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Abstract

Obtaining novel skills is one of the most important problems
in robotics. Machine learning techniques may be a promising approach
for automatic and autonomous acquisition of movement policies. However,
this requires both an appropriate policy representation and suitable
learning algorithms. Employing the most recent form of the dynamical
systems motor primitives originally introduced by Ijspeert et al. [1],
we show how both discrete and rhythmic tasks can be learned using
a concerted approach of both imitation and reinforcement learning, and
present our current best performing learning algorithms. Finally, we show
that it is possible to include a start-up phase in rhythmic primitives. We
apply our approach to two elementary movements, i.e., Ball-in-a-Cup
and Ball-Paddling, which can be learned on a real Barrett WAM robot
arm at a pace similar to human learning.