逆動力学の不一致を最小化する観察からの模倣学習
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement
状態のみのデモンストレーションから模倣するLfOにおいて、模倣者と専門家の逆動力学モデルの不一致がLfDとの差であることを理論的に示し、その上限をモデルフリーに最小化するIDDMを提案した論文。
著者: Chao Yang, Xiaojian Ma, Wenbing Huang, Fuchun Sun, Huaping Liu, Junzhou Huang, Chuang Gan
分類: cs.LG, cs.AI, cs.RO, stat.ML
原文アブストラクト
This paper studies Learning from Observations (LfO) for imitation learning with access to state-only demonstrations. In contrast to Learning from Demonstration (LfD) that involves both action and state supervision, LfO is more practical in leveraging previously inapplicable resources (e.g. videos), yet more challenging due to the incomplete expert guidance. In this paper, we investigate LfO and its difference with LfD in both theoretical and practical perspectives. We first prove that the gap between LfD and LfO actually lies in the disagreement of inverse dynamics models between the imitator and the expert, if following the modeling approach of GAIL. More importantly, the upper bound of this gap is revealed by a negative causal entropy which can be minimized in a model-free way. We term our method as Inverse-Dynamics-Disagreement-Minimization (IDDM) which enhances the conventional LfO method through further bridging the gap to LfD. Considerable empirical results on challenging benchmarks indicate that our method attains consistent improvements over other LfO counterparts.
