身体アフォーダンス学習による行動空間の簡素化
Learning body-affordances to simplify action spaces
目標センサー空間上の距離指標を用いて、高次元の行動方策をインデックス化する低次元の身体アフォーダンスを学習・補間する手法を提案した。
著者: Nicholas Guttenberg, Martin Biehl, Ryota Kanai
分類: cs.AI, cs.RO
原文アブストラクト
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.