This is Bert.
Bert is an iCub.
iCub is a very sophisticated humanoid robot.
It is built by a European consortium.
And there are different robot cubs in various laboratories throughout the world.
We happen to have the only one in the Western Hemisphere.
We call our iCub Bert.
Bert is short for Bertrand Russell.
We have always named
all of our robots after famous
people who have contributed to the early days of cybernetics.
My research his about language
and getting machines to interact with humans
via natural spoken language.
Bert has human architecture in many significant ways;
particularly dexterous hands...
arms and legs...
very sophisticated eye movements...
balance control...
and, to all intents and purposes, Bert can mimic almost any action
that a human can do.
Onboard, Bert has 13 microcontrollers
to control the various motors and mechanical linkages.
Bert also has the human senses,
including binocular color vision and binaural hearing.
And we think that all these things are going to be necessary
in order for Bert to ultimately build a model
of the external world that would support natural language communication.
The approach that we're using is 'embodied cognition.'
The idea is that, the closer you get to being able to
experience the world as a human does,
the more likely you will be able to produce a human-like model
of the external world.
What's really going on in natural language communication is
that people are making internal mental models
of the external world and then encoding that model in natural language.
So let's take a simple example.
How could you possibly know what it means to balance two ideas or
to balance two options against each other
if you don't know how to physically balance
your physical being in the world?
And natural language is just simply loaded with words that we
use in our everyday language that have to do with spatial reasoning.
And the best way to learn and understand
how to reason spatially is by having a body that moves and
lives in a three-dimensional space.
A human instructor can go up to Bert
and grasp his hand
and move it in a circular motion.
And then you can say to Bert, "This is a circle."
And then Bert learns what the circle is.
And then, later on, if you say to Bert, "Draw a circle,"
then he can reproduce the behavior.
What an engineer might do in order to produce this capability
is to simply take a commercial speech recognizer,
put it inside Bert, and connect it up to
Bert's ability to measure all its joint angles.
And then, every time you said, "Circle," Bert would simply
play-back the exact recorded motions.
That is absolutely not what's going on in this experiment.
Bert is building a linguistic model of
the speech that it hears and it's building a mechanical model of the
motions that it produces.
And, exactly like humans, every time Bert produces a gesture,
it's slightly different.
The models that humans are capable of making are imperfect.
And we are giving Bert the same disadvantage.
[BALL HITTING FLOOR]
INSTRUCTOR: "Uhg!"
[Laughing]
The ultimate goal
is to take all the things that Bert learns about spatial movements
and to have him be able to take those models apart
and reassemble them; so that he could produce novel gestures,
novel behaviors, and even novel linguistic expressions.
Now, we think that this is what humans ultimately learn to do
and it seems to us that that their ought to be
mathematical representations of this process.
Nature has already given us a solution
in the form of ourselves and our bodies.
And, if the robot could learn the same thing and represent
the problems and their solutions in the same way,
then we would learn how to make other artificial devices
that would be able to solve problems in very specific contexts.
And the applications of this kind of
knowledge
are almost limitless.