Item

Abstract

Table tennis is a sufficiently complex motor task for studying complete skill learning systems. It consists of several elementary motions and requires fast movements, accurate
control, and online adaptation. To represent the elementary
movements needed for robot table tennis, we rely on dynamic
systems motor primitives (DMP). While such DMPs have been
successfully used for learning a variety of simple motor tasks,
they only represent single elementary actions. In order to select
and generalize among different striking movements, we present
a new approach, called Mixture of Motor Primitives that uses
a gating network to activate appropriate motor primitives. The
resulting policy enables us to select among the appropriate
motor primitives as well as to generalize between them. In
order to obtain a fully learned robot table tennis setup, we
also address the problem of predicting the necessary context
information, i.e., the hitting point in time and space where
we want to hit the ball. We show that the resulting setup
was capable of playing rudimentary table tennis using an
anthropomorphic robot arm.