>>Marc Raibert: The kind of robots we are most interested in are ones that are like
people and animals, life like, like people and animals.
And our dream is to be able to build robots that can do everything that we all can do,
or even better than that.
Now, that's a pretty tall challenge.
Today's robots really aren't that good compared to people and animals.
But I think it's a good direction and a good goal.
One of the exciting things about studying or developing robots that act like people
and animals is that you can use the animals and the people themselves and make measurements
on them to get clues as to what reasonable behavior is.
You can get sort of feasibility study.
If the robot or animal -- I mean, if the person or animal can do that, then it's likely that,
in theory, at least, you can make a robot do that.
You can also look at the designs and the -- what you can figure out about what the brain is
doing in order to get insight into what kind of mechanisms you want to build and what kind
of control algorithms you want to develop.
And that's what we do.
There's also another opportunity, which is to use the -- what we learn and what we develop
in robots to inform ourselves about how robots -- how people and animals really work.
It might not be obvious that if you build a robot, it's going to tell you about the
person or animal, but there's lots of things that are very difficult to get at in biological
studies that doing robotics does.
And we work with people in the biological community, most closely with people at the
Harvard Concord Field Station and at the Royal Veterinary School in London in some of our
studies.
And it's been a very exciting two-way flow.
So just to summarize, I think that if we can get at the fundamentals of how people and
animals work and build them into our reboots -- and when I say "fundamentals," I mean the
mobility, the agility, the dexterity, the Grace, really -- we're going to create a new
generation of robots that are really capable, much more capable than today's robots, and
they'll have all kinds of useful applications.
We'll be -- in the early days, we'll be able to use them for emergency response, maybe
send them into places like the failed Fukushima reactor, where nothing could really help us
now.
We didn't want to -- all we could do was risk people's lives by going in there.
Why not send robots in?
Fighting fires.
Eventually, maybe have them in our homes, helping us care for our elderly or I guess
we'll be the elderly soon enough.
And, you know, I'd really much rather have a robot change my diapers than have my son
or daughter have to change my diapers.
I know that seems kind of an odd thing to say.
But, you know, that's where many of us are headed.
And that's -- [ Laughter ]
>>Marc Raibert: I think there's an interesting set of opportunities there.
Okay.
I have a little film here that gave us some inspiration.
We're going to start with inspiration, then go to a status report, and then circle in
on robot vulnerabilities.
Here's a set of goats that are traveling in very rough terrain.
There was a baby goat there.
And, you know, they work with incredible agility, confidence.
Look at this animal, this one climbing.
Really, this animal holds its whole life in its hooves when it goes up on this steep surface.
And here's animals that are running for their lives, both the Antelope that's trying to
get away, but, really, the cheetah's running for its life, too.
Because if he doesn't get a meal, he may not survive.
And, look, here's just ordinary people.
Look at the amazing things they can do.
So I think in the theme of this session of the exploring the world, I think our focus
is on the world that's within ourselves and within animals and trying to bring that out
into the robotics world, and then to make robots that can explore the larger world.
So our approach to understanding and to building both animal behavior and robots is to focus
on three things: The behavior, the physical machine, and then the computational part of
the problem.
And so in the parlance of animals, you might call that the behavior, the body, and the
brain.
And in the context of robots, you'd call that the robot behavior, the mechanical design
or the mechanical implementation, and the control computer.
And the control computer might also be a sensing computer.
And it's really the interaction between these three things that's at the core of progress
and solutions to these things.
And I believe that the computer control part of this really represents a kind of intelligence.
It's not intelligence like Google intelligence, which is, you know -- you know, has all the
knowledge of the world.
Or it's not like being able to play chess, which is planning out a sequence of moves.
It's more about the intelligence of how a physical thing behaves in the world, how I'm
affected by the physics around me, how I fall.
Here, I'll give you an example, or an example of a working dynamic system that's using this
kind of a setup.
I'm standing on my feet.
And I'm dynamically balancing myself.
That means that my sensors are determining how I'm moving.
And then those signals are used with control algorithms to influence my muscles.
And if you don't believe I'm balancing now, here, I'll go up on one leg.
[ Applause.
] >>Marc Raibert: I'll go up on my toe.
Or I can even jump on one leg and travel across the stage that way.
So I think that's a miracle.
And it's not just 'cause it's me.
You all can do this, too.
[ Laughter ] >>Marc Raibert: And in order to implement
a process like that, you need a machine that's capable of moving and exerting forces.
You need a controller that's looking at the world around it and using that information
and a knowledge of how the machine works in order to give instructions to the machine.
And you need to have a concept of the whole behavior.
And one thing that I think is a misnomer, especially in the digital age, I think everybody
thinks that computers figure out stuff and then they just dictate behavior to a machine
like this.
And the real facts of life are that this mechanical machine has two masters.
One master might be the computer.
But the other one is the physics of the world around it.
It has to fall under gravity.
It has to resist forces that are applied to it.
And unless those -- the control system reconciles itself with that physical world, it really
can't get anywhere in getting the behavior it wants.
So, really, all the control system can do is make suggestions to the physical system,
and then the behavior unfolds.
So I believe the design, the mechanical system, and the control work together to produce behavior.
So I'll give you a little status report on the robots we've been working on.
I know many of you are probably avid YouTube watchers, so you've seen some of this.
But I have some new things in here.
Here's a dynamic robot that is carrying a little engine that operates a hydraulic actuation
system.
It's also got sensors built into its legs so it can feel the terrain it's on.
It's also got a gyro.
And it uses all that information and the onboard control in order to control its behavior.
It doesn't want to fall over when it's being disturbed.
Now, that guy isn't an angry guy who's kicking it because he's punishing it.
He's doing it to show how proudly it can keep its balance.
But here's an accidental balance where we had the robot out on an icy winter day and
it slipped on the ice and was able to keep its balance.
Now, for real dynamic behavior where you're flying through the air, as animals do, you
want to have a gate like this one.
This is a running trot.
We have springs built into the legs.
And some of the energy is recycled from step to step on each cycle and returned.
That's one of the reasons we call it a dynamic system.
Here's a robot that's on a very slippery surface.
This surface is so slippery that we had a very hard time getting up that hill as humans.
And I think this is a case where the stability and traction of the robot really rivaled that
of a human being.
That previous robot's called Big Dog.
This one is called bigger dog.
That's actually not its name, but it is a bigger dog.
It weighs about 800 pounds and can carry about 400 pounds of payload.
That's a very good fraction of its weight.
And it's got really remarkable mobility, all dynamically provided by having an onboard
computer.
And this robot does another trick, which is, it doesn't have to have a driver.
So it's using a sensory system, which you see on the front there.
You might call it the head.
And it's got cameras and laser range finders.
And it's watching the leader and going wherever the leader goes.
In this case, the leader's cooperative, so it's just following him, weaving in and out
of the trees as he goes along.
Eventually, we hope to get this to follow at about 30 meters, and at full -- at whatever
speed a human can go.
Now, Bigger Dog is really a lot harder to knock over than Big Dog was.
And you saw us kind of demonstrating it there.
We're working on having it be able to go on all kinds of obstacles.
And if it does fall over, this robot was designed so it can get itself back up and keep chugging.
So with that recovery capability, it's really got a shot at being able to go out into the
world, and fail sometime, but then recover and go again.
Here's a cheetah.
And one of the things I wanted you to see is that one of the ways a cheetah runs fast
is by using its back to articulate the legs.
So the range of motion on the front legs and the back legs is much larger than it would
be if the back were rigid.
So here's an experimental robot cheetah that we built where it has an articulated back
and it has legs that don't have quite the range of motion of the actual cheetah.
Now, this cheetah doesn't run nearly as fast as a real cheetah, but it runs faster than
the fastest human.
So this summer, we beat Usain Bolt's fastest running speed, which is about 27.8 miles per
hour, and we gotten the cheetah up to 30 miles an hour.
You saw it at the very end there just before its hydraulics let lose.
And this is a model of a three-dimensional outdoor version that we're working on and
hope to have working in the spring.
So the last robot I'm going to show is this human-form robot that is the same anthropometry,
that means the same dimensions and fits in the same envelope, as the 50th percentile
male human.
And, eventually, it'll have a head that also fits in that envelope.
We haven't finished the head yet.
And this robot was originally designed to test clothing, chemical protection clothing.
Therefore, it has to go through a bunch of exercises and it has to conform to the size
and shape of a person.
Now, we've been adapting this robot for a new program where we're building a small fleet
of them.
It's sort of an open competition that DARPA is running where they're going to use the
robot to see if they can do emergency disaster scenarios.
So they're going to have the robot climb a ladder, crawl across rubble, operate some
devices that are normally operated by a human being.
Now, I know from experience that many of you are afraid of my robots.
And I love our robots.
And I don't really, in my heart, understand why you're afraid.
But I know reading comments on YouTube that a fair number of people are afraid.
And maybe we can -- during the break, we can talk about why that is.
But I thought maybe if I showed you, you know, the vulnerable side of the robots, you could
come around to my way of seeing things.
So I think you might -- So this is -- I know in your labs, everything always works perfectly
and like clock work.
But in our place, it doesn't always go the way it's supposed to.
[ Laughter ] >>Marc Raibert: And, of course, I'm subtly
showing you that the robot actually keeps going, even though it's got the brick on its
foot and can adjust to that situation and keep its balance.
Now, you've seen us -- Want to try again to see if it -- there's
more of this.
You've seen us pick on Big Dog and keep its balance.
But it took us quite some development time before it was able to do that.
And I'm going to show you a couple of shots if the video successfully plays ahead, what
the development sequence was like during the course of bringing it up to speed.
We seem to have having a malfunction here.
Ah.
Is there -- okay.
We'll just skip on.
[ Laughter ] >>Marc Raibert: It's too bad.
I was going to show you some really funny stuff.
So one thing that I get a lot of pleasure out of is that Big Dog's become part of the
-- I guess it's the Zeitgeist of the YouTube generation.
And people using the tools that are available to themselves have gone out and -- and flattered
Big Dog.
And I'll show you one of those.
[ Video.
] >>Marc Raibert: How many people have seen
this?
You can think of this as a reverse touring test.
A touring test was a test to see whether a system was as smart as a person and whether
it could disguise itself as a person.
And this is a test to see whether this system was really Big Dog, or is it an impostor of
some kind.
[ Laughter ] >>Marc Raibert: So Boston Dynamics -- Boston
Dynamics had absolutely nothing to do with the production of this video.
And I take it as a sign of Big Dog's success that there's not only this spoof, but --
[ Laughter ] >>Marc Raibert: Can you go to the next one?
But there's a whole -- There's about two dozen Big Dog spoofs out
there.
The one in the upper left is from the Marunouchi section of Tokyo.
There's an online television channel that's been spoofing us for a while.
I guess that lower right one is from Appalachia somewhere where all the guy had was a cardboard
box, didn't even have a friend.
The lower left is from Norway.
Thank you.
[ Laughter ]