How, exactly, do you teach a robot?
Traditionally, to get a robot to do what you want
requires programming--
a list of tasks or rules to follow--
which often means nuance
and the unpredictable are left out.
Or, in many AI systems, the robot learns from
massive sets of training data.
What if you could make a robot figure it out for itself,
based on concepts and inference?
Researchers have developed a new system
for teaching robots concepts by presenting them
a pair of images and having them figure out
how to transform one to the other in real life.
The goal is to introduce a method of learning
used by humans all the time--learning by inference—
like looking at the instructions
for assembling an IKEA chair.
Let’s take an easier example,
these two images of apples - the input, on the left,
and the output on the right.
For us, it’s easy to see how the apples
are reorganized by position and color.
The trick here is to make a robot recognize
the conceptual differences, plan the changes needed,
and then act on that plan.
Researchers experimented with two different robots,
each equipped with a camera at the end of a gripper
for manipulating different objects on a tabletop.
They presented the robots with a pair of images -
an input and an output -
representing a concept.
Each concept consisted of different shapes,
colors, and positions, which are captured
by the camera and then run through special software.
It was then up to the robots to infer exactly how
to rearrange the objects on the tabletop
to match the output image in each concept.
The robots achieved that by evaluating
the different matchups in their imagination
to arrive at a program that described the concept
Researchers used a dataset of 546 different concepts
to test the robots, and the robots
successfully learned 535 of them.
Researchers then tested 6 of these concepts
with real-world variations in objects, backgrounds,
and complexities.
despite those variations, the robots were able to
successfully execute the concepts
They even found that they could teach the robots
the steps needed by directing their
attention with a pointer.
Researchers believe that in the future,
systems like these could be used to
help robots achieve tasks
without copying the movements of people
and in new situations
never encountered before...