Abstract

Movement Primitives (MP) are a well-established approach for representing modular
and re-usable robot movement generators. Many state-of-the-art robot learning
successes are based MPs, due to their compact representation of the inherently
continuous and high dimensional robot movements. A major goal in robot learning
is to combine multiple MPs as building blocks in a modular control architecture
to solve complex tasks. To this effect, a MP representation has to allow for
blending between motions, adapting to altered task variables, and co-activating
multiple MPs in parallel. We present a probabilistic formulation of the MP concept
that maintains a distribution over trajectories. Our probabilistic approach
allows for the derivation of new operations which are essential for implementing
all aforementioned properties in one framework. In order to use such a trajectory
distribution for robot movement control, we analytically derive a stochastic feedback
controller which reproduces the given trajectory distribution. We evaluate
and compare our approach to existing methods on several simulated as well as
real robot scenarios.