Abstract

Future robots need to autonomously acquire motor
skills in order to reduce their reliance on human programming.
Many motor skill learning methods concentrate
on learning a single solution for a given task. However, discarding
information about additional solutions during learning
unnecessarily limits autonomy. Such favoring of single solutions
often requires re-learning of motor skills when the task, the
environment or the robot’s body changes in a way that renders
the learned solution infeasible. Future robots need to be able to
adapt to such changes and, ideally, have a large repertoire of
movements to cope with such problems. In contrast to current
methods, our approach simultaneously learns multiple distinct
solutions for the same task, such that a partial degeneration of
this solution space does not prevent the successful completion
of the task. In this paper, we present a complete framework
that is capable of learning different solution strategies for a
real robot Tetherball task.