
And so, this is an opportunity.
It means that you could have feelings for a machine,
and people have feelings for their cars.
They get attracted to them, attached to them.
So, that 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 also can be a bit of a curse,
because I think people look at these machines,
and they attribute a level of intelligence
that the machines don't have.
Why?
Because again, they've seen things move like this
that were living beings, which are intelligent.
And so, they want to attribute intelligence to the robots
that isn't appropriate yet,
even though they move like an intelligent being.
But you try to acknowledge
that the anthropomorphization is there,
and try to, first of all, acknowledge that it's there,
and have a little fun with it.
You know, our most recent video,
it's just kind of fun to look at the robot.
We started off the video with Atlas,
kind of looking around for where the bag of tools was,
because the guy up on the scaffolding says,
send me some tools.
Atlas has to kind of look around and see where they are.
And there's a little personality there that is fun,
it's entertaining, it makes our jobs interesting,
and I think in the long run,
can enhance interaction between humans and robots
in a way that isn't available
to machines that don't move that way.
This is something, to me personally, is very interesting.
I happen to have a lot of legged robots.
I hope to have a lot of spots in my possession.
I'm interested in celebrating robotics
and celebrating companies, and I also don't want to,
companies that do incredible stuff like Boston Dynamics,
there's a, you know, I'm a little crazy.
And you say you don't want to, you want to align,
you want to help the company,
because I ultimately want a company like Boston Dynamics
to succeed, and part of that we'll talk about,
you know, success kind of requires making money.
And so the kind of stuff I'm particularly interested in
may not be the thing that makes money in the short term.
I can make an argument that it will in the long term.
But the kind of stuff I've been playing with
is a robust way of having the quadrupeds,
the robot dogs, communicate in motion
with their body movement.
The same kind of stuff you do with a dog.
But not hard-coded, but in a robust way.
And be able to communicate excitement or fear,
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 going to be really important.
And it's a thing we're beginning to pay attention to.
We really want to start,
a differentiator for the company has always been,
we really want the robot to work.
We want it to be useful.
Making it work at first meant
the 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,
taking measurements.
We need for people who are operating those robots
to understand what the robots are doing.
If the robot needs help or is in trouble or something,
it needs to be able to communicate.
And a physical indication of some sort
so that a person looks at the robot and goes,
oh, I know what that robot's doing.
The robot's going to go take measurements
of my vacuum pump with its thermal camera.
You want to be able to indicate that.
And we're even just, the robot's about to turn
in front of you and maybe indicate that it's going to turn.
And so you sort of see and can anticipate its motion.
So this kind of communication is going to become
more and more important.
It wasn't sort of our starting point.
But now that the robots are really out in the world
and we have about 1,000 of them out
with customers right now,
this layer of physical indication,
I think is going to become more and more important.
We'll talk about where it goes
because there's a lot of interesting possibilities.
But if 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 that scale.
So big dog.
Who's big dog?
So the company started in 1992.
And in probably 2003,
2003, I believe is when we took a contract
from DARPA, so basically 10 years, 11 years.
We weren't doing robotics.
We did a little bit of robotics with Sony.
They had a AIBO, their AIBO robot.
We were developing some software for that
that kind of got us a little bit involved
with robotics again.
Then there's this opportunity to do a DARPA contract
where they wanted to build a robot dog.
We won a contract to build that.
And so that was the genesis of big dog.
And it was a quadruped.
It was the first time we built a robot
that had everything onboard
that you could actually take the robot
out into the wild and operate it.
So it had an onboard power plant.
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.
I mean, can you just comment on the roughness
of that transition?
Because big dog, I mean, this is this big
quadruped, four legs robot.
We built a few different versions of them.
But the first one, the very earliest ones,
didn't work very well.
We would take them out.
And it was hard to get a go-kart engine
driving a hydraulic pump.
I was not what it was.
And having that all work while trying to get
the robot to stabilize itself.
So what was the power plant?
What was the engine?
It seemed like my vague recollection.
It, I don't know, it felt very loud and aggressive
and kind of thrown together.
That's 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 big dog
had literally go-kart engines or something like that.
Usually it's like a gas-powered two-stroke engine.
And the reason why it was two-stroke
is two-stroke engines are lighter weight.
That they're also, and we generally didn't put mufflers
on them, because we're trying to save the weight.
And we didn't care about the noise.
And so these things were horribly loud.
But we're trying to manage weight,
because managing weight in a legged robot
is always important, because it has to carry everything.
That said, that thing was big.
Well, I've seen the videos of it.
Yeah, I mean, the early versions stood about,
I don't know, belly high, chest high.
They probably weighed maybe a couple of hundred pounds.
But over the course of probably five years,
we were able to get that robot
to really manage a remarkable level of rough terrain.
So 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 by the end of that program,
we were convinced that legged locomotion
in a robot could actually work.
Because going into it, we didn't know that.
We had built quadrupeds at MIT,
but they used a giant hydraulic pump in the lab.
They used a giant computer that was in the lab.
They were always tethered to the lab.
This was the first time something
that was sort of self-contained
walked around in the world.
And balanced.
And the purpose was to prove to ourself
that the legged locomotion could really work.
And so, Big Dog really cut that open for us.
And it was the beginning of what became
a whole series of robots.
So once we showed to DARPA
that you could make a legged robot that could work,
there was a period at DARPA where robotics got really hot,
and there was lots of different programs.
And we were able to build other robots.
We built other quadrupeds to hand,
like LS3, designed to carry heavy loads.
We built Cheetah, which was designed to explore
what are the limits to how fast you can run.
We began to build sort of a portfolio
of machines and software that let us build
not just one robot, but a whole family of robots.
To push the limits in all kinds of directions.
Yeah, and to discover those principles.
You asked earlier about the art and science
of a 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 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 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,
well, what's your product gonna be?
And the logical thing,
the thing that we had the most history with
that we wanted to continue developing was our quadruped.
But we knew it needed to be smaller.
We knew it couldn't have a gas engine.
We thought it probably couldn't be hydraulically actuated.
So that began the process of exploring if we could migrate
to a smaller electrically actuated robot.
And that was really the genesis of Spot.
So not a gas engine and the actuators are electric.
Yes.
So can you maybe comment on what it's like at Google
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 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.
Of course, we were only building two,
but we didn't see how to get all the way
to this consumer level product.
In a short amount of time.
In a short amount of time.
And he suggested that we make the robots really inexpensive.
And part of our philosophy has always been
build the best hardware you can.
Make the machine operate well.
So that you're trying to solve,
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
under-actuated 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 their productivity depends on
keeping their factory going.
And so if Spot costs $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 getting there
needs to go through these really nice machines
so that we can then learn how to simplify.
So what can you say to the almost engineering challenge
of bringing down cost of a robot?
So that presumably, when you try to build the robot
at scale, that also comes into play
when you're trying to make money on a robot,
even in the industrial setting.
But how interesting, how challenging of a thing is that,
in particular, probably new to an R&D company?
Yeah, I'm glad you brought that last part up.
The transition from an R&D company to a commercial company,
that's the thing you worry about.
Because 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, 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, even having to get reliability to where it needs to be,
we have to have fleets of robots
that we're just operating 24-7 in our offices
to go find those rare failures and eliminate them.
It's just a totally different kind of activity
than the research activity where you get it to work,
you know, the one robot you have to work in a repeatable way
you know, at the high-stakes demo.
It's just very different.
But I think we're making remarkable progress, I guess.
So, one of the cool things I got
a chance to visit Boston Dynamics and, I mean,
one of the things that's really cool
is to see a large number of robots moving about.
Because I think one of the things you notice
in the research environment at MIT, for example,
I don't think anyone ever has a working robot
for a prolonged period of time.
Exactly.
So, like, most robots are just sitting there
in a sad state of despair waiting to be born,
brought to life for a brief moment of time.
Just to have, 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 supervising it.
It was just doing its thing.
I'm gonna stop way short of the sentience.
It is the case that if you come to our office today
and walk around the hallways,
you're gonna see a dozen robots
just kind of walking around all the time.
And that's really a reliability test for us.
So we have these robots programmed
to do autonomous missions, get up off their charging dock,
walk around the building, collect data
at a few different places and go sit back down.
And we want that to be a very reliable process
because that's what somebody who's running a brewery,
a factory, that's what they need the robot to do.
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 over 1,000 hours of operation every week.
And that's something that almost,
I don't think anybody else in the world can do
because A, you have to have a fleet of robots
to just accrue that much information.
You have to be willing to dedicate it to that test.
And so that's, but that's essential.
That's how you get the reliability.
That's how you get it.
What about some of the cost cutting
from the manufacturer side?
What have you learned from the manufacturer side
of the transition from R&D?
And we're still learning a lot there.
We're learning how to cast parts
instead of mill it all out of billet aluminum.
We're learning how to get plastic molded parts
and we're learning about how to control that process
so that you can build the same robot twice in a row.
There's a lot to learn there
and we're only partway through that process.
We've set up a manufacturing facility in Wolf M.
It's about a mile from our headquarters
and we're doing final assembly and test
of both spots and stretches 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 want to move as fast as we do.
And that's been challenging and it makes it,
the folks who are working supply chain
who are trying to get the cheapest parts for us
kind of requires that you buy a lot of them
to make them cheap.
And then we go change the design from underneath them
and they're like, what are you doing?
And so getting everybody on the same page here,
yep, we still need to move fast
but we also need to try to figure out how to reduce costs.
That's one of the challenges of this migration
we're going through.
And over the past few years,
challenges to the supply chain.
I'm imagining 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 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.
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 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
a next generation of that machine.
So we should mention that Spot,
for people who somehow are not familiar,
so it's a yellow robotic dog
and has been featured in many dance videos.
It also has gained an arm.
So what can you say about the arm that Spot has,
about the challenges of this design and the manufacture of it?
We think the future of mobile robots is mobile manipulation.
That's where, in the past 10 years,
it was getting mobility to work,
getting the leg and locomotion to work.
If you ask what's the hard problem in the next 10 years,
it's getting a mobile robot
to do useful manipulation for you.
And so we wanted Spot to have an arm
to experiment with those problems.
And the arm is almost as complex as the robot itself.
And it's an attachable payload.
It has several motors and actuators and sensors.
It has a camera in the end of its hand,
so 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 so, and of course we made the hand
look a little like a head, you know,
because again, we want it to be sort of identifiable.
In the last year, a lot of our sales
have been people who already have a robot
now buying an arm to add to that robot.
Oh, interesting.
And so the arm is for sale.
Oh yeah, oh yeah, it's an option.
What's the interface like to work with the arm?
Like is it pretty, so are they designed primarily,
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 of,
if you have an 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 just, you can program easily with the robot,
strictly through, you know, we have a tablet interface.
And so on the tablet, you know,
you sort of see the view that Spot sees.
You say, there's the door handle.
You know, the hinges are on the left and it opens in.
The rest is up to you.
Take care of it.
Oh, so it just takes care of everything.
Yeah.
So we want, and for a task like opening doors,
you can automate most of that.
And we've automated a few other tasks.
We had a customer who had a high-powered
breaker switch, essentially.
It's an electric utility, Ontario power generation.
And they have to, when they're 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
involved in this breaker switch.
And it's a dangerous event,
because occasionally you'll get what's called an arc flash.
As you just do this disconnect,
the power, the sparks jump across
and people die doing this.
And so Ontario power generation used our Spot
and the arm through the interface
to operate this disconnect
in an interactive way.
And they showed it to us.
And we were so excited about it and said,
you know, I bet we can automate that task.
And so we got some examples of that breaker switch.
And I believe in the next generation of 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.
Yeah, maintains its balance.
It walks itself 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.
I mean, that has to be like one of the most requests,
I suppose, in the industrial setting.
That's a non-alcoholic beverage of picking up objects
and bringing the objects to you.
We love working with customers
who have challenging problems like this.
And this one in particular,
because we felt like what they were doing,
A, it was a safety feature.
B, we saw that the robot could do it
cause they teleoperated it the first time.
Probably took them an hour to do it the first time, right?
But the robot was clearly capable.
And we thought, oh, this is a great problem
for us to work on,
to figure out how to automate a manipulation task.
And so we took it on,
not because we were 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,
we try to, 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.
And if they're, 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.
Note to self, buy 10 spots
and aggressively push for beer manipulation.
I think it's fair to say it's notoriously difficult
to make a lot of money as a robotics company.
How can you make money as a robotics company?
Can you speak to that?
It seems that a lot of robotics companies fail.
It's difficult to build robots.
It's difficult to build robots at a low enough cost
where customers, even the industrial setting,
want to purchase them.
And it's difficult to build robots that are useful,
sufficiently useful.
What can you speak to?
And Boston Dynamics has been successful
for many years of finding a way to make money.
Well, in the early days, of course,
the money we made was from doing contract R&D work.
And we made money, but we weren't growing
and we weren't selling a product.
And then we went through several owners
who had a vision of not only developing advanced technology,
but eventually developing products.
And so both Google and SoftBank and now Hyundai
had that vision and were willing to provide that investment.
Now, our discipline is that we need to go find applications
that are broad enough that you could imagine
selling thousands of robots.
Because it doesn't work if you don't sell thousands
or tens of thousands of robots.
If you only sell hundreds, you will commercially fail.
And that's where most of the small robot companies
have died.
And that's a challenge because,
A, you need to field the robots.
They need to start to become reliable.
And as we'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 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.
We're working with Global Foundries.
It takes place in electric utilities
and nuclear power plants.
And so the same robot can be applied
in all of these industries.
And as I said, we have about,
actually it's 1,100 spots out now
to really get profitability.
We need to be at 1,000 a year,
maybe 1,500 a year for that sort of part of the business.
So it still needs to grow,
but we're on a good path.
So I think that's totally achievable.
So the application should require
crossing that 1,000 robot barrier.
It really should, yeah.
I wanna mention our second robot, Stretch.
Yeah, tell me about Stretch.
What's Stretch?
Who's Stretch?
Stretch started differently than Spot.
We built because we had decades
of experience building quadrupeds.
We just, we had it in our blood.
We had to build a quadruped product.
But we had to go figure out what the application was.
And we actually discovered this factory patrol application,
basically preventative maintenance,
by seeing what our customers did with it.
Stretch is very different.
We started knowing that there was warehouses
all over the world.
There's shipping containers moving
all around the world full of boxes
that are mostly being moved by hand.
By some estimates, we think there's a trillion boxes,
cardboard boxes, shipped around the world each year.
And a lot of it's done manually.
It became clear early on that there was an opportunity
for a mobile robot in here to move boxes around.
And the commercial experience has been very different
between Stretch and with Spot.
As soon as we started talking to people,
potential customers, about what Stretch
was gonna be used for, they immediately started saying,
oh, I'll buy, I'll buy that robot.
In fact, I'm gonna put in an order for 20 right now.
We just started shipping the robot in January,
after several years of development.
Of this year.
Of this year.
So our first deliveries of Stretch to customers
were DHL and Maersk in January.
We're delivering the Gap right now.
And we have about seven or eight other customers,
all who've already agreed in advance
to buy between 10 and 20 robots.
And so we've already got commitments
for a couple hundred of these robots.
This one's 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.
It's not a legged robot.
It's not a legged robot.
And so it was our first,
it was actually a bit of a
commitment from us, a challenge for us,
to build a non-balancing robot.
To do the much easier problem.
And to put to do a well.
Well, because it wasn't, 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.
All right, it's a beautiful machine.
It's an incredible machine.
So it was,
I mean, it looks epic.
It looks like out of a,
I mean, out of a sci-fi movie of some sort.
I mean, just, can you actually just linger
on 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, I'd love talking about the history
of how Handle came about.
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 tight rope,
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 the balance,
but it was gonna have a big long arm
so it could reach a box at the top of a truck.
And it was gonna, it needed yet another counterbalance,
a big tail, to help it balance while it was using its arm.
So the reason why this robot sort of looks epic,
some people said it looked like an ostrich,
or maybe an ostrich moving around,
was the wheels, it has legs so it can extend its legs.
So it's wheels on legs.
We always wanted to build wheels on legs.
It had a tail and 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.
And it would do it beautifully with elegance.
It did beautifully, but it just wasn't efficient enough.
So we let it go.
But I think we'll come back to that machine eventually.
The fact that it's possible,
the fact that you showed that you could do so many things
at the same time in coordination and so beautifully,
there's something there.
That was a demonstration of what is possible.
Basically, we made a hard decision
and this was really kind of a hard-nosed business decision.
It indicated us not doing it just for the beauty
of the mathematics or the curiosity,
but no, we actually need to build a business
that can make money in the long run.
And so we ended up building Stretch,
which has a big heavy base with a giant battery
in the base of it that allows it to run for two shifts,
16 hours worth of operation.
And that big battery sort of helps it stay balanced, right?
So you can move a 50-pound box around with its arm
and not tip over.
It's omnidirectional.
It can move in any direction.
So it has a nice suspension built into it
so it can deal with gaps or things on the floor
and roll over it.
But it's not a balancing robot.
It's a mobile robot arm that can work to carry
or pick or place a box up to 50 pounds
anywhere in the warehouse.
Take a box from point A to point B anywhere.
Yeah, palletize, depalletize.
We're starting with unloading trucks
because there's so many trucks and containers
that 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 Mariskan Gap,
the warehouse workers who were doing that manual labor
are now the robot operators.
And so we see this as ultimately a benefit to them as well.
Can you say how much stretch costs?
Not yet.
But I will say that when we engage with our customers,
they'll be able to see a return on investment
in typically two years.
Okay, so that's something
that you're constantly thinking about, how.
And I suppose you have to do
the same kind of thinking with Spot.
So it seems like with Stretch,
the application is directly obvious.
Yeah, it's a slam dunk.
Yeah, and so you have a little more flexibility.
Well, I think we know the target.
We know what we're going after.
And with Spot, it took us a while
to figure out what we were going after.
Well, let me return to that question
about maybe the conversation you were having
a while ago with Larry Page,
maybe looking to the longer future of social robotics,
of using Spot to connect with human beings,
perhaps in the home.
Do you see a future there?
If we were to sort of hypothesize or dream
about a future where Spot-like robots are in the home
as pets, social robots.
We definitely think about it.
And we would like to get there.
We think the pathway to getting there
is likely through these industrial applications
and then mass manufacturing.
Let's figure out how to build the robots,
how to make the software
so that they can really do a broad set of skills.
That's gonna take real investment to get there.
Performance first, right?
The principle of the company has always been
really make the robots do useful stuff.
And so the social robot companies
that tried 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 consumer-level market.
And so, yeah, maybe we'll be able to build a smaller spot
with an arm that could really go get your beer for you.
But there's things we need to figure out still.
How to safely, really safely,
and if you're gonna be interacting with children,
you better be safe.
And right now, we count on a little bit of standoff distance
between the robot and people
so that you don't pinch a finger in the robot.
So you've got a lot of things you need to go solve
before you jump to that consumer-level product.
Well, there's a kind of trade-off in safety
because it feels like in the home, you can fall.
Like, you don't have to be as good at,
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
because it feels like in the factory,
you're not allowed to fail.
That may be true, but I also think the variety of things
a consumer-level robot would be expected to do
will also be quite broad.
And they're gonna want to get the beer
and know the difference between the beer and a Coca-Cola
or my snack.
And they're all gonna want you to clean up the dishes
from the table without breaking them.
Those are pretty complex tasks.
And so there's still work to be done there.
So to push back on that, here's what application
I think will be very interesting.
I think the application of being a pet, a friend.
So like, no tasks, just be cute.
Because I, not cute, not cute.
A dog is more than just cute.
A dog is a friend, is a companion.
There's something about just having interacted with them,
and maybe because I'm hanging out alone
with the robot dogs too much.
But there's a connection there,
and it feels like that connection is not,
should not be disregarded.
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, the Sony Aibo?
They sold over 100,000 of those, maybe 150,000.
You know, it probably wasn't considered
a successful product for them.
They suspended that eventually,
and then they brought it back, Sony brought it back.
And people definitely treated this as a pet,
as a companion.
And I think that will come around again.
Will you get away without having any other utility?
Maybe in a world where we can really talk
to our simple little pet, because, you know,
Jack GPT 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.
So, which is that the very path you're taking,
which is creating a great robot platform,
will very easily take a leap to adding
a Chad GPT-like capability, maybe GPT-5.
And there's just so many open source alternatives
you could just plop that on top of the spot.
And because you have this robust platform,
and you're figuring out how to mass manufacture it
and how to drive the cost down,
and how to make it reliable, all those kinds of things,
it'll be a natural transition to where
just adding Chad GPT on top of it will create it.
Oh, I do think that being able to verbally converse,
or even converse through gestures,
part of these learning models is that
you can now look at video and imagery
and associate intent with that.
Those will all help in the communication
between robots and people, for sure.
And that's gonna happen, obviously,
more quickly than any of us were expecting.
I mean, what else do you want from life?
A friend to get your beer,
and then just talk shit about the state of the world.
I mean, where there's a deep loneliness within all of us,
and I think a beer and a good chat solves so much of it,
or it takes us a long way to solving a lot of it.
It'll be interesting to see
when a generative AI can give you that warm feeling
that you connected, and that, oh, yeah, you remember me,
you're my friend, we have a history.
That history matters, right?
Memory of joint, like joint?
Memory of, yeah.
Having witnessed, that's what friendship,
that's what connection, that's what love is in many cases.
Some of the deepest friendships you have
is having gone through a difficult time together,
and having a shared memory of an amazing time
or a difficult time, and kind of that memory
creating this foundation based on which
you can then experience the world together.
The silly, the mundane stuff of day to day
is somehow built on a foundation
of having gone through some shit in the past.
And the current systems are not personalized in that way,
but I think that's a technical problem,
not some kind of fundamental limitation.
So combine that with an embodied robot like Spot,
which already has magic in its movement.
I think it's a very interesting possibility
of where that takes us.
But of course you have to build that
on top of a company that's making money
with real application, with real customers,
and with robots that are safe and at work
and reliable and manufactured at scale.
And I think we're in a unique position in that
because of 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, what does mobility mean in the future?
How is this mobile robot technology going to influence us?
Can we shape that?
And they want both.
And so we 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's become a friend.
So Elon Musk and Tesla have announced
in the early days of building a humanoid robot.
How does that change the landscape of your work?
So there's sort of from the outside perspective,
it seems like, well, as a fan of robotics,
it just seems exciting.
Very exciting, right?
When Elon speaks, people listen.
And so it suddenly brought a bright light
onto the work that we'd been doing 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?
Yes.
And interestingly, many of them as they're raising money,
for example, will claim whether or not
they have a former Boston Dynamics employee
on their staff as a criteria.
Yeah, that's true.
That's a, I would do that as a company, yeah, for sure.
And shows you're legit, yeah.
Yeah, so you know what, it's bring,
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,
maybe he implied that the problem is solvable
in the near term, which is a low cost humanoid robot
that's able to do,
that's a relatively general use case robot.
So I think Elon is known for sort of setting
these kinds of incredibly ambitious goals.
Maybe missing deadlines, but actually pushing
not just the particular team he leads,
but the entire world to accomplishing those.
Do you see, do you see Boston Dynamics in the near future
being pushed in that kind of way?
Like this excitement of competition kind of
pushing Atlas maybe to do more cool stuff,
trying to drive the cost of Atlas down perhaps?
Or, I mean, I guess I wanna ask if there's
some kind of exciting energy in Boston Dynamics
due to this a little bit of competition.
Oh yeah, definitely.
When we released our most recent video of Atlas,
you know, I think you'd seen it,
scaffolding and throwing the box of tools around
and then doing the flip at the end.
We were trying to show the world
that not only can we do this parkour mobility thing,
but we can pick up and move heavy things.
Because if you're 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
by picking up a 50-pound block
for a robot that weighs 150 pounds,
that's a lot to accommodate.
So we're trying to show that we can do that.
And so it's totally been energizing.
We see the next phase of Atlas being more dexterous hands
that can manipulate and grab more things,
that we're gonna start by moving big things around
that are heavy and that affect balance.
And why is that?
Well, really tiny dexterous things
probably are gonna be hard for a while yet.
Maybe you could go build a special-purpose robot arm
for stuffing chips into electronics boards,
but we don't really wanna do really fine work like that.
I think more coursework, where you're using two hands
to pick up and balance an unwieldy thing,
maybe in a manufacturing environment,
maybe in a construction environment,
those are the things that we think robots
are 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 I think, 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 this, 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
and scaling.
By scaling, you can get efficiencies
that drive cost down very well.
And the smart thing that, you know,
they have in their favor is that, you know,
they know how to manufacture,
they know how to build electric motors,
they know how to build our 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 the automotive company behind us as well.
So bring it on.
Who's doing pretty well, right?
The electric vehicles from Hyundai are doing pretty well.
I love it.
So how much, so we've talked about some of the
low-level controls, some of the incredible stuff
that's going on, and basic perception.
But how much do you see currently in the future
of Boston Dynamics' sort of 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 oughta be something you can just download.
Like, I'm going to a new environment,
I have a new kind of door handle or piece of equipment
I wanna inspect, you oughta be able to just download that.
And I think people, besides Boston Dynamics,
will provide that, and we've actually built an API
that lets people add these vision algorithms to Spot.
And we're currently working with some partners
who are providing that.
Levitas is an example of a small provider
who's giving a software for reading gauges.
And actually another partner in Europe,
Reply is doing the same thing.
So we see that, we see ultimately an ecosystem
of providers doing stuff like that.
And I think ultimately, you might even be able
to do the same thing with behaviors.
So this technology will also be brought to bear
on controlling the robot, the motions of the robot.
And we're using reinforcement learning
to develop algorithms for both locomotion and manipulation.
And ultimately, this is gonna mean
you can add new behaviors to a robot 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
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.
Yes.
So there's increasing levels of learning with these robots?
Yes.
And that's for both for locomotion,
for manipulation, and for perception?
Yes.
Well, what do you think in general
about all the exciting advancements
of transformer neural networks,
most beautifully illustrated
through the large language models like GPT-4?
Like everybody else, 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.
Disinformation is a curse that's an unintended consequence
of social media that could be exacerbated with these tools.
So if you use them to deploy disinformation,
it could be a real risk.
But I also think that the risks associated
with these kinds of models don't have a whole lot to do
with the way we're gonna use them in our robots.
If I'm using a robot, I'm building a robot
to do a manual task of some sort.
I can judge very easily.
Is it doing the task I asked it to?
Is it doing it correctly?
There's sort of a built-in mechanism for judging.
Is it doing the right thing?
Did it successfully do the task?
Yeah, physical reality is a good verifier.
It's a good verifier.
That's exactly it.
Whereas if you're asking for, yeah, I don't know,
trying to ask a theoretical question in chat GPT,
it could be true or it may not be true.
And it's hard to have that verifier.
What is that truth that you're comparing against?
Whereas in physical reality, you know the truth.
And this is an important difference.
So I'm not, I think there is reason to be
a little bit concerned about how these tools,
large language models could be used,
but I'm not very worried about how they're gonna be used.
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.
There's no, I would say, negative consequences to that,
but just increasing the bandwidth and the variety of ways
you can communicate with this particular robot.
So you could communicate visually,
you can communicate through some interface,
and to be able to communicate verbally,
again with the beer and so on.
I think that's really exciting
to make that much, much easier.
We have this partner, Levitas,
that's adding the vision algorithms
for daydreaming for us.
They just, just this week I saw a demo
where they hooked up a language tool to Spot
and they're talking to Spot to give it a chance.
Can you tell me about the Boston Dynamics AI Institute?
What is it and what is its mission?
So it's a separate organization,
the Boston Dynamics Artificial Intelligence Institute.
It's led by Mark Raybird, the founder of Boston Dynamics
and the former CEO, and my old advisor at MIT.
Mark has always loved the research, the pure research,
without the confinement or demands of commercialization.
And he wanted to continue to, 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 kind of 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 Mark wants to have the freedom
to pursue really hard, over-the-horizon problems.
And that'll be the goal of the institute.
So we mentioned some of the dangers of,
some of the concerns about large language models.
That said, you know, there's been a long-running fear
of these embodied robots.
Why do you think people are afraid of legged robots?
Yeah, I wanted to show you this.
So this is in the Wall Street Journal,
and this is all about chat GPT, right?
But look at the picture.
It's a humanoid robot.
That's saying I will replace you.
That looks scary, and it says I'm gonna replace you.
And so the humanoid robot is sort of,
is the embodiment of this chat GPT tool
that there's reason to be a little bit nervous
about how it gets deployed.
So I'm nervous about that connection.
It's unfortunate that they chose to use a robot
as that embodiment for, as you and I just said,
there's big differences in this.
But people are afraid because we've been taught
to be afraid for over 100 years.
So you know, the word robot was developed
by a playwright named Carol Chapek in 1921,
the Czech playwright, Rossum's Universal Robots.
And in that first depiction of a robot,
the robots took over 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, but I, and I think that's as much
why people are afraid as anything else,
is 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 a robot, Spot, is not gonna be pretend,
it could pretend it's human all at once.
It could tell you, you could put your LGBT 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, like if it,
I mean, I'm sure you can start, just like a dog,
lies to you, like, I wasn't part of tearing up that couch.
Spot can try to lie that like, you know,
it wasn't me that spilled that thing,
but you're going to kind of figure it out eventually
if it happens multiple times, you know?
But I think that.
Humanity has figured out how to make machines safe.
And there's, you know, 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 in 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 just, I took a ride.
I was in San Francisco, I took a ride
in the Waymo vehicle, it's an autonomous vehicle.
And I've done it several times.
They're doing incredible work over there.
But people flicked it off.
Not the car.
So, I mean, that's a long story
of what the psychology of that is.
It could be maybe big tech or what.
I don't know exactly what they're flicking off,
but there is an element of like these robots
are taking our jobs or irreversibly transforming society
such that it will have economic impact
and the little guy will lose a lot,
would lose their wellbeing.
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 an automation anxiety,
that it's gonna have a broader impact than we expected.
And there will be, 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 lot of people who are doing that job today
that it could be impacted.
But I think the reality is, as I said before,
we're gonna build the technologies
so that those very same people can operate it.
And so I think there's a pathway to upskilling
and operating just like, look, we used to farm
with hand tools and now we farm with machines
and nobody has really regretted that transformation.
And I think the same can be said for a lot of manual labor
that we're doing today.
And on top of that, you know, look,
we're entering a new world where demographics
are gonna have strong impact on economic growth.
And 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.
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 can be easily replaced
by GPT, that's going to have...
Well, and a lot, you know, anyone who deals with texts
and writing a draft proposal might be easily done
with a chat GPT now, where it wasn't before.
Consultants, journalists, 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.
That's another conversation altogether.
But yes.
More right than the average, the mean journalist, yes.
You spearheaded the NT weaponization letter
Boston Dynamics has.
Can you describe what that letter states
and the general topic of the use of robots in war?
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 Animall in Europe and, you know, some others
to co-sign a letter that said we won't put weapons
on our robots.
And part of the motivation there is, you know,
as these robots start to become commercially available,
you can see videos online of people who've gotten a robot
and strapped a gun on it and shown that they can,
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.
And so we are, in fact, I have a policy director
at my company whose job it is to engage with the public,
to engage with interested parties and including regulators
to sort of begin these discussions.
Yes, and a really important topic
and it's an important topic for people that worry
about the impact of robots on our society
with autonomous weapon systems.
So I'm glad you're sort of leading the way in this.
You are the CEO of Boston Dynamics.
What's it take to be a CEO of a robotics company?
So you started as a humble engineer, PhD.
Just looking at your journey,
what does it take to go from being,
from building the thing to leading a company?
What are some of the big challenges for you?
Courage, I would put front and center for multiple reasons.
I talked earlier about the courage to tackle hard problems.
So I think there's courage required not just of me
but of all of the people who work at Boston Dynamics.
I also think we have a lot of really smart people.
We have people who are way smarter than I am.
And it takes a kind of courage to be willing to lead them
and to trust that you have something to offer
to somebody who probably is maybe a better engineer
than I am.
Adaptability, part of it, it's been a great career for me.
I never would have guessed I'd stayed
in one place for 30 years.
And the job has always changed.
I didn't really aspire to be CEO from the very beginning,
but it was the natural progression of things.
There always needed to be some level of management
that was needed.
And so when I saw something that needed to be done
that wasn't being done, I just stepped in to go do it.
And oftentimes, because we were full
of such strong engineers, oftentimes that was
in the management direction
or it was in the business development direction
or organizational hiring.
Geez, I was the main person hiring at Boston Dynamics
for probably 20 years, so I was the head of HR basically.
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 an engineering team
that's doing some of the hardest work in the world?
We developed an interview process that I was quite fond of.
It's a little bit of a hard interview process
because the best interviews, you ask somebody
about what they're interested in and what they're good at.
And if they can describe to you something
that they worked on and you saw they really did the work,
they solved the problems, and you saw their passion for it,
and you could ask, but what makes that hard
is you have to ask a probing question about it.
You have to be smart enough about what they're telling you,
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.
At Google, they taught us about their interview process,
and it was a little bit different.
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.
They're hiring tens of thousands of people.
They need a more standardized process.
So they would err on the other side.
Or they would ask you a standard question.
I'm gonna ask you a programming question,
and I'm just gonna ask you to write code in front of me.
That's a terrifying application process.
It does let you compare candidates really well,
but it doesn't necessarily let you tap in to who they are,
right, because you're asking them to answer your question
instead of you asking them about what they're interested in.
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 the technical people.
But 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 for the world.
Did they really do it?
Did they find something interesting or curious?
And do they care about it?
I think somebody admires Jim Keller,
and he likes details.
So one of the ways,
if you get a person to talk about what they're interested in
how many details,
like how much of the whiteboard can you fill out?
Yeah, well, I think you figure out
did they really do the work
if they know some of the details?
Yes.
And if they have to wash over the details,
well, then they didn't do it.
Especially with engineering,
the work is in the details.
I have to go there briefly
just to get your kind of thoughts
in 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 a 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,
and I don't spend a lot of time thinking about this, right?
And I do think it's fairly remote
for the machines that we're dealing with.
Our robots, you're right, the people anthropomorphize.
They read into the robot's intelligence and emotion
that isn't there because they see physical gestures
that are similar to things they might even see
in people or animals.
I don't know much about how these large language models
really work.
I believe it's a kind of statistical averaging
of the most common responses 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 world wide web.
If you know it to be true,
it may only be there once, but by God, it's true.
And I think emotions are a little bit like that too.
You know something,
and I just think that's a different kind of knowledge
than the way these large language models
derive sort of simulated intelligence.
It does seem that the things that are true
very well might be statistically well represented
on the internet because the internet is made up of humans.
So I tend to suspect that large language models
are going to be able to simulate consciousness
very effectively.
And I actually believe that current GPT-4,
when fine tuned correctly, would be able to do just that.
And that's going to be a lot of very complicated
ethical questions that have to be dealt with,
that have nothing to do with robotics
and everything to do with.
There needs to be some process of labeling, I think,
what is true because there is also disinformation
available on the web and these models are going to consider
that kind of information as well.
And again, you can't average something that's true
and something that's untrue and get something
that's moderately true.
It's either right or it's wrong.
And so how is that process,
and this is obviously something that the purveyors
of these, Bard and Chad GPT,
I'm sure this is what they're working on.
Well, if you interact on some controversial topics
with these models, they're actually refreshingly nuanced.
They present, because you realize there's no one truth.
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.
Here's the, it presents the different hypotheses.
It presents calmly the amount of evidence for each one.
It's very, it's really refreshing.
It makes you realize that truth is nuanced
and it does that well.
And I think with consciousness,
it would very accurately say,
well, it sure as hell feels like I'm one of you humans,
but where's my body?
I don't understand.
You're going to be confused.
The cool thing about GPT is it seems to be easily confused
in the way we are.
You wake up in a new room and you ask, where am I?
It seems to be able to do that extremely well.
It'll tell you one thing,
like a fact about when a war started.
And when you correct it, say, well, this isn't,
that's not consistent.
It'll be confused.
It'd be, yeah, you're right.
It'll have that same element,
childlike element with humility
of trying to figure out its way in the world.
And I think that's a really tricky area
to sort of figure out with us humans of what we want
to allow AI systems to say to us,
because then if there's elements of sentience
that are being on display,
you can then start to manipulate human emotion,
all that kind of stuff.
But I think that's something,
that's a really serious and aggressive discussion
that needs to be had on the software side.
I think, again, embodiment robotics are actually saving us
from the arbitrary scaling of software systems
versus creating more problems.
But that said, I really believe in that connection
between human and robot.
There's magic there.
And I think there's also, I think,
a lot of money to be made there.
And Boston Dynamics is leading the world
in the most elegant movement done by robots.
So I can't wait to what maybe other people
that built on top of Boston Dynamics robots
or Boston Dynamics by itself.
So you had a one wild career,
one place and one set of problems,
but incredibly successful.
Can you give advice to young folks today in high school,
maybe in college, looking out to this future
where so much robotics and AI seems to be defining
the trajectory of human civilization?
Can you give them advice on how to have a career
they can be proud of or how to have a life
they can be proud of?
Well, I would say follow your heart and your interest.
Again, this was an organizing principle, I think,
behind the Leg Lab at MIT that turned into a value
at Boston Dynamics, which was follow your curiosity,
love what you're doing.
You'll have a lot more fun
and you'll be a lot better at it as a result.
I think it's hard to plan.
Don't get too hung up on planning too far ahead.
Find things that you like doing
and then see where it takes you.
You can always change direction.
You will find things that, wow, that wasn't a good move.
I'm gonna back up and go do something else.
So when people are trying to plan a career,
I always feel like, yeah, there's a few happy mistakes
that happen along the way and just live with that.
But make choices then.
So avail yourselves to these interesting opportunities
like when I happen to run into Mark down in the lab,
the basement of the AI lab,
but be willing to make a decision and then pivot
if you see something exciting to go at.
Because if you're out and about enough,
you'll find things like that that get you excited.
So there was a feeling when you first met Mark
and saw the robots that there's something interesting.
Oh boy, I gotta go do this.
There is no doubt.
What do you think in 100 years,
what do you think Boston Dynamics is doing?
What do you think is the role, even bigger,
what do you think is the role of robots in society?
Do you think we'll be seeing
billions of robots everywhere?
Do you think about that long-term vision?
Well, I do think that,
I think that robots will be ubiquitous
and they will be out amongst us.
And they'll be certainly doing
some of the hard labor that we do today.
I don't think people don't 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
because I'm not sure if people would know
what to do with themselves.
And I think just self-satisfaction and feeling productive
is such an ingrained part of being human
that we need to keep doing this work.
So we're definitely gonna have to
work in a complementary fashion.
And I hope that the robots and the computers
don't end up being able to do all the creative work, right?
Because that's the part that's, 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?
Or that adrenaline rush that you never forget.
And so people need to be able to do that creative work
and just feel productive.
And sometimes you can feel productive
over fairly simple work, but it's just well done, you know?
And that you can see the result of.
So I, you know, there was a, 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,
kind of sliding around on the deck of the movie
because they didn't do anything anymore.
Well, we definitely don't want to be there, you know?
We need to work in some complementary fashion
where we keep all of our faculties and our physical health
and we're doing some labor, right,
but in a complementary fashion somehow.
And I think a lot of that has to do with the interaction,
the collaboration with robots and with 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?
You'd say, just do it.
It's a machine and I don't have to have qualms about that.
The ones that are machines, I also hope to see a future
and it is hope, I do have optimism about that future
where some of the robots are pets,
have an emotional connection to us humans.
And because one of the problems that humans have to solve
is kind of a general loneliness.
The more love you have in your life,
the more friends you have in your life.
I think that makes a more enriching life, helps you grow.
And I don't fundamentally see why some of those friends
can't be robots.
There's an interesting long running study,
maybe it's in Harvard, they just, nice report,
article written about it recently.
They've been studying this group of a few thousand people now
for 70 or 80 years.
And the conclusion is that companionship and friendship
are the things that make for a better and happier life.
And so I agree with you.
And I think that could happen with a machine
that is probably, 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 and they could,
I guess it remains to be seen whether they can establish
that real deep, when you sit with a friend
and they remember something about you and bring that up
and you feel that connection, it remains to be seen
if a machine's gonna be able to do that for you.
Well, I have to say, it's inklings of that
already started happening for me,
some of my best friends are robots.
And I have you to thank for leading the way
in the accessibility and the ease of use of such robots
and the elegance of their movement.
Robert, you're an incredible person.
Boston Dynamics is an incredible company.
I've just been a fan for many, many years,
for everything you stand for,
for everything you do in the world.
If you're interested in great engineering, robotics,
go join them, build cool stuff.
I'll forever celebrate the work you're doing.
And it's just a big honor that you sit with me today
and talk, it means a lot, so thank you so much.
You're doing great work.
Thank you, Lex.
I'm honored to be here and I appreciate it, it was fun.
Thanks for listening to this conversation
with Robert Plater.
To support this podcast, please check out our sponsors
in the description.
And now, let me leave you with some words
from Alan Turing in 1950,
defining what is now termed the Turing Test.
A computer would deserve to be called intelligent
if it could deceive a human into believing
that it was human.
Thank you for listening and hope to see you next time.