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.
@article{arxiv.1708.04391,
title = {Learning body-affordances to simplify action spaces},
author = {Nicholas Guttenberg and Martin Biehl and Ryota Kanai},
journal= {arXiv preprint arXiv:1708.04391},
year = {2017}
}