This is Toddler.
This is Dr. Russ Tedrake.
Together, they're covering new ground at MIT.
So we call the robot Toddler because it learns to walk
and because it toddles when it walks.
These are baby steps towards developing
a new class of robot, one that uses very little energy,
about the same as a student walking to class.
There are no complicated gears and hydraulics for Toddler.
It's a simple mechanical device, actually based
on an old design for walking toys.
They're called passive dynamic walkers.
They waddled down hills, using gravity as their power source.
Toddler can do that, but its makers
have added a little extra power.
It has a computer and battery pack on board,
but it does a lot of the work itself, gaining momentum
from its own steps.
And Toddler doesn't just walk downhill,
he can walk on flat surfaces, too.
We're letting the dynamics of the robot
solve most of the control problem,
and we just add a little bit of energy
here there to kind of push and pull the robot,
and that's a much more energy efficient strategy
for designing a walking robot.
That little bit of energy makes a big difference.
High-performance robots like ASIMO
may be capable of many tasks.
But they're loaded with control programs,
and those demand a lot of juice.
ASIMO's batteries need to be recharged every half hour,
but not Toddler.
It's just such a thrill whenever you see the robot walking,
and especially when you do it in, like, in a public place
and you can see all the people go, oh look,
it's the cool little robot.
But what makes Toddler really intriguing
is that like a human youngster, this robot can learn.
Toddler's sensors communicate directly with its motors.
They sense a change in the walking surface,
allowing the robot to make adjustments with every step.
And that's what we call learning.
It's a simple idea, but it's the best way
we can embody what we think the nervous system is doing, too,
when we're talking about learning.
Researchers can adjust the pace at which Toddler learns,
but faster learning comes with a price.
Toddler can move from linoleum to carpet,
quickly learning the difference and adjusting its movement
and balance.
But if it has to move back to linoleum,
takes the robot a little longer.
There's this very interesting result
in learning theory, which tells you,
you can't have learning without generalization.
The problem is when you start generalizing,
then there's a possibility that you'll overwrite things
that you've learned in the past.
Toddler is one of three new walking
robots that are testing the limits
of passive dynamic design.
This one at Cornell University, in New York state,
is testing efficiency.
The design allows this robot to push off with its trailing leg,
giving it a little extra boost.
And this robot at Delft University in the Netherlands
is testing robustness of design.
It's able to maintain a stable gait,
even when pushed off balance.
This robot works on the principle of the best way
to stop yourself from falling forward
is to put your foot out.
The recovery motion is made possible
by a hip actuator that acts like a hip joint.
These three teams are hoping to meld their research
to create one robot that is efficient, robust,
and capable of learning.
It's our goal to convince people of the value of this approach
and to just keep building better and better robots to do
more and more things, and all exploit the dynamics.
So how does studying a robot that
walks like a human actually benefit humans?
Ultimately, down the line, far down the line,
if we understand the motor learning system better, then
there's a potential that we could use that information
to design rehabilitation programs, even
neural prosthetics, to help people recover from injuries
or augment their performance.
There's a lot of different potential applications.
As for Toddler, he's got a big brother already
in the works, one with smaller feet
and a fine set of knees that can lock and swing.
And that will make the robot more versatile
and also, just a little more human, something
Dr. Tedrake thinks is important.
When you see a robot that looks like a human,
you have a tendency to reach out to it.
And it really helps, I think, with
the human-robot interaction.
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I think it's the first step in a long road of building
better robots, more efficient robots,
and learning systems for robots.
So I hope that it's just the first piece of a very
complex and very long puzzle.