モデルベース逆強化学習による時間不変な報酬関数の学習
Learning Time-Invariant Reward Functions through Model-Based Inverse Reinforcement Learning
実演から時間不変なコストを学習することで、実行速度や時間的ずれに依存せず汎化する報酬関数を獲得する手法を提案し、Kukaアームでの配置・ペグ挿しタスクで有効性を示した。
著者: Todor Davchev, Sarah Bechtle, Subramanian Ramamoorthy, Franziska Meier
分類: cs.RO, stat.ML
原文アブストラクト
Inverse reinforcement learning is a paradigm motivated by the goal of learning general reward functions from demonstrated behaviours. Yet the notion of generality for learnt costs is often evaluated in terms of robustness to various spatial perturbations only, assuming deployment at fixed speeds of execution. However, this is impractical in the context of robotics and building, time-invariant solutions is of crucial importance. In this work, we propose a formulation that allows us to 1) vary the length of execution by learning time-invariant costs, and 2) relax the temporal alignment requirements for learning from demonstration. We apply our method to two different types of cost formulations and evaluate their performance in the context of learning reward functions for simulated placement and peg in hole tasks executed on a 7DoF Kuka IIWA arm. Our results show that our approach enables learning temporally invariant rewards from misaligned demonstration that can also generalise spatially to out of distribution tasks.
