Title: Learning body-affordances to simplify action spaces,
Abstract: Controlling embodied agents with many actuated degrees of freedom is a
challenging task. We propose a method that can discover and interpolate between
context dependent high-level actions or body-affordances. These provide an
abstract, low-dimensional interface indexing high-dimensional and time-
extended action policies. Our method is related to recent ap- proaches in the
machine learning literature but is conceptually simpler and easier to
implement. More specifically our method requires the choice of a n-dimensional
target sensor space that is endowed with a distance metric. The method then
learns an also n-dimensional embedding of possibly reactive body-affordances
that spread as far as possible throughout the target sensor space.