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SPEAKER_00
3D.
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SPEAKER_00
In fact, when I started, it was all just about the energy of bouncing.
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SPEAKER_00
I was going to have a springy thing in the leg and some actuator so that you could get an energy regime going of bouncing.
1
SPEAKER_00
The idea that balance was an important part of it didn t come until a little later.
1
SPEAKER_00
And then I made the one-legged pogo stick robots.
1
SPEAKER_00
Now I think that we need to do that in manipulation.
1
SPEAKER_00
If you look at robot manipulation, we, a community, has been working on it for 50 years.
1
SPEAKER_00
we re nowhere near human levels of manipulation.
1
SPEAKER_00
I mean, we can, you know, it s come along, but I think it s all too safe, and I think trying to break out of that safety thing of static grasping, you know, if you look at the, a lot of work that goes on, it s about the geometry of the part, and then you figure out how to move your hand so that you can position it with respect to that, and then you grasp it carefully, and then you move it.
1
SPEAKER_00
Well, that s not anything like how people and animals work, you know?
1
SPEAKER_00
We juggle in our hands, we hug multiple objects and can sort them.
1
SPEAKER_00
So now, to be fair, being more aggressive is gonna mean things aren t gonna work very well for a while.
1
SPEAKER_00
So it s a longer term approach to the problem.
1
SPEAKER_00
And that s just theory now.
1
SPEAKER_00
Maybe that won t pay off, but that s sort of how I m trying to think about it, trying to encourage our group to go at it.
1
SPEAKER_01
Well, yeah, I mean, we ll talk about what it means to, what is the actual thing we re trying to optimize for a robot.
1
SPEAKER_01
Sometimes, especially human-robot interaction, maybe flaws is a good thing.
1
SPEAKER_01
Perfection is not necessarily the right thing to be chasing.
1
SPEAKER_01
Just like you said, maybe being good at fumbling an object,
1
SPEAKER_01
being good at fumbling might be the right thing to optimize versus perfect modeling of the object and perfect movement of the arm to grasp that object, because maybe perfection is not supposed to exist in the real world.
1
SPEAKER_00
I don t know if you know my friend Matt Mason, who is the director of the Robotics Institute at Carnegie Mellon, and we go back to graduate school together, but he analyzed
1
SPEAKER_00
a movie of Julia Childs doing a cooking thing.
1
SPEAKER_00
I think he said something like there were 40 different ways that she handled a thing, and none of them was grasping.
1
SPEAKER_00
She would nudge, roll, flatten with her knife, things like that, and none of them was grasping.
1
SPEAKER_01
So, okay, let s go back to the early days.
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SPEAKER_01
First of all, you ve created and led the Leg Lab, the legendary Leg Lab at MIT.
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SPEAKER_01
So what was that first hopping robot?
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SPEAKER_01
Can you?
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SPEAKER_00
But first of all, the Leg Lab actually started at Carnegie Mellon.
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SPEAKER_00
So I was a professor there starting in 1980, about 1986.
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SPEAKER_00
That s where the first hopping machines were built.
1
SPEAKER_00
I guess we got the first one working in about 1982, something like that.
1
SPEAKER_00
That was a simplified one.
1
SPEAKER_00
Then we got a three-dimensional one in 1983.
1
SPEAKER_00
The quadruped that we built at the Leg Lab, the first version, was built in about 1984 or 1985 and really only got going about 1986 or so.
1
SPEAKER_00
It took years of development to get it to
1
SPEAKER_01
Let s just pause here.
1
SPEAKER_01
For people who don t know, I m talking to Mark Waber, founder of Boston Dynamics, but before that, you were a professor developing some of the most incredible robots for 15 years, and before that, of course, a grad student and all that.
1
SPEAKER_01
So you ve been doing this for a really long time.
1
SPEAKER_01
So you skipped over this, but go to the first hopping robot.
1
SPEAKER_01
There s videos of some of this.
1
SPEAKER_01
I mean, these are incredible robots.
1
SPEAKER_01
So you talked about the very first step was to get a thing hopping up and down.
1
SPEAKER_01
And then you realized, well, balancing is a thing you should care about and it s actually a solvable problem.
1
SPEAKER_01
So can you just go through how to create that robot?
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SPEAKER_01
What was involved in creating that robot?
1
SPEAKER_00
Well, I m gonna start on the, not the technical side, but the, I guess we could call it the motivational side, or the funding side.
1
SPEAKER_00
So, before Carnegie Mellon, I was actually at JPL, at the Jet Propulsion Lab, for three years.
1
SPEAKER_00
And while I was there, I connected up with Ivan Sutherland, who is sometimes regarded as the father of computer graphics, because of work he did both at MIT and then University of Utah, and Evans and Sutherland.
1
SPEAKER_00
Anyway, I got to know him, and at one point he said he encouraged me to do some kind of project at Caltech, even though I was at JPL.
1
SPEAKER_00
Those are kind of related institutions.
1
SPEAKER_00
So I thought about it and I made up a list of three possible projects.
1
SPEAKER_00
And I purposely made the top one and the bottom one really boring sounding.
1
SPEAKER_00
And in the middle, I put a pogo stick robot.
1
SPEAKER_00
And when he looked at it, Ivan is a brilliant guy, brilliant engineer, and a real cultivator of people.
1
SPEAKER_00
He looked at it and knew right away what the thing that was worth doing.
1
SPEAKER_00
And so he had an endowed chair, so he had about $3,000 that he gave me to build the first model.
1
SPEAKER_00
which I went to the shop and with my own hands made a first model, which didn t work and was just a beginning shot at it.
1
SPEAKER_00
Ivan and I took that to Washington.
1
SPEAKER_00
In those days, you could just walk into DARPA and walk down the hallway and see who s there.
1
SPEAKER_00
Ivan, who had been there in his previous life,
1
SPEAKER_00
And so we walked around and we looked in the offices.
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SPEAKER_00
Of course, I didn t know anything.
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SPEAKER_00
I was basically a kid, but I didn t know this way around.
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SPEAKER_00
And we found Craig Fields in his office.
1
SPEAKER_00
Craig later became the director of DARPA, but in those days he was a program manager.
1
SPEAKER_00
And so we went in.
1
SPEAKER_00
I had a little Samsonite suitcase which we opened and it had just the skeleton of this one-legged hopping robot and we showed it to him.
1
SPEAKER_00
And you could almost see the drool going down his chin of excitement.
1
SPEAKER_00
And he sent me $250,000.
1
SPEAKER_00
He said, okay, I wanna fund this.
1
SPEAKER_00
And I was between institutions.
1
SPEAKER_00
I was just about to leave JPL and I hadn t decided yet where I was going next.
1
SPEAKER_00
And then when I landed at CMU, he sent $250,000, which in 1980 was a lot of research money.
1
SPEAKER_01
Did you see the possibility of where this is going?
1
SPEAKER_01
Why this is an important problem?
1
SPEAKER_01
The balance, I mean, it has to do with legged locomotion.
1
SPEAKER_01
I mean, it has to do with all these problems that the human body solves when we re walking, for example.
1
SPEAKER_01
Like, all the fundamentals are there.
1
SPEAKER_00
Yeah, I mean, I think that was the motivation to try and get more at the fundamentals of how animals work.
1
SPEAKER_00
But the idea that it would result in machines that were anything like practical,
1
SPEAKER_00
like we re making now, that wasn t anywhere in my head, no.
1
SPEAKER_00
You know, as an academic, I was mostly just trying to do the next thing, you know, make some progress, impress my colleagues if I could.
1
SPEAKER_01
And have fun.
1
SPEAKER_01
And have fun.
1
SPEAKER_01
Pogo stick robot.
1
SPEAKER_01
Pogo stick robot.
1
SPEAKER_01
So what was, on the technical side, what are some of the challenges of getting up, getting to the point where we saw, like in the video, the pogo stick robot that s actually successfully hopping and then eventually doing flips and all this kind of stuff?
1
SPEAKER_00
Well, in the very early days, I needed some better engineering than I could do myself, and I hired Ben Brown.
1
SPEAKER_00
We each had our way of contributing to the design, and we came up with a thing that could start to work.
1
SPEAKER_00
I had some stupid ideas about how the actuation system should work, and we sorted that out.
1
SPEAKER_00
It wasn t that hard to make it balanced once you get the physical machine to be working well enough and have enough control over the degrees of freedom.
1
SPEAKER_00
We started out by having it floating on an inclined air table, and that only gave us six foot of travel.
1
SPEAKER_00
Once it started working, we switched to a thing that could run around the room on another device.
1
SPEAKER_00
It s hard to explain these without you seeing them, but you probably know what I m talking about, a planarizer.
1
SPEAKER_00
And then the next big step was to make it work in 3D, which that was really the scary part, with these simple things.
1
SPEAKER_00
You know, people had inverted pendulums at the time for years, and they could control them by driving a cart back and forth.
1
SPEAKER_00
But could you make it work in three dimensions while it s bouncing and all that?
1
SPEAKER_00
But it turned out, you know, not to be that hard to do, at least at the level of performance we achieved at the time.
1
SPEAKER_01
So, okay, you mentioned inverted pendulum, but like, can you explain how a hopping stick in 3D can control, can balance itself?
1
SPEAKER_01
What does the actuation look like?
1
SPEAKER_00
You know, the simple story is that there s three things going on.
1
SPEAKER_00
There s something making it bounce, and you know, we had a system that was estimating how high the robot was off the ground, and using that, you know,
1
SPEAKER_00
There s energy that can be in three places in a pogo stick.
1
SPEAKER_00
One is in the spring, one is in the altitude, and the other is in the velocity.
1
SPEAKER_00
And so when at the top of the hop, it s all in the height.
1
SPEAKER_00
And so you could just measure how high you re going and thereby have an idea of a lot about the cycle and you could decide whether to put more energy in or less.
1
SPEAKER_00
So that is one element.
1
SPEAKER_00
Then there s a part that you decide where to put the foot.
1
SPEAKER_00
And if you think when you re landing on the ground with respect to the center of mass.
1
SPEAKER_00
So if you think of a pole vaulter,
1
SPEAKER_00
The key thing the pole vaulter has to do is get its body to the right place when the pole gets stuck.
1
SPEAKER_00
If they re too far forward, they get thrown backwards.
1
SPEAKER_00
If they re too far back, they go over.
1
SPEAKER_00
What they need to do is get it so that they go mostly up to get over the thing.
1
SPEAKER_00
High jumpers is the same kind of thing.
1
SPEAKER_00
So there s a calculation about where to put the foot, and we did something relatively simple.
1
SPEAKER_00
And then there s a third part to keep the body at an attitude that s upright, because if it gets too far, you know, you could hop and just keep rotating around, but if it gets too far, then you run out of motion of the joints at the hips, so you have to do that.
1
SPEAKER_00
And we did that by applying a torque between the legs and the body every time the foot s on the ground.
1
SPEAKER_00
You only can do it while the foot s on the ground.
1
SPEAKER_00
In the air, you know,
1
SPEAKER_00
the physics don t work out.
1
SPEAKER_01
How far does it have to tilt before it s too late to be able to balance itself?
1
SPEAKER_01
Or it s impossible to balance itself, correct itself?
1
SPEAKER_00
Well, you re asking an interesting question because...
1
SPEAKER_00
In those days, we didn t actually optimize things.
1
SPEAKER_00
And they probably could have gone much further than we did and then had higher performance.
1
SPEAKER_00
And we just kind of got a sketch of a solution and worked on that.
1
SPEAKER_00
And then in years since, some people working for us, some people working for others, people came up with all kinds of equations for, or algorithms for how to do a better job, be able to go faster.
1
SPEAKER_00
One of my students worked on getting things to go faster, another one worked on
1
SPEAKER_00
climbing over obstacles, because when you re running, on the open ground, it s one thing.
1
SPEAKER_00
If you re running up a stair, you have to adjust where you are, otherwise things don t work out right.
1
SPEAKER_00
You land your foot on the edge of the step.
1
SPEAKER_00
So there s other degrees of freedom to control if you re getting to more realistic, practical situations.
1
SPEAKER_01
I think it s really interesting to ask about the early days, because believing in yourself, believing that there s something interesting here, and then you mentioned finding somebody else, Ben Brown.
1
SPEAKER_01
What s that like, finding other people with whom you can build this crazy idea and actually make it work?
1
SPEAKER_00
Probably the smartest thing I ever did, is to find the other people.
1
SPEAKER_00
I mean, when I look at it now, I look at Boston Dynamics and all the really excellent engineering there, people who really make stuff work.
1
SPEAKER_00
I m only the dreamer.
1
SPEAKER_01
So when you talk about pogo stick robot or legged robots, whether it s quadrupeds or humanoid robots, did people doubt that this is possible?
1
SPEAKER_01
Did you experience a lot of people around you kinda?
1
SPEAKER_00
I don t know if they doubted whether it was possible, but I think they thought it was a waste of time.
1
SPEAKER_01
oh, it s not even an interesting problem.
1
SPEAKER_00
I think for a lot of people, people who were, I think it s been both, though.
1
SPEAKER_00
Some people, I think, I felt like they were saying, oh, why are you wasting your time on this stupid problem?
1
SPEAKER_00
But then I ve been at many things where people have told me it s been an inspiration to go out and attack these harder things.
1
SPEAKER_00
And I think it has turned out,
1
SPEAKER_00
I think legged locomotion has turned out to be a useful thing.
1
SPEAKER_01
Did you ever have doubt about bringing Atlas to life, for example, or with Big Dog, just every step of the way, did you have doubt, like, this is too hard of a problem?
1
SPEAKER_00
I mean, at first, I wasn t an enthusiast for the humanoids, because, again, it goes back to saying, what s the functionality?
1
SPEAKER_00
And the form wasn t as important as the functionality.
1
SPEAKER_00
And also, there s an aspect to humanoid robots that s about,
1
SPEAKER_00
all about the cosmetics, where there isn t really other functionality, and that kind of is off-putting for me.
1
SPEAKER_00
As a roboticist, I think the functionality really matters.
1
SPEAKER_00
So probably that s why I avoided humanoid robots to start with.
1
SPEAKER_00
But I ll tell you, now after we started working on him, you could see that the connection and the impact with other people, whether they re lay people or even other technical people, there s a special thing that goes on.
1
SPEAKER_00
Even though most of the humanoid robots aren t that much like a person.
1
SPEAKER_01
But we anthropomorphize and we see the humanity.
1
SPEAKER_01
But also with Spot, you can see, not the humanity, but whatever we find compelling about social interactions there in Spot as well.
1
SPEAKER_00
I ll tell you, I go around giving talks and take Spot to a lot of them, and it s amazing.
1
SPEAKER_00
The media likes to say that they re terrifying and that people are afraid.
1
SPEAKER_00
And YouTube commenters like to say that it s frightening.
1
SPEAKER_00
But when you take a spot out there, now maybe it s self-selecting, but you get a crowd of people who want to take pictures, want to pose for selfies, want to operate the robot, want to pet it, want to put clothes on it.
1
SPEAKER_00
It s amazing.
1
SPEAKER_01
Yeah, I love Spot.
1
SPEAKER_01
So if we move around history a little bit.
1
SPEAKER_01
So you said, I think, in the early days of Boston Dynamics that you quietly worked on making a running version of AIBO.
1
SPEAKER_01
Yeah.
1
SPEAKER_01
Sony s robot dog.
1
SPEAKER_00
Yeah.
1
SPEAKER_01
It s just an interesting little tidbit of history for me.
1
SPEAKER_01
What stands out to you in memory from that task?
1
SPEAKER_01
For people who don t know, that little dog robot moves slowly.
1
SPEAKER_01
How did that become Big Dog?
1
SPEAKER_01
What was involved there?
1
SPEAKER_01
What was the dance between how do we make this cute little dog versus a thing that can actually carry a lot of payload and move fast and stuff like that?
1
SPEAKER_00
What the connection was is that at that point, Boston Dynamics was mostly a physics-based simulation company.
1
SPEAKER_00
So when I left MIT to start Boston Dynamics, there was a few years of overlap, but the concept wasn t to start a robot company.
1
SPEAKER_00
The concept was to use this dynamic simulation tool that we developed to do robotics for other things.
1
SPEAKER_00
But working with Sony, we got back into robotics by doing the AIBO Runner.
1
SPEAKER_00
We made some tools for programming Curio, which was a humanoid this big that could do some dancing and other kinds of fun stuff.
1
SPEAKER_00
And I don t think it ever reached the market, even though they did show it.
1
SPEAKER_00
When I look back, I say that we got us back where we belonged.
1
SPEAKER_01
You rediscovered the soul of the company.
1
SPEAKER_00
That s right.
1
SPEAKER_01
And so from there, it was always about robots.
1
SPEAKER_00
Yep.
1
SPEAKER_01
So you started Boston Dynamics in 1992.
1
SPEAKER_01
Right.
1
SPEAKER_01
What are some fond memories from the early days?
1
SPEAKER_00
One of the robots that we built wasn t actually a robot, it was a surgical simulator, but it had force feedback, so it had all the techniques of robotics.
1
SPEAKER_00
And you look down into this
1
SPEAKER_00
mirror it actually was.
1
SPEAKER_00
And it looked like you were looking down onto the body you were working on.
1
SPEAKER_00
Your hands were underneath the mirror, so they were where you were looking.
1
SPEAKER_00
And you had tools in your hands that were connected up to these force feedback devices made by another MIT spin-out, Sensible Technologies.
1
SPEAKER_00
So they made the force feedback device, we attached the tools, and we wrote all the software, and did all the graphics.
1
SPEAKER_00
So we had 3D computer graphics, it was in the old days, this was in the late 90s, when you had
2
SPEAKER_01
a silicon graphics computer that was about this big.
2
SPEAKER_01
It was the heater in the office, basically.
2
SPEAKER_01
And we were doing surgical operations, anastomosis, which was stitching tubes together, tubes like blood vessels or other things in their body.
2
SPEAKER_01
And you could feel, and you could see the tissues move, and it was really exciting.
2
SPEAKER_01
And the idea was to make a trainer to teach surgeons how to do stuff.
2
SPEAKER_01
We built a scoring system, because we interviewed surgeons that told us what you re supposed to do and what you re not supposed to do.
2
SPEAKER_01
You re not supposed to tear the tissue.
2
SPEAKER_01
You re not supposed to touch it in any place except for where you re trying to engage.
2
SPEAKER_01
There were a bunch of rules.
2
SPEAKER_01
We built this thing and took it to a surgical trade show.
2
SPEAKER_01
The surgeons were practically lined up.
2
SPEAKER_01
We kept a score and we posted their scores on a video game.
2
SPEAKER_01
Those guys are so competitive that they really loved doing it.
2
SPEAKER_01
They would come around and they d see someone s score was higher there, so they would come back
2
SPEAKER_01
But we figured out shortly after that we thought surgeons were gonna pay us to get trained on these things.
2
SPEAKER_01
And the surgeons thought we should pay them in order to, so they could teach us about the thing.
2
SPEAKER_01
And there was no money from the surgeons.
2
SPEAKER_01
And we looked at it and thought, well, maybe we could sell it to hospitals that would train their surgeons.
2
SPEAKER_01
And then we said, well, we re this, at the time we were probably a 12-person company or maybe 15 people, I don t remember.
2
SPEAKER_01
There s no way we could go after a marketing activity.
2
SPEAKER_01
The company was all bootstrapped in those years.
2
SPEAKER_01
We never had investors until Google bought us, which was after 20 years.
2
SPEAKER_01
So we didn t have any resources to go after hospitals.
2
SPEAKER_01
So at one day, Rob and I were looking at that, and we said, we d built another simulator for knee arthroscopy, and we said, this isn t gonna work, and we killed it, and we moved on, and that was really a milestone in the company, because we sort of understood who we were and what would work and what wouldn t, even though technically it was really a fascinating thing.
2
SPEAKER_00
What was that meeting like?
2
SPEAKER_00
Were you just sitting at a table, you know what,
2
SPEAKER_00
Probably.
2
SPEAKER_00
We re going to pivot completely.
2
SPEAKER_00
We re going to let go of this thing we put so much hard work into and then go back to the thing.
2
SPEAKER_01
It just always felt right once we did it, you know.
2
SPEAKER_00
Just look at each other and said, let s build robots.
2
SPEAKER_00
Yeah.
2
SPEAKER_00
What was the first robot?
2
SPEAKER_00
you built under the flag of Boston Dynamics, Big Dog?
2
SPEAKER_01
Well, there was the AIBO runner, but it wasn t even a whole robot.
2
SPEAKER_01
It was just legs that we took off the legs on AIBOs and attached legs we d made.
2
SPEAKER_01
We got that working and showed it to the Sony people.
2
SPEAKER_01
We worked pretty closely with Sony in those years.
2
SPEAKER_01
One of the interesting things is that it was before the internet and Zoom and anything like that.
2
SPEAKER_01
So we had six ISDN lines installed, and we would have a telecon every week that worked at very low frame rates, something like 10 hertz.
2
SPEAKER_01
English across the boundary with Japan was a challenge, trying to understand what each of us was saying and have meetings every week.
2
SPEAKER_01
for several years doing that.
2
SPEAKER_01
And it was a pleasure working with them.
2
SPEAKER_01
They were really supporters.
2
SPEAKER_01
They seemed to like us and what we were doing.
2
SPEAKER_01
That was the real transition from us being a simulation company into being a robotics company again.
2
SPEAKER_00
It was a quadruped?
2
SPEAKER_00
The legs were, four legs or two legs?
2
SPEAKER_01
Yeah, no, four legs, yeah.
2
SPEAKER_00
And what did you learn from that experience of building basically a fast-moving quadruped?
2
SPEAKER_01
Mostly we learned that something that small doesn t look very exciting when it s running.
2
SPEAKER_01
It s like it s scampering, and you had to watch a slow-mo for it to look like it was interesting.
2
SPEAKER_01
If you watch it fast, it was just like a, you know.
2
SPEAKER_01
One of my things was to show stuff in video, even from the very early days of the hopping machines.
2
SPEAKER_01
And so I was always focused on how s this gonna look through the viewfinder, and running AIBO didn t look so cool through the viewfinder.
2
SPEAKER_00
So, what came next in terms of, what was a big next milestone in terms of a robot you built?
2
SPEAKER_01
I mean, you gotta say that Big Dog was, you know, sort of put us on the map and got our heads really pulled together.
2
SPEAKER_01
We scaled up the company.
2
SPEAKER_01
Big Dog was the result of Alan Rudolph at DARPA starting a biodynamics program.
2
SPEAKER_01
And he put out a, you know, a request for proposals and
2
SPEAKER_01
I think there were 42 proposals written and three got funded.
2
SPEAKER_01
One was Big Dog, one was a climbing robot, Rise, and that put things in motion.
2
SPEAKER_01
We hired Martin Buehler.
2
SPEAKER_01
He was a professor in Montreal at McGill.
2
SPEAKER_01
He was incredibly important for getting Big Dog out of the lab and into the mud, which was a key step to really be willing to go out there and build it, break it, fix it, which is sort of one of our mottos at the company.
2
SPEAKER_00
So testing it in the real world.
2
SPEAKER_00
For people who don t know, Big Dog, maybe you can correct me, but it s a big quadruped, four-legged robot.
2
SPEAKER_00
It looks big, could probably carry a lot of weight.
2
SPEAKER_00
Not the most weight that Boston Dynamics have built, but a lot.
2
SPEAKER_01
Well, it s the first thing that worked.
2
SPEAKER_01
So let s see, if we go back to the leg lab, we built a quadruped that could do many of the things that Big Dog did, but it had a hydraulic pump sitting in the room with hoses connected to the robot.
2
SPEAKER_01
It had a VAX computer in the next room.
2
SPEAKER_01
It needed its own room because it was this giant thing with air conditioning, and it had this very complicated bus connected to the robot.
2
SPEAKER_01
The robot itself just had the actuators.
2
SPEAKER_01
It had gyroscopes for sensing and some other sensors, but all the power and computing was off board.
2
SPEAKER_01
Big Dog had all that stuff integrated on the platform.
2
SPEAKER_01
It had a gasoline engine for power, which was a very complicated thing to undertake.
2
SPEAKER_01
It had to convert the rotation of the engine into hydraulic power, which is how we actuated
2
SPEAKER_01
So there was a lot of learning just on the building the physical robot and the system integration for that.
2
SPEAKER_01
And then there was the controls of it.
2
SPEAKER_00
So for Big Dog, you brought it all together onto one platform.
2
SPEAKER_00
You could take it out in the woods.
2
SPEAKER_00
Yeah, and you did.
2
SPEAKER_01
We did.
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SPEAKER_01
We spent a lot of time down at the Marine Corps base in Quantico where there was a trail.
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called the Guadalcanal Trail.
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SPEAKER_01
And our milestone that DARPA had specified was that we could go on this one particular trail that involved a lot of challenge.
2
SPEAKER_01
And we spent a lot of time, our team spent a lot of time down there.
2
SPEAKER_01
Those were fun days.
2
SPEAKER_00
Hiking with a robot.
2
SPEAKER_00
So what did you learn about what it takes to balance a robot like that on a trail?
2
SPEAKER_00
on a hiking trail in the woods.
2
SPEAKER_00
Basically, forget the woods, just the real world.
2
SPEAKER_00
That s the big leap into testing in the real world.
2
SPEAKER_01
Yeah.
2
SPEAKER_01
As challenging as the woods were, working inside of a home or in an office is really harder.
2
SPEAKER_01
Because when you re in the woods, you can actually take any path up the hill.
2
SPEAKER_01
All you have to do is avoid the obstacles.
2
SPEAKER_01
There s no such thing as damaging the woods, at least to first order.
2
SPEAKER_01
Whereas if you re in a house, you can t leave scuff marks, you can t bang into the walls, the robots aren t very comfortable bumping into the walls, especially in the early days.
2
SPEAKER_01
So I think those were actually bigger challenges once we faced them.
2
SPEAKER_01
It was mostly getting the systems to work well enough together, the hardware systems to work, and the controls.
2
SPEAKER_01
In those days, we did have a human operator who did all the visual perception going up the Guadalcanal Trail.
2
SPEAKER_01
So there was an operator who was right there, who was very skilled at
2
SPEAKER_01
Even though the robot was balancing itself and placing its own feet, if the operator didn t do the right thing, it wouldn t go.
2
SPEAKER_01
But years later, we went back with one of the electric, the precursor to Spot, and we had advanced the controls and everything so much that an amateur, complete amateur, could operate the robot the first time, up and down and up and down, whereas it had taken us years to get there in the previous robot.
2
SPEAKER_00
So if you fast forward, Big Dog eventually became Spot.
2
SPEAKER_01
So Big Dog became LS3, which is the big load carrying one.
2
SPEAKER_00
Just a quick pause, it can carry 400 pounds.
2
SPEAKER_01
It was designed to carry 400, but we had it carrying about 1,000 pounds at one time.
2
SPEAKER_01
Of course you did, just to make sure.
2
SPEAKER_01
We had one carrying the other one, we had two of them, so we had one carrying the other one.
2
SPEAKER_01
There s a little clip of that, we should put that out somewhere, that s from like 20 years ago.
2
SPEAKER_00
Wow, wow, and it can go for very long distances, it can travel 20 miles.
2
SPEAKER_00
Yeah, gasoline.
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SPEAKER_00
Gasoline, yeah.
2
SPEAKER_00
And that eventually, okay, sorry, so LS3, how did that lead to spot?
2
SPEAKER_01
So BigDog and LS3 had engine power and hydraulic actuation.
2
SPEAKER_01
Then we made a robot that was
2
SPEAKER_01
Electric power, so there s a battery driving a motor, driving a pump, but still hydraulic actuation.
2
SPEAKER_01
Larry sort of asked us, could you make something that weighed 60 pounds that would not be so intimidating if you had it in a house where there were people?
2
SPEAKER_01
And that was the inspiration behind the spot, pretty much as it exists today.
2
SPEAKER_01
We did a prototype the same size that was the first all-electric,
2
SPEAKER_01
non-hydraulic robot.
2
SPEAKER_00
What was the conversation with Larry Page like about, so here s a guy that kind of is very product focused and can see a vision for like what the future holds.
2
SPEAKER_00
That s just interesting kind of aside, what was the brainstorm about the future robotics with him like?
2
SPEAKER_01
I mean, it was almost as simple as what I just said.
2
SPEAKER_01
We were having a meeting.
2
SPEAKER_01
He said, yeah, do you think you could make a smaller one that wouldn t be so intimidating, like a big dog, if it was in your house?
2
SPEAKER_01
And I said, yeah, we could do that.
2
SPEAKER_01
And we started and did.
2
SPEAKER_00
Is there a lot of technical challenges to go from hydraulic to electric?
2
SPEAKER_01
You know, I had been in love with hydraulics and still love hydraulics.
2
SPEAKER_01
It s a great technology.
2
SPEAKER_01
It s too bad that somehow the world out there looks at it like it s old-fashioned or that it s icky.
2
SPEAKER_01
And it s true that you do, it is very hard to keep it from having some amount of dripping from time to time.
2
SPEAKER_01
But if you look at the performance, how strong you can get in a lightweight package, and of course we did a huge amount of innovation.
2
SPEAKER_01
Most of hydraulic control, that is the valve that controls the flow of oil, had been designed in the 50s for airplanes.
2
SPEAKER_01
It had been made robust enough, safe enough, that you could count on it so that humans could fly in airplanes.
2
SPEAKER_01
And very little innovation had happened.
2
SPEAKER_01
You know, that might not be fair to the people who make the valves.
2
SPEAKER_01
I m sure that they did innovate.
2
SPEAKER_01
But the basic design had stayed the same, and there was so much more you could do.
2
SPEAKER_01
And so our engineers designed valves, the ones that are in Atlas, for instance, that had new kinds of circuits.
2
SPEAKER_01
They sort of did some of the computing that could get you much more efficient use.
2
SPEAKER_01
They were much smaller and lighter, so that the whole robot could be smaller and lighter.
2
SPEAKER_01
We made a hydraulic power supply that had a bunch of components integrated in this tiny package.
2
SPEAKER_01
It s about this big, the size of a football.
2
SPEAKER_01
It weighs five kilograms and it produces five kilowatts of power.
2
SPEAKER_01
Of course, it has to have a battery operating, but it s got a motor, a pump, filters, heat exchanger to keep it cool, some valves, all in this tiny little package.
2
SPEAKER_01
So hydraulics,
2
SPEAKER_01
you know, could still have a ways to go.
2
SPEAKER_00
One of the things that stands out about the robots Boston Dynamics have created is how beautiful the movement is, how natural the walking is and running is, even flipping is, throwing is, so maybe you can talk about what s involved in making it look natural.
2
SPEAKER_01
Well, I think having good hardware is part of the story, and people who think you don t need to innovate hardware anymore are wrong, in my opinion.
2
SPEAKER_01
So I think one of the things certainly in the early years for me, taking a dynamic approach where you think about what s the evolution of the motion of the thing gonna be,
2
SPEAKER_01
in the future and having a prediction of that that s used at the time that you re giving signals to it.
2
SPEAKER_01
As opposed to it all being servoing, which is, servoing is sort of backward looking.
2
SPEAKER_01
It says, okay, where am I now?
2
SPEAKER_01
I m gonna try and adjust for that.
2
SPEAKER_01
But you really need to think about what s coming.
2
SPEAKER_00
So how far ahead do you have to look in time?
2
SPEAKER_01
It s interesting.
2
SPEAKER_01
I think that the number is only a couple of seconds for a spot.
2
SPEAKER_01
So there s a limited horizon type approach where you re recalculating, assuming what s gonna happen in the next.
2
SPEAKER_01
a second or a second and a half.
2
SPEAKER_01
And then you keep iterating, you know, at the next, even though a tenth of a second later, you ll say, okay, let s do that again and see what s happening.
2
SPEAKER_01
And you re looking at what the obstacles are, where the feet are gonna be placed, how to, you know, you have to coordinate a lot of things if you have obstacles and you re balancing at the same time.
2
SPEAKER_01
And it s that limited horizon type calculation that s doing a lot of that.
2
SPEAKER_01
But if you re doing something like a somersault, you re looking out a lot further.
2
SPEAKER_01
If you want to stick the landing, you have to at the time of launch have momentum and rotation, all those things coordinated so that a landing is within reach.
2
SPEAKER_00
How hard is it to stick a landing?
2
SPEAKER_00
I mean, it s very much underactuated.
2
SPEAKER_00
Once you re in the air, you don t have as much control about anything.
2
SPEAKER_00
So how hard is it to get that to work?
2
SPEAKER_00
First of all, I did flips with a hopping robot.
2
SPEAKER_01
If you look at the first time we ever made a robot do a somersault, it was in a planar robot.
2
SPEAKER_01
It had a boom, so it was restricted to the surface of a sphere, we call that planar.
2
SPEAKER_01
So it could move fore and aft, it could go up and down, and it could rotate.
2
SPEAKER_01
And so the calculation of what you need to do to stick a landing isn t all that complicated.
2
SPEAKER_01
You have to get time to make the rotation, so how high you jump gives you time.
2
SPEAKER_01
you look at how quickly you can rotate.
2
SPEAKER_01
If you get those two right, then when you land, you have the feet in the right place.
2
SPEAKER_01
You have to get rid of all that rotational and linear momentum, but that s not too hard to figure out.
2
SPEAKER_01
Back in about 1985 or 86, I can t remember, we had a simple robot doing somersaults.
2
SPEAKER_01
To do it in 3D, really the calculation is the same, you just have to be balancing in the other degrees of freedom.
2
SPEAKER_01
If you re just doing a somersault, it s just a plainer thing.
2
SPEAKER_01
When Rob was my graduate student and we were at MIT, which is when we made a two-legged robot do a 3D somersault for the first time.
2
SPEAKER_01
There, in order to get enough rotation rate, you needed to do tucking also.
2
SPEAKER_01
Withdraw the legs in order to accelerate it.
2
SPEAKER_01
He did some really fascinating work on how you stabilize more complicated maneuvers.
2
SPEAKER_01
Remember, he was a champion gymnast before he d come to me.
2
SPEAKER_01
He had the physical abilities, and he was an engineer, so he could
2
SPEAKER_01
translate some of that into the math and the algorithms that you need to do that.
2
SPEAKER_00
He knew how humans do it, he just had to get robots to do the same.
2
SPEAKER_01
Unfortunately though, humans don t really know how they do it, right?
2
SPEAKER_01
We re coached, we have ways of learning, but do we really understand in a physics way what we re doing?
2
SPEAKER_01
Probably most gymnasts and athletes don t know.
2
SPEAKER_00
So in some way, by building robots, you are in part understanding how humans do, like walking.
2
SPEAKER_00
Most of us walk without considering how we walk, really.
2
SPEAKER_00
And how we make it so natural and efficient, all those kinds of things.
2
SPEAKER_01
Atlas still doesn t walk like a person, and it still doesn t walk quite as gracefully as a person, even though it s been getting closer and closer.
2
SPEAKER_01
The running might be close to a human, but the walking is still a challenge.
2
SPEAKER_00
That s interesting, right, that running is closer to a human.
2
SPEAKER_00
It just shows that the more aggressive and kind of, the more you leap into the unknown, the more natural it is.
2
SPEAKER_00
I mean, walking is kind of falling always, right?
3
SPEAKER_01
And something weird about the knee, that you can kind of do this folding and unfolding, and get it to work out just, a human can get it to work out just right, there s compliances.
3
SPEAKER_01
Compliance means springiness in the design that are important to how it works.
3
SPEAKER_01
Well, we used to have a motto at Boston Dynamics in the early days, which was, you have to run before you can walk.
3
SPEAKER_00
That s a good motto.
3
SPEAKER_00
You also had Wildcat, which was one of the, along the way towards Spot, which is a quadruped that went 19 miles an hour on flat terrain.
3
SPEAKER_00
Is that the fastest you ve ever built?
3
SPEAKER_00
Might be the fastest quadruped in the world, I don t know.
3
SPEAKER_01
for a quad pad probably.
3
SPEAKER_01
Of course it was probably the loudest too.
3
SPEAKER_01
So we had this little racing go-kart engine on it.
3
SPEAKER_01
And we would get people from three buildings away sending us, complaining about how loud it was.
3
SPEAKER_00
So at the leg lab, I believe most of the robots didn t have knees.
3
SPEAKER_00
How do you figure out what is the right number of actuators?
3
SPEAKER_00
What are the joints to have?
3
SPEAKER_00
What do you need to have?
3
SPEAKER_00
We humans have knees and all kinds of interesting stuff on the feet.
3
SPEAKER_00
The toe is an important part, I guess, for humans.
3
SPEAKER_00
Or maybe it s not.
3
SPEAKER_00
I injured my toe recently and it made running very unpleasant.
3
SPEAKER_00
So that seems to be kind of important.
3
SPEAKER_00
So how do you figure out for efficiency, for function, for aesthetics, how many joints to have, how many actuators to have?
3
SPEAKER_01
Well, it s always a balance between wanting to get where you really wanna get and what s practical to do based on your resources or what you know and all that.
3
SPEAKER_01
I mean, the whole idea of the pogo stick was to do simplification.
3
SPEAKER_01
Obviously, it didn t look like a human.
3
SPEAKER_01
I think a technical scientist could appreciate that we were capturing some of the things that are important in human locomotion without it looking
3
SPEAKER_01
like it without having a knee, an ankle.
3
SPEAKER_01
I ll tell you the first sketch that Ben Brown made when we were talking about building this thing was a very complicated thing with zillions of springs, lots of joints.
3
SPEAKER_01
It looked much more like a kangaroo or an ostrich or something like that, things we were paying a lot of attention to at the time.
3
SPEAKER_01
So my job was to say, okay, well, let s do something simpler to get started and maybe we ll get there at some point.
3
SPEAKER_00
I just love the idea that you two were studying kangaroos and ostriches.
3
SPEAKER_01
Oh, yeah.
3
SPEAKER_01
We filmed and digitized
3
SPEAKER_01
data from horses.
3
SPEAKER_01
I did a dissection of an ostrich at one point, which has absolutely remarkable legs.
3
SPEAKER_00
Dumb question.
3
SPEAKER_00
Do ostriches have a lot of musculature on the legs or no?
3
SPEAKER_01
Most of it s up in the feathers, but there s a huge amount going on in the feathers, including a knee joint.
3
SPEAKER_01
The knee joint s way up there.
3
SPEAKER_01
The thing that s halfway down the leg that looks like a backwards knee is actually the ankle.
3
SPEAKER_01
The thing on the ground which looks like the foot is actually the toes, it s an extended toe.
3
SPEAKER_01
But the basic morphology is the same in all these animals.
3
SPEAKER_00
What do you think is the most beautiful movement of an animal?
3
SPEAKER_00
Like what animal do you think is the coolest?
3
SPEAKER_00
Land animal, that s cool, because fish is pretty cool, like the way fish moves through water, but like legged locomotion.
3
SPEAKER_01
You know, the slow-mo s of cheetahs running are incredible.
3
SPEAKER_01
There s so much back motion and grace, and of course they re moving very fast.
3
SPEAKER_01
The animals running away from the cheetah are pretty exciting.
3
SPEAKER_01
The pronghorn, which they do this all four legs at once jump called a prong to kind of confuse the, especially if there s a group of them, to confuse whoever s chasing them.
3
SPEAKER_01
So they do like a misdirection type of thing?
3
SPEAKER_01
Yep, they do a misdirection thing.
3
SPEAKER_01
The front-on views of the cheetahs running fast, where the tail is whipping around to help in the turns, to help us stabilize in the turns, that s pretty exciting.
3
SPEAKER_00
Because they spend a lot of time in the air, I guess, as they re running that fast.
3
SPEAKER_01
But they also turn very fast.
3
SPEAKER_00
Is that a tail thing, or do you have to have contact with ground?
3
SPEAKER_01
Everything in the body is probably helping turn, because they re chasing something that s trying to get away that s also zigzagging around.
3
SPEAKER_01
But I would be remiss if I didn t say humans are pretty good too.
3
SPEAKER_01
You watch gymnasts, especially these days, they re doing just incredible stuff.
3
SPEAKER_00
Well, like especially like Olympic level gymnasts.
3
SPEAKER_00
See, but there could be, there could be cheetahs that are Olympic level.
3
SPEAKER_00
We might be watching the average cheetah versus like, there could be like a really special cheetah that can do like.
3
SPEAKER_00
You re right.
3
SPEAKER_00
When do the knees first come into play in you building legged robots?
3
SPEAKER_01
In Big Dog.
3
SPEAKER_01
Big Dog.
3
SPEAKER_01
Yeah, Big Dog came first and then Little Dog was later.
3
SPEAKER_01
And you know, there was a,
3
SPEAKER_01
There s a big compromise there.
3
SPEAKER_01
Human knees have multiple muscles, and you could argue that there s, I mean, it s a technical thing about negative work.
3
SPEAKER_01
When you re contracting a joint, but you re pushing out, that s negative work.
3
SPEAKER_01
And if you don t have a place to store that, it can be very expensive to do negative work.
3
SPEAKER_01
And in Big Dog, there was no place to store negative work in the knees.
3
SPEAKER_01
But Big Dog also had pogo stick springs down below, so part of the action was to comply in a bouncing motion.
3
SPEAKER_01
Later on in SPOT, we took that out.
3
SPEAKER_01
As we got further and further away from the leg lab, we had more energy-driven controls.
3
SPEAKER_00
Is there something to be said about knees that go forward versus backward?
3
SPEAKER_01
Sure.
3
SPEAKER_01
There s this idea called passive dynamics, which says that although you can use computers and actuators to make a motion, a mechanical system can make a motion just by itself if it gets stimulated the right way.
3
SPEAKER_01
So Tad McGeer, I think in the mid- 80s, maybe it was in the late- 80s, started to work on that.
3
SPEAKER_01
He made this legged system that could walk down an inclined plane where the legs folded and unfolded and swung forward, do the whole walking motion, where there was no computer.
3
SPEAKER_01
There were some adjustments to the mechanics so that there were dampers and springs in some places that helped the mechanical action happen.
3
SPEAKER_01
It was essentially a mechanical computer.
3
SPEAKER_01
The interesting idea there is that it s not all about the brain.
3
SPEAKER_01
dictating to the body what the body should do.
3
SPEAKER_01
The body is a participant in the motion.
3
SPEAKER_00
So a great design for a robot has a mechanical component where the movement is efficient even without a brain.
3
SPEAKER_00
Yes.
3
SPEAKER_00
How do you design that?
3
SPEAKER_01
I think that these days most robots aren t doing that.
3
SPEAKER_01
Most robots are
3
SPEAKER_01
are basically using the computer to govern the motion.
3
SPEAKER_01
Now, the brain, though, is taking into account what the mechanical thing can do and how it s gonna behave.
3
SPEAKER_01
Otherwise, it would have to really forcefully move everything around all the time, which probably some solutions do, but I think you end up with a more efficient and more graceful thing if you re taking into account what the machine wants to do.
3
SPEAKER_00
So this might be a good place to mention that you re now
3
SPEAKER_00
leading up the Boston Dynamics AI Institute, newly formed, which is focused more on designing the robots of the future.
3
SPEAKER_00
I think one of the things, maybe you can tell me the big vision for what s going on, but one of the things is this idea that hardware still matters with organic design and so on.
3
SPEAKER_00
Maybe before that, can you zoom out and tell me what the vision is for the AI Institute?
3
SPEAKER_01
You know, I like to talk about intelligence having two parts, an athletic part and a cognitive part.
3
SPEAKER_01
And I think, you know, Boston Dynamics, in my view, has sort of set the standard for what athletic intelligence can be.
3
SPEAKER_01
And you know, it has to do with all the things we ve been talking about, the mechanical design, the real-time control, the energetics, and that kind of stuff.
3
SPEAKER_01
But obviously, people have another kind of intelligence, and animals have another kind of intelligence.
3
SPEAKER_01
We can make a plan.
3
SPEAKER_01
Our meeting started at 9.30.
3
SPEAKER_01
I looked up on Google Maps how long it took to walk over here.
3
SPEAKER_01
It was 20 minutes.
3
SPEAKER_01
So I decided, okay, I d leave my house at nine, which is what I did.
3
SPEAKER_01
Simple intelligence, but we use that kind of stuff all the time.
3
SPEAKER_01
It s sort of what we think of as going on in our heads.
3
SPEAKER_01
And I think that s in short supply for robots.
3
SPEAKER_01
Most robots are pretty dumb.
3
SPEAKER_01
And as a result, it takes a lot of skilled people to program them to do everything they do.
3
SPEAKER_01
And it takes a long time.
3
SPEAKER_01
And if robots are gonna, you know, satisfy our dreams, they need to be smarter.
3
SPEAKER_01
So the AI Institute is designed to combine that physicality of the athletic side with the cognitive side.
3
SPEAKER_01
So for instance, we re trying to make robots that can watch a human do a task, understand what it s seeing, and then do the task itself.
3
SPEAKER_01
So sort of OJT for, on-the-job training for robots as a paradigm.
3
SPEAKER_01
Now, that s pretty hard.
3
SPEAKER_01
It s sort of science fiction, but our idea is to work on a longer timeframe and work on solving those kinds of problems.
3
SPEAKER_01
I have a whole list of things that are in that vein.
3
SPEAKER_00
Maybe we can just take many of the things you mentioned, just take it as a tangent.
3
SPEAKER_00
First of all, athletic intelligence is a super cool term.
3
SPEAKER_00
And that really is intelligence.
3
SPEAKER_00
We humans kind of take it for granted that we re so good at walking and moving about the world.
3
SPEAKER_01
And using our hands, you know.
3
SPEAKER_01
Using our hands.
3
SPEAKER_01
The mechanics of interacting with all, you know.
3
SPEAKER_01
these two things, you know.
3
SPEAKER_01
I m not, I m not looking.
3
SPEAKER_01
Well, I ve touched ones like this.
3
SPEAKER_01
Look at all the things I can do, right?
3
SPEAKER_01
I can juggle and I m rotating it this way.
3
SPEAKER_01
I can rotate it without looking.
3
SPEAKER_01
I could fetch these things out of my pocket and figure out which one was which and all that kind of stuff.
3
SPEAKER_01
And I don t think we have much of a clue how all that works yet.
3
SPEAKER_00
Right, and that s, I really like putting that under the banner of athletic intelligence.
3
SPEAKER_00
what are the big open problems in athletic intelligence?
3
SPEAKER_00
So, Boston Dynamics, with Spot, with Atlas, just have shown time and time again, like pushed the limits of what we think is possible with robots, but where do we stand, actually, if we kind of zoom out?
3
SPEAKER_00
What are the big open problems on the athletic intelligence side?
3
SPEAKER_01
I mean, one question you could ask that isn t my question, but, you know, are they commercially viable?
3
SPEAKER_01
Will they increase productivity?
3
SPEAKER_01
We re getting very close to that.
3
SPEAKER_01
I don t think we re quite there still.
3
SPEAKER_01
Most of the robotics companies, it s a struggle.
3
SPEAKER_01
It s really the lack of the cognitive side that probably is the biggest barrier at the moment, even for the physically successful robots.
3
SPEAKER_01
But your question s a good one.
3
SPEAKER_01
You can always do a thing that s more efficient, lighter, more reliable.
3
SPEAKER_01
I d say reliability.
3
SPEAKER_01
I know that Spot, they ve been working very hard
3
SPEAKER_01
on getting the tail of the reliability curve up.
3
SPEAKER_01
And they ve made huge progress.
3
SPEAKER_01
So the robots, there s 1,500 of them out there now.
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SPEAKER_01
Many of them being used in practical applications, day in and day out, where they have to work reliably.
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SPEAKER_01
And it s very exciting that they ve done that.
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SPEAKER_01
But it takes a huge effort to get that kind of reliability in the robot.
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SPEAKER_01
There s cost, too.
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SPEAKER_01
You d like to get the cost down.
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SPEAKER_01
Spots are still pretty expensive, and I don t think that they have to be, but it takes a different kind of activity to do that.
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SPEAKER_01
I think that Boston Dynamics is owned primarily by Hyundai now, and I think that the skills of Hyundai in making cars can be brought to bear in making robots that are less expensive and more reliable and those kinds of things.
3
SPEAKER_00
So on the cognitive side for the Eye Institute, what s the trade-off between moonshot projects for you and maybe incremental progress?
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SPEAKER_01
That s a good question.
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SPEAKER_01
I think we re using the paradigm called stepping stones to moonshots.
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SPEAKER_01
I don t believe, that was in my original proposal for the institute, stepping stones to moonshots.
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SPEAKER_01
I think if you go more than a year without seeing a tangible
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SPEAKER_01
status report of where you are, which is the stepping stone.
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SPEAKER_01
And it could be a simplification, right?
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SPEAKER_01
You don t necessarily have to solve all the problems of your target goal, even though your target goal is gonna take several years.
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SPEAKER_01
Those stepping stone results give you feedback, give motivation, because usually there s some success in there.
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SPEAKER_01
And so that s the mantra we ve been working on.
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SPEAKER_01
And that s pretty much how I d say Boston Dynamics has worked, where you make progress and show it as you go.
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SPEAKER_01
Show it to yourself, if not to the world.
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SPEAKER_00
What does success look like?
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SPEAKER_00
What are some of the milestones you re chasing?
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SPEAKER_01
Well, with Watch, Understand, Do, the project I mentioned before, we ve broken that down into getting some progress with what does meaningfully watching something mean, breaking down an observation of a person doing something into the components, segmenting.
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SPEAKER_01
You watch me do something, I m gonna pick up this thing and put it down here and stack this on it.
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SPEAKER_01
Well, it s not obvious if you just look at the raw data.
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SPEAKER_01
what the sequence of acts are.
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SPEAKER_01
It s really a creative, intelligent act for you to break that down into the pieces and understand them in a way so you could say, okay, what skill do I need to accomplish each of those things?
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SPEAKER_01
So we re working on the front end of that kind of a problem where we observe and translate the, it may be video, it may be live, into
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SPEAKER_01
a description of what we think is going on, and then trying to map that into skills to accomplish that, and we ve been developing skills as well.
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SPEAKER_01
So we have kind of multiple stabs at the pieces of doing that.
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SPEAKER_00
And this is usually video of humans manipulating objects with their hands kind of thing?
3
SPEAKER_01
Mm-hmm, we re starting out with bicycle repair, some simple bicycle repair task.
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SPEAKER_00
Oh no, that seems complicated.
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SPEAKER_00
That seems really complicated.
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SPEAKER_01
It is, but there s some parts of it that aren t.
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SPEAKER_01
like putting the seat in into the, you have a tube that goes inside of another tube and there s a latch.
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SPEAKER_01
That should be within range.
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SPEAKER_00
Is it possible to observe, to watch a video like this without having an explicit model of what a bicycle looks like?
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SPEAKER_01
I think it is.
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SPEAKER_01
And I think that s the kind of thing that people don t recognize.
3
SPEAKER_01
Let me translate it to navigation.
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SPEAKER_01
I think the basic paradigm for navigating a space is to get some kind of sensor that tells you where an obstacle is and what s open, build a map, and then go through the space.
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SPEAKER_01
But if we were doing on-the-job training where I was giving you a task, I wouldn t have to say anything about the room.
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SPEAKER_01
We came in here, all we did is adjust the chair, but we didn t say anything about the room and we could navigate it.
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SPEAKER_01
So I think there s opportunities to build that kind of navigation skill into robots.
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SPEAKER_01
and we re hoping to be able to do that.
3
SPEAKER_00
So operate successfully under a lot of uncertainty.
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SPEAKER_01
Yeah, and lack of specification.
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SPEAKER_00
Lack of specification.
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SPEAKER_01
I mean that s what sort of intelligence is, right?
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SPEAKER_01
Kind of dealing with, understanding a situation even though it wasn t explained.
3
SPEAKER_00
So how big of a role does machine learning play in all of this?
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SPEAKER_00
Is this more and more learning based?
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SPEAKER_01
You know,
3
SPEAKER_01
Since ChatGBT, which is a year ago, basically, there s a huge interest in that and a huge optimism about it.
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SPEAKER_01
And I think that there s a lot of things that machine learn, that kind of machine learning.
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SPEAKER_01
Now, of course, there s lots of different kinds of machine learning.
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SPEAKER_01
I think there s a lot of interest and optimism about it.
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SPEAKER_01
I think the facts on the ground are that doing physical things with physical robots is a little bit different
4
SPEAKER_01
and language, and the tokens sort of don t exist.
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SPEAKER_01
Pixel values aren t like words.
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SPEAKER_01
But I think that there s a lot that can be done there.
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SPEAKER_01
We have several people working on machine learning approaches.
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SPEAKER_01
I don t know if you know, but we opened an office in Zurich recently, and Marco Hutter, who s one of the real leaders in reinforcement learning for robots, is the director of that office.
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SPEAKER_01
He s still half-time at ETH.
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SPEAKER_01
the university there, where he has an unbelievably fantastic lab, and then he s half-time leading, will be leading off efforts in the Zurich office.
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SPEAKER_01
So we have a healthy learning component, but there s part of me that still says if you look out in the world at what the most impressive performances are,
4
SPEAKER_01
They re still pretty much, I hate to use the word traditional, but that s what everybody s calling it, traditional controls, like model predictive control.
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SPEAKER_01
You know, the thing, the Atlas performances that you ve seen are mostly model predictive control.
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SPEAKER_01
They ve started to do some learning stuff that s really incredible.
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SPEAKER_01
I don t know if it s all been shown yet, but you ll see it over time.
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SPEAKER_01
And then Marco s done some great stuff and others.
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SPEAKER_00
So especially for the athletic intelligent piece, the traditional approach seems to be the one that still performs the best.
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SPEAKER_01
I think we re gonna find a mating of the two, and we ll have the best of both worlds, and we re working on that at the institute too.
4
SPEAKER_00
If I can talk to you about teams, you built an incredible team at Boston Dynamics before at MIT and CMU, at Boston Dynamics, and now at the AI Institute, and you said that there s four components to a great team.
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SPEAKER_00
Technical, fearlessness, diligence, intrepidness, and fun, technical fun.
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SPEAKER_00
Can you explain each?
4
SPEAKER_00
Technical fearlessness, what do you mean by that?
4
SPEAKER_01
Sure, technical fearlessness means being willing to take on a problem that you don t know how to solve.
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SPEAKER_01
you know, study it, figure out an entry point, you know, maybe a simplified version or a simplified solution or something.
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SPEAKER_01
Learn from the stepping stone and go back and eventually make a solution that meets your goals.
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SPEAKER_01
And I think that s really important.
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SPEAKER_00
The fearlessness comes into play because some of it has never been done before.
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SPEAKER_01
Yeah, and you don t know how to do it.
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SPEAKER_01
There s easier stuff to do in life.
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SPEAKER_01
I mean, I don t know, watch, understand, do.
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SPEAKER_01
It s a mountain of a challenge.
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SPEAKER_00
So that s the really big challenge you re tackling now.
4
SPEAKER_00
Can we watch humans at scale and have robots, by watching humans, become effective actors in the world?
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SPEAKER_01
I mean, we have others like that.
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SPEAKER_01
We have one called Inspect, Diagnose, Fix.
4
SPEAKER_01
You call up the Maytag repairman.
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SPEAKER_01
Okay, he s the one who you don t have to call, but you call up the dishwasher repair person, and they come to your house, and they look at your machine.
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SPEAKER_01
It s already been actually figured out that something doesn t work, but they have to kind of examine it and figure out.
4
SPEAKER_01
what s wrong and then fix it.
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SPEAKER_01
I think robots should be able to do that.
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SPEAKER_01
Boston Dynamics already has spot robots.
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SPEAKER_01
collecting data on machines, things like thermal data, reading the gauges, listening to them, getting sounds, and that data are used to determine whether they re healthy or not, but the interpretation isn t done by the robots yet, and certainly the diagnosing and the fixing isn t done yet, but I think it could be, and that s bringing the AI and combining it with the physical skills to do it.
4
SPEAKER_00
Yeah, and you re referring to the fixing in the physical world.
4
SPEAKER_00
I can t wait until you can fix the psychological problems of humans and show up and just talk, do therapy.
4
SPEAKER_01
No, that s a different thing.
4
SPEAKER_00
Yeah, it s a different, well, it s all part of the same thing.
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SPEAKER_00
Again, humanity.
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SPEAKER_00
Maybe, maybe.
4
SPEAKER_01
You mean convincing you it s okay that the dishwasher s broken?
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SPEAKER_00
Just do the marketing approach?
4
SPEAKER_00
Yeah, exactly.
4
SPEAKER_00
It s all, yeah, don t sweat the small stuff.
4
SPEAKER_00
Yeah, as opposed to fixing the dishwasher, it ll convince you that it s okay that the dishwasher s broken.
4
SPEAKER_00
It s a different approach.
4
SPEAKER_00
Diligence, why is diligence important?
4
SPEAKER_01
Well, if you want a real robot solution, it can t be a very narrow solution that s gonna break at the first variation in what the robot does or the environment if it wasn t exactly as you expected it.
4
SPEAKER_01
How do you get there?
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SPEAKER_01
I think having an approach that leaves you unsatisfied until you ve embraced the bigger problem is the diligence I m talking about.
4
SPEAKER_01
Again, I ll point at Boss Dynamics.
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SPEAKER_01
I think they ve done some of the videos that we had showing the engineer making it hard for the robot to do its task.
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SPEAKER_01
spot opening a door, and then the guy gets there and pushes on the door so it doesn t open the way it s supposed to, pulling on the rope that s attached to the robot so its navigation has been screwed up.
4
SPEAKER_01
We have one where the robot s climbing stairs and an engineer is tugging on a rope that s pulling it back down the stairs.
4
SPEAKER_01
That s totally different than just the robot seeing the stairs, making a model, putting its feet carefully on each step.
4
SPEAKER_01
But that s what probably robotics needs to succeed, and having that broader
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SPEAKER_01
That broader idea that you wanna come up with a robust solution is what I meant by diligence.
4
SPEAKER_00
So really testing it in all conditions, perturbing the system in all kinds of ways.
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SPEAKER_00
Right.
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SPEAKER_00
And as a result, creating some epic videos.
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SPEAKER_00
The legendary.
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SPEAKER_01
The fun part, the hockey stick.
4
SPEAKER_00
And then yes, tugging on spot as it s trying to open the door.
4
SPEAKER_00
I mean, it s great testing, but it s also,
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SPEAKER_00
I don t know, it s just somehow extremely compelling demonstration of robotics in video form.
4
SPEAKER_01
I learned something very early on with the first three-dimensional hopping machine.
4
SPEAKER_01
If you just show a video of it hopping,
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SPEAKER_01
It s a so what.
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SPEAKER_01
If you show it falling over a couple of times and you can see how easily and fast it falls over, then you appreciate what the robot s doing when it s doing its thing.
4
SPEAKER_01
I think the reaction you just gave to the robot getting interfered with or tested while it s going through the door, it s showing you the scope of the solution.
4
SPEAKER_00
the limits of the system, the challenges involved, and failure.
4
SPEAKER_00
Showing both failure and success makes you appreciate the success, yeah.
4
SPEAKER_00
And then just the way the videos are done in Boston Dynamics are incredible, because there s no flash, there s no extra production, it s just raw testing of the robot.
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SPEAKER_01
Well, I was the final edit for most of the videos up until
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SPEAKER_01
until about three years ago or four years ago.
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SPEAKER_01
And my theory of the video is no explanation.
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SPEAKER_01
If they can t see it, then it s not the right thing.
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SPEAKER_01
And if you do something worth showing, then let them see it.
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SPEAKER_01
Don t interfere with a bunch of titles that slow you down or a bunch of distraction.
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SPEAKER_01
Just do something worth showing and then show it.
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SPEAKER_01
That s brilliant.
4
SPEAKER_01
It s hard though for people to buy into that.
4
SPEAKER_00
Yeah, I mean, people always wanna add more stuff, but the simplicity of just do something worth showing and show it, that s brilliant.
4
SPEAKER_00
And don t add extra stuff.
4
SPEAKER_01
People have criticized, especially the Big Dog videos where there s a human driving the robot.
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SPEAKER_01
And I understand the criticism now.
4
SPEAKER_01
At the time, we wanted to just show, look, this thing s using its legs to get up the hill, so we focused on showing that, which was, we thought, the story.
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SPEAKER_01
The fact that there was a human, so they were thinking about autonomy, whereas we were thinking about the mobility.
4
SPEAKER_01
And so, you know, we ve adjusted to a lot of things that we see that people care about, trying to be honest.
4
SPEAKER_01
We ve always tried to be honest.
4
SPEAKER_00
but also just show cool stuff in its raw form, the limits of the system, to see the system be perturbed and be robust and resilient and all that kind of stuff, and dancing with some music.
4
SPEAKER_00
Intrepidness and fun, so intrepid.
4
SPEAKER_01
I mean, it might be the most important ingredient, and that is, you know, robotics is hard,
4
SPEAKER_01
It s not gonna work right, right away.
4
SPEAKER_01
So don t be discouraged is all it really means.
4
SPEAKER_01
So usually when I talk about these things, I show videos and I show a long string of outtakes.
4
SPEAKER_01
And you have to have courage to be intrepid when you work so hard to build your machine and then you re trying and it just doesn t do what you thought it would do, what you want it to do.
4
SPEAKER_01
And you have to,
4
SPEAKER_01
Stick to it and keep trying.
4
SPEAKER_00
How long, I mean, we don t often see that, the story behind S.P.O.D.
4
SPEAKER_00
and Atlas.
4
SPEAKER_00
How long, how many failures was there along the way to get a working Atlas, a working S.P.O.D., in their early days, even working Big Dog?
4
SPEAKER_01
There s a video of Atlas climbing three big steps, and it s very dynamic, and it s really exciting, real accomplishment.
4
SPEAKER_01
It took 109 tries, and we have video of every one of them.
4
SPEAKER_01
We always shoot everything.
4
SPEAKER_01
Again, we, this is at Boston Dynamics.
4
SPEAKER_01
So it took 109 tries, but once it did it, it had a high percentage of success.
4
SPEAKER_01
So it s not like we re cheating by just showing the best one, but we do show the evolved performance, not everything along the way.
4
SPEAKER_01
But everything along the way is informative,
4
SPEAKER_01
It shows there s stupid things that go wrong, like the robot just when you say go and it collapses right there on the start.
4
SPEAKER_01
That doesn t have to do with the steps, or the perception didn t work right so you missed the target when you jump, or something breaks and there s oil flying everywhere.
4
SPEAKER_01
But that s fun.
4
SPEAKER_00
So the hardware failures and maybe some software.
4
SPEAKER_01
Lots of control of evolution during that time.
4
SPEAKER_01
I think it took six weeks to get those 109 trials.
4
SPEAKER_01
Because there was programming going on.
4
SPEAKER_01
It was actually robot learning, but there were human in the loop helping with the learning.
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SPEAKER_01
So all data driven.
4
SPEAKER_00
And you always are learning from that failure.
4
SPEAKER_00
And how do you protect Atlas from not getting damaged from 109 attempts?
4
SPEAKER_01
It s remarkable.
4
SPEAKER_01
One of the accomplishments of Atlas is that the engineers have made a machine that s robust enough that it can take that kind of testing where it s falling and stuff, and it doesn t break every time.
4
SPEAKER_01
It still breaks, and part of the paradigm is to have people to repair stuff.
4
SPEAKER_01
You gotta figure that in if you re gonna do this kind of work.
4
SPEAKER_01
I sometimes criticize the people who have their gold-plated thing and they keep it on the shelf and they re afraid to use it.
4
SPEAKER_01
I don t think you can make progress if you re working that way.
4
SPEAKER_01
You need to be ready to have it break and go in there and fix it.
4
SPEAKER_01
It s part of the thing.
4
SPEAKER_01
Plan your budget so you have spare parts and a crew and all that stuff.
4
SPEAKER_00
Yeah, if it falls 109 times, it s okay.
4
SPEAKER_00
Wow.
4
SPEAKER_00
So, intrepid, truly.
4
SPEAKER_00
And that applies to spot, that applies to all the other.
4
SPEAKER_00
Applies to everything.
4
SPEAKER_01
I think it applies to everything anybody tries to do that s worth doing.
4
SPEAKER_01
Yeah.
4
SPEAKER_00
And especially with systems in the real world, right?
4
SPEAKER_00
Yeah.
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SPEAKER_00
And so fun.
4
SPEAKER_01
Fun.
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SPEAKER_01
Technical fun, I usually say.
4
SPEAKER_01
Have technical fun.
4
SPEAKER_01
I think that life as an engineer
4
SPEAKER_01
is really satisfying.
4
SPEAKER_01
I think you get to, to some degree, it can be like crafts work, where you get to do things with your own hands or your own design or whatever your media is, and it s very satisfying to be able to just do the work, unlike a lot of people who have to do something that they don t like doing.
4
SPEAKER_01
I think engineers typically get to do something that they like, and there s a lot of satisfaction from that.
4
SPEAKER_01
Then there s
4
SPEAKER_01
In many cases, you can have impact on the world somehow because you ve done something that other people admire, which is different from the craft fun of building a thing.
4
SPEAKER_01
That s the second
4
SPEAKER_01
way that being an engineer is good.
4
SPEAKER_01
I think the third thing is that if you re lucky to be working in a team where you re getting the benefit of other people s skills that are helping you do your thing, none of us has all the skills needed to do most of these projects.
4
SPEAKER_01
If you have a team where you re working well with the others, that can be very satisfying.
4
SPEAKER_01
And then if you re an engineer, you also usually get paid.
4
SPEAKER_01
And so you kinda get paid four times in my view of the world.
4
SPEAKER_01
So what could be better than that?
4
SPEAKER_00
Get paid to have fun.
4
SPEAKER_00
I mean, what do you love about engineering?
4
SPEAKER_00
When you say engineering, what does that mean to you exactly?
4
SPEAKER_00
What is this kinda...
4
SPEAKER_00
big thing that we call engineering.
4
SPEAKER_01
I think it s both being a scientist or getting to use science at the same time as being kind of an artist or a creator, because you re making some, you know, scientists only get to describe, to study what s out there, and engineers get to make stuff that didn t exist before.
4
SPEAKER_01
And so it s really, I think, a higher calling, even though I think most, you know, the public out there thinks science is top and engineering is somehow secondary, but I think it s the other way around.
4
SPEAKER_00
And at the cutting edge, I think, when we talk about robotics, there is a possibility to do art in that you do the first of its kind thing.
4
SPEAKER_00
So then there s the production at scale, which is its own beautiful thing, but when you do the first new robot or the first new thing, that s the possibility to create something totally new.
4
SPEAKER_00
That is the art.
4
SPEAKER_01
I mean, bringing metal to life, or a machine to life, is kind of, is fun.
4
SPEAKER_01
It was fun doing the dancing videos where I got a huge public response.
4
SPEAKER_01
We re going to do more.
4
SPEAKER_01
We re doing some at the Institute and we ll do more.
4
SPEAKER_00
But that metal-to-life moment, I mean, to me, that s still magical.
4
SPEAKER_00
When inanimate objects comes to life, that s still, to me, to this day, it s still an incredible moment that human intelligence can create systems that instill life, or whatever that is.
4
SPEAKER_00
into inanimate objects, it s truly magical, especially when it s at the scale that humans can perceive and appreciate directly.
4
SPEAKER_01
But I think sort of with it going back to the pieces of that, you design a linkage that turns out to be half the weight and just as strong, that s very satisfying.
4
SPEAKER_01
There are people who do that, and it s a creative act.
4
SPEAKER_00
What to you is most beautiful about robotics?
4
SPEAKER_00
Sorry for the big romantic question.
4
SPEAKER_01
I think having the robots move in a way that s evocative of life is pretty exciting.
4
SPEAKER_01
So the elegance of movement.
4
SPEAKER_01
Yeah, or if it s a high performance act where it s doing it faster, bigger than other robots.
4
SPEAKER_01
Usually we re not doing it bigger, faster than people, but we re getting there in a few,
4
SPEAKER_01
narrow dimensions.
4
SPEAKER_00
So faster, bigger, smoother, more elegant, more graceful.
4
SPEAKER_01
I mean, I d like to do dancing that starts, you know, we re nowhere near the dancing capabilities of a human.
4
SPEAKER_01
We ve been having a ballerina in, who s kind of a well-known ballerina, and she s been programming the robot.
4
SPEAKER_01
We ve been working on the tools that can make it so that she can use her way of talking
4
SPEAKER_01
a way of doing a choreography or something like that, more accessible to get the robot to do things, and starting to produce some interesting stuff.
4
SPEAKER_00
Well, we should mention that there is a choreography tool.
4
SPEAKER_01
There is.
4
SPEAKER_00
I mean, I guess I saw versions of it, which is pretty cool.
4
SPEAKER_00
You can kind of, at slices of time, control different parts at the high level, the movement of the robot.
4
SPEAKER_01
We hope to take that forward and make it more tuned to how the dance world wants to talk.
5
SPEAKER_01
wants to communicate and get better performances.
5
SPEAKER_01
I mean, we ve done a lot, but there s still a lot possible.
5
SPEAKER_01
And I d like to have performances where the robots are dancing with people.
5
SPEAKER_01
So right now, almost everything that we ve done on dancing is to a fixed time base.
5
SPEAKER_01
So once you press go, the robot does its thing and plays its thing.
5
SPEAKER_01
It s not listening, it s not watching, but I think it should do those things.
5
SPEAKER_00
I think I would love to see a professional ballerina alone in a room with a robot slowly teaching the robot.
5
SPEAKER_00
Just actually the process of a clueless robot trying to figure out a small little piece of a dance.
5
SPEAKER_00
So it s not like, because right now, Atlas and Spot have done perfect dancing to a beat and so on, to a degree.
5
SPEAKER_00
But the learning process of interacting with a human would be incredible to watch.
5
SPEAKER_01
One of the cool things going on, you know that there s a class at Brown University called Choreo Robotics.
5
SPEAKER_01
Sidney Skybetter is a dancer, choreographer, and he teamed up with Stephanie Tellex, who s a computer science professor, and they taught this class, and I think they have some graduate students helping teach it.
5
SPEAKER_01
where they have two spots, and people come in, I think it s 50-50 of computer science people and dance people, and they program performances that are very interesting.
5
SPEAKER_01
I show some of them sometimes when I give a talk.
5
SPEAKER_00
And making that process of a human teaching the robot more efficient, more intuitive, maybe partial language, part movement, that d be fascinating.
5
SPEAKER_00
That d be really fascinating.
5
SPEAKER_00
One of the things I ve kind of realized is
5
SPEAKER_00
Humans communicate with movement a lot.
5
SPEAKER_00
It s not just language.
5
SPEAKER_00
There s body language.
5
SPEAKER_00
There s so many intricate little things.
5
SPEAKER_00
And like that, to watch a human and a spot communicate back and forth with movement, there s just so many wonderful possibilities there.
5
SPEAKER_01
But it s also a challenge.
5
SPEAKER_01
We get asked to have our robots perform with famous
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And they have 200 degrees of freedom or something, right?
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Every little ripple and thing, and they have all this head and neck and shoulders and stuff.
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And the robots mostly don t have all that stuff.
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And it s a daunting challenge to not look stupid, physically stupid next to them.
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So we ve pretty much avoided that kind of performance, but we ll get to it.
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I think even with the limited degrees of freedom, we could still have some sass and flavor and so on.
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You can figure out your own thing, even if you can t.
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And we can reverse things, like if you watch a human do robot animation, which is a dance style, where you jerk around, sort of, and you pop and lock and all that stuff.
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I think the robots could show up the humans by doing unstable oscillations and things that are faster than a person.
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So that s sort of on my,
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You know, my plan, but we haven t quite gotten there yet.
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You mentioned about building teams and robotics teams and so on.
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How do you find great engineers?
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How do you hire great engineers?
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I think you need to have an environment where interesting engineering, well, you know, it s a chicken and egg.
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If you have an environment where interesting engineering is going on,
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then engineers want to work there.
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I think it took a long time to develop that at Boston Dynamics.
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In fact, when we started, although I had the experience of building things in the leg lab both at CMU and at MIT, we weren t that sophisticated of an engineering thing compared to what Boston Dynamics is now.
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But it was our ambition to do that, and Sarkos was another robot company.
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So I always thought of us as being this much
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on the computing side and this much on the hardware side, and they were like this.
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Then over the years, I think we achieved the same or better levels of engineering.
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Meanwhile, Sarkis got acquired, and then they went through all kinds of changes.
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I don t know exactly what their current status is, but
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So it took many years is part of the answer.
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I think you gotta find people who love it.
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In the early days, we paid a little less, so we only got people who were doing it because they really loved it.
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We also hired people who might not have professional degrees, people who were building bicycles and building kayaks.
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We have some people who come from kind of the maker world, and that s really important.
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for the kind of work we do to have that be part of the mix.
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Whatever that is, whatever the magic ingredient that makes a great builder, maker, that s the big part of it.
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People who repaired the cars or motorcycles or whatever in their garages when they were kids.
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There s a kind of, like the robotics students, grad students, and just roboticists that I know and I hang out with, there s a kind of endless energy.
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They re just happy.
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I compare it, another group of people that are like that are people that skydive professionally.
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There s just like excitement and general energy that I think probably has to do with the fact that they re just constantly,
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First of all, fail a lot.
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And then the joy of building a thing that eventually works.
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Talking about being happy, there used to be a time when I was doing the machine shop work myself, back in those JPL and Caltech days, when if I came home smelling like the machine shop, you know, because it s an oily place, my wife would say, oh, you had a good day today, huh?
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Because she could tell that that s where I d been.
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You ve actually built something, you ve done something in the physical world.
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Yeah, and probably the videos help, right?
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The videos help show off what robotics is,
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Oh, you know, at Boston Dynamics, it put us on the map.
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We, I remember interviewing some sales guy, and he was from a company, and he said, well, no one s ever heard of my company, but we have products, you know, really good products.
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You guys, everybody knows who you are, but you don t have any products at all, which was true.
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So it was, and you know, we thank YouTube for that.
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YouTube came, we caught the YouTube wave, and it had a huge impact on our company.
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I mean, it s a big impact, not just on your company, but on robotics in general, helping people understand and inspire what is possible with robots.
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They inspire imagination, fear, everything.
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The full spectrum of human emotion was aroused, which is great for the entirety of humanity, and also probably inspiring for young people that want to get into AI and robotics.
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Let me ask you about some competitors.
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You ve been a complimentary of Elon and Tesla s work on Optimus Robot with their humanoid robot.
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What do you think of their efforts there with the humanoid robot?
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You know, I really admire Elon as a technologist.
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I think that what he did with Tesla, it was just totally mind boggling that he could go from this totally niche
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area that less than 1% of anybody seemed to be interested to making it.
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Essentially, every car company in the world is trying to do what he s done, so you got to give it to him.
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Then look at SpaceX.
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He s basically replaced NASA.
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That might be a little exaggeration, but not by much.
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You got to admire the guy.
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I wouldn t count him out for anything.
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I don t think Optimist today is where Atlas is, for instance.
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I don t know.
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It s a little hard to compare them to the other companies.
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I visited Figure.
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I think they re doing well and they have a good team.
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I ve visited Eptronic and I think they have a good team and they re doing well.
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Elon has a lot of resources.
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He has a lot of ambition.
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I d like to take some credit for his ambition.
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I think if I read between the lines, it s hard not to think that him seeing what Atlas is doing is a little bit of an inspiration.
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I hope so.
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Do you think Atlas and Optimus will hang out at some point?
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I would love to host that.
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Now that I m not at Boston Dynamics, I m not officially connected.
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I am on the board, but I m not officially connected.
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I would love to host a- Robot meetups?
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A robot meetup, yeah.
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Does the AI Institute work with Spots and Atlas?
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Is it focused on Spots mostly right now as a platform?
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We have a bunch of different robots.
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We bought everything we could buy.
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So we have Spots.
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I think we have a good-sized fleet of them.
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I don t know how many it is, but a good-sized fleet.
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We have a couple of animal robots.
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Animal is a company founded by Marco Hutter, even though he s not that involved anymore, but we have a couple of those.
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We have a bunch of arms like Franka s and U.S.
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Robotics.
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Because even though we have ambitions to build stuff, and we are starting to build stuff, day one, getting off the ground, we just bought stuff.
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I love this like robot playground you ve built.
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You can come over and take a look if you want.
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That s great.
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So it s like all these kinds of robots, legged, arms.
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It doesn t feel that much like, well, there s some areas that feel like a playground, but it s not like they re all frolic together.
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Hey, again, maybe you ll arrange it, a robot meetup.
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But in general, what s your view on competition in this space, especially like humanoid and Lego robots?
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Are you excited by the competition or the friendly competition?
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I think that it doesn t, I don t think about competition that much.
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I m not a commercial guy.
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I think for many years at Boston, the many years I was at Boston Dynamics, we didn t think about competition.
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We were just kind of doing our thing.
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It wasn t like there were products out there that we were competing with.
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Maybe there was some competition for DARPA funding, which we got a lot of, got very good at getting.
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But even there, in a couple of cases where we might have competed, we ended up just being the robot provider.
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That is for the little dog program.
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We just made the robots.
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We didn t participate as developers except for developing the robot.
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And in the DARPA Robotics Challenge,
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We didn t compete, we provided the robots.
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In the AI world now, now that we re working on cognitive stuff, it feels much more like a competition.
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The entry requirements in terms of computing hardware and the skills of the team are
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And hiring talent, it s a much tougher place.
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So I think much more about competition now on the cognitive side.
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On the physical side, it doesn t feel like it s that much about competition yet.
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Obviously with 10 humanoid companies out there, 10 or 12, I mean there s probably others that I don t know about, they re definitely in competition, will be in competition.
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how much room is there for a quadruped and especially a humanoid robot to become cheaper?
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So like cutting cost.
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And like how low can you go?
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And how much of it is just mass production?
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So questions of, you know, Hyundai, like how to produce versus like engineering innovation, how to simplify.
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I think there s a huge way to go.
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I don t think we ve seen the bottom of it, or the bottom in terms of lower prices.
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I think you should be totally optimistic that at asymptote, things don t have to be anything like as expensive as they are now.
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Back to competition, I wanted to say one thing.
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I think in the quadruped space, having other people selling quadrupeds
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is a great thing for Boston Dynamics.
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Because the question, I believe the question in the user s minds is, which quadruped do I want?
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It s not, oh, do I want a quadruped?
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Can a quadruped do my job?
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It s much more like that, which is a great place for it to be.
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Then you re just doing the things you normally do to make your product better and compete and selling and all that stuff.
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And that ll be the way it is with humanoids at some point.
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where there s a lot of humanoids and you re just not even, it s like iPhone versus Android and people are just buying both and it s kind of just, you re not really.
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You re creating the category, or the category is happening.
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I mean right now, the use cases, that s the key thing, having realistic use cases that are money making.
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in robotics is a big challenge.
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There s the warehouse use case.
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That s probably the only thing that makes anybody any money in robotics at this point.
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There s gotta be a moment.
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There s old fashioned, I mean there s aren t fixed arms doing manufacturing.
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I don t wanna say that they re not making money.
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Industrial robotics, yes, but there s gotta be a moment when social robotics starts making real money, meaning like a spa-type robot in the home, and there s tens of millions of them in the home, and they re like, you know, I don t know how many dogs there are in the United States as pets, but this feels, many.
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It feels like there s something we love about having a intelligent companion with us that remembers us, that s excited to see us, all that kind of stuff.
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But it s also true that the companies making those things, there ve been a lot of failures in recent times, right?
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There s that one year when I think three of them went under.
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So it s not that easy to do that, right?
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Getting performance, safety, and cost all to be where they need to be at the same time, that s hard.
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But also some of it is, like you said, you can have a product, but people might not be aware of it.
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So also part of it is the videos or however you connect with the public, the culture, and create the category.
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Make people realize this is the thing you want.
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There s a lot of negative perceptions you can have.
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Do you really want...
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a system with a camera in your home walking around, right, if it s presented correctly and if there s like the right kind of boundaries around it that you understand how it works and so on, that a lot of people would want to.
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And if they don t, they might be suspicious of it.
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So that s an important one.
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Like we all use smartphones and it has a camera that s looking at us.
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You know?
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Yeah, it has two or three or four.
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And it s listening.
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Very few people are, you know, suspicious about it.
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They kind of take it for granted and so on.
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And I think robots would be the same kind of way.
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I agree.
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So as you work on the cognitive aspect of these robots, do you think we ll ever get to human level or superhuman level intelligence?
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There s been a lot of conversations about this recently, given the rapid development in large language models.
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I think that intelligence is a lot of different things, and I think some things, computers are already smarter than people, and some things, they re not even close, and I think you d need a menu of detailed categories to come up with that.
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But I also think that
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The conversation that seems to be happening about AGI puzzles me.
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So I ask you a question.
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Do you think there s anybody smarter than you in the world?
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Absolutely, yes.
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Do you find that threatening?
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No.
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So I don t understand, even if computers were smarter than people, why we should assume that that s a threat.
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especially since they could easily be smarter but still available to us or under our control, which is basically how computers generally are.
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I think the fear is that they would be 10x, 100x smarter and operating under different morals and ethical codes than humans naturally do.
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and so almost become misaligned in unintended ways and therefore harm humans in ways we just can t predict and even if we program them to do a thing as
6
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on the way of doing that thing that would cause a lot of harm.
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And when they re 100 times, 1,000 times, 10,000 times smarter than us, we won t be able to stop it or we won t be able to even see the harm as it s happening until it s too late, that kind of stuff.
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So you can construct all kinds of possible trajectories of how the world ends because of super intelligent systems.
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It s a little bit like that line in the Oppenheimer movie.
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where they contemplate whether the first time they set off a reaction, all matter on Earth is going to go up.
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I don t remember what the verb they used was for the chain reaction.
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Yeah, I guess
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It s possible, but I personally don t think it s worth worrying about that.
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It s balancing opportunities and risk.
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I think if you take any technology, there s opportunity and risk.
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I ll point at the car.
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They pollute and about 1.25 million people get killed every year around the world because of them.
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Despite that, I think they re a boon to humankind, very useful.
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Many of us love them.
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And those technical problems can be solved.
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I think they are becoming safer.
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I think they re becoming less polluting, at least some of them are.
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And every technology you can name
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has a story like that in my opinion.
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What s the story behind the Hawaiian shirt?
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Is it a fashion statement, philosophical statement?
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Is it just a statement of rebellion?
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Engineering statement.
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It was born of me being a contrarian.
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Yes.
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It s a symbol.
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Someone told me once that I was wearing one when I only had one or two.
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And they said, oh, those things are so old-fashioned.
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You can t wear that, Mark.
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And I stopped wearing them for about a week.
6
Yeah.
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And then I said, I m not gonna let them tell me what to do.
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And so, every day since, pretty much.
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That was years ago, that was 20 years ago.
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15 years ago, probably.
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Ah, that says something about your personality, that s great.
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It took me a while to realize I was a contrarian.
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But, you know, it can be a useful tool.
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Have you had people tell you about, on the robotics side, that like, I don t think you could do this?
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That kind of negative motivation.
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I d rather talk about, there s a guy, when we were doing a lot of DARPA work, there was a Marine, Ed Tovar, who s still around, what he would always say is when someone would say, oh, you can t do that, he d say, why not?
6
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And it s a great question.
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I ask all the time when I m thinking, oh, we re not gonna do that, and I say, why not?
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And I give him credit for opening my eyes to,
6
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to resisting that.
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So yeah, the Hawaiian shirt is almost like a symbol of why not.
6
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Okay.
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What advice would you give to young folks that are trying to figure out what they want to do with their life, how to have a life they can be proud of, how they can have a career they can be proud of?
6
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When I was teaching at MIT, for a while I had undergraduate advisees where people would have to meet with me once a semester or something, and they frequently would ask what they should do.
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I think the advice I used to give was something like, well, if you had no constraints on you,
6
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no resource constraints, no opportunity constraints, and no skill constraints.
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What could you imagine doing?
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And I said, well, start there and see how close you can get.
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What s realistic for how close you can get?
6
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The other version of that is try and figure out what you wanna do and do that.
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A lot of people think that they re in a channel and there s only limited opportunities, but it s usually wider than they think.
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Yeah, the opportunities really are limitless.
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But at the same time, you want to pick a thing, right?
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And it s the diligence.
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And really, really pursue it, right?
6
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Really pursue it.
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Because sometimes the really special stuff happens after years of pursuit.
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Yeah.
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Oh, absolutely.
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It can take a while.
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I mean, you ve been doing this for 40 plus years.
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Some people think I m in a rut, right?
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Why don t I do, and in fact, some of the inspiration for the AI Institute is to say, okay, I ve been working on locomotion for however many years it was, let s do something else.
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And it s a really fascinating and interesting challenge.
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And you re hoping to show it off also in the same way as we ve done with Boston Dynamics?
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Just about to start showing some stuff off, yeah.
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I hope we have a YouTube channel.
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I mean, one of the challenges is it s one thing to show athletic skills on YouTube.
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Showing cognitive function is a lot harder, and I haven t quite figured out yet how that s gonna work.
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There might be a way.
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There s a way.
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There s a way.
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Why not?
6
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I also do think
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sucking at a task is also compelling.
6
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Like the incremental improvement.
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A robot being like really terrible at a task and then slowly becoming better.
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Even in athletic intelligence, honestly, like learning to walk and falling and slowly figuring that out.
6
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I think there s something extremely compelling about that.
6
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We like flaws, especially with a cognitive task.
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It s okay to be clumsy.
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It s okay to be confused and a little silly and all that kind of stuff.
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It feels like in that space is where we can.
6
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There s charm.
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SPEAKER_01
There s charm.
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There s charm and there s something inspiring about a robot sucking and then becoming less terrible slowly at a task.
6
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I think you re right.
6
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that kind of reveals something about ourselves.
6
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Ultimately, that s what s one of the coolest things about robots, is it s kind of a mirror about what makes humans special.
6
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Just by watching a heart assist to make a robot do the things that humans do, you realize how special we are.
6
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What do you think is the meaning of this whole thing?
6
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Why are we here?
6
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Mark, you ever ask about the big questions as you try to create these humanoid, human-like intelligence systems?
6
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I don t know, I think you have to have fun while you re here.
6
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That s about all I know.
6
SPEAKER_02
It would be a waste not to, right?
6
SPEAKER_01
The ride is pretty short, so might as well have fun.
6
SPEAKER_01
Mark, I m a huge fan of yours.
6
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It s a huge honor that you would talk with me.
6
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This is really amazing, and your work for many decades has been amazing.
6
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I can t wait to see what you do at the AI Institute.
6
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I m gonna be waiting impatiently for the videos and the demos and the next robot meetup for maybe Atlas and Optimus to hang out.
6
SPEAKER_02
I would love to do that.
6
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That would be fun.
6
SPEAKER_01
Thank you so much for talking to me.
6
SPEAKER_02
Thank you, it was fun talking to you.
6
SPEAKER_01
Thanks for listening to this conversation with Mark Rybert.
6
SPEAKER_01
To support this podcast, please check out our sponsors in the description.
6
SPEAKER_01
And now, let me leave you with some words from Arthur C. Clarke.
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Whether we re based on carbon or on silicon makes no fundamental difference.
6
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We should each be treated with appropriate respect.
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Thank you for listening and hope to see you next time.