Title: Adaptation and Robust Learning of Probabilistic Movement Primitives,
Abstract: Probabilistic representations of movement primitives open important new
possibilities for machine learning in robotics. These representations are able
to capture the variability of the demonstrations from a teacher as a
probability distribution over trajectories, providing a sensible region of
exploration and the ability to adapt to changes in the robot environment.
However, to be able to capture variability and correlations between different
joints, a probabilistic movement primitive requires the estimation of a larger
number of parameters compared to their deterministic counterparts, that focus
on modeling only the mean behavior. In this paper, we make use of prior
distributions over the parameters of a probabilistic movement primitive to make
robust estimates of the parameters with few training instances. In addition, we
introduce general purpose operators to adapt movement primitives in joint and
task space. The proposed training method and adaptation operators are tested in
a coffee preparation and in robot table tennis task. In the coffee preparation
task we evaluate the generalization performance to changes in the location of
the coffee grinder and brewing chamber in a target area, achieving the desired
behavior after only two demonstrations. In the table tennis task we evaluate
the hit and return rates, outperforming previous approaches while using fewer
task specific heuristics.