Cornell engineers are helping
humans and robots work together to find the best way to do a job, an approach
called ‘coactive learning’. Modern industrial robots, like those on automobile
assembly lines, have no brains, just memory. An operator programs the robot to
move through the desired action; the robot can then repeat the exact same
action every time a car goes by. But off the assembly line, things get
complicated: A personal robot working in a home has to handle tomatoes more
gently than canned goods. If it needs to pick up and use a sharp kitchen knife,
it should be smart enough to keep the blade away from humans. Researchers set
out to teach a robot to work on a supermarket checkout line, modifying a Baxter
robot from Rethink Robotics in Boston, designed for assembly line work. It can
be programmed by moving its arms through an action, but also offers a mode
where a human can make adjustments while anaxctiinis in progress. The Baxter’s
arms have two elbows and a rotating wrist, so it’s not always obvious to a
human operator how best to move the arms to accomplish a particular task. So
the researchers, drawing on previous work, added programming that lets the
robot plan its own motions. It displays three possible trajectories on a touch
screen where the operator can select the one that looks best.
Then humans can give corrective
feedback. As the robot executes its movements, the operator can intervene,
guiding the arms to fine-tune the trajectory. The robot has what the
researchers’ call a ‘zero-G’ mode, where the robot's arms hold their position
against gravity but allow the operator to move them. The first correction may
not be the best one, but it may be slightly better. The learning algorithm the
researchers provided allows the robot to learn incrementally, refining its
trajectory a little more each time the human operator makes adjustments. Even
with weak but incrementally correct feedback from the user, the robot arrives
at an optimal movement. The robot learns to associate a particular trajectory
with each type of object. A quick flip over might be the fastest way to move a
cereal box, but that wouldn’t work with a carton of eggs. Also, since eggs are
fragile, the robot is taught that they shouldn’t be lifted far above the
counter. Likewise, the robot learns that sharp objects shouldn’t be moved in a
wide swing; they are held in close, away from people. In tests with users who
were not part of the research team, most users were able to train the robot
successfully on a particular task with just five corrective feedbacks. The
robots also were able to generalize what they learned, adjusting when the
object, the environment or both were changed.
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