Abstract

Learning motor skills for robots is a hard
task. In particular, a high number of degrees-of-freedom
in the robot can pose serious challenges to existing reinforcement
learning methods, since it leads to a highdimensional
search space. However, complex robots are
often intrinsically redundant systems and, therefore, can
be controlled using a latent manifold of much smaller
dimensionality. In this paper, we present a novel policy
search method that performs efficient reinforcement learning
by uncovering the low-dimensional latent space of
actuator redundancies. In contrast to previous attempts
at combining reinforcement learning and dimensionality
reduction, our approach does not perform dimensionality
reduction as a preprocessing step but naturally combines
it with policy search. Our evaluations show that the new
approach outperforms existing algorithms for learning
motor skills with high-dimensional robots.