遮蔽と観測ノイズ下の逆強化学習のための周辺MAP推定
Marginal MAP Estimation for Inverse RL under Occlusion with Observer Noise
専門家の実演が遮蔽や知覚ノイズで部分的にしか観測できない状況で、軌跡の隠れた部分を周辺化する周辺MAP推定により逆強化学習を行う手法を提案し、玉ねぎ仕分けロボットのタスクで有効性を示した。
著者: Prasanth Sengadu Suresh, Prashant Doshi
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
We consider the problem of learning the behavioral preferences of an expert engaged in a task from noisy and partially-observable demonstrations. This is motivated by real-world applications such as a line robot learning from observing a human worker, where some observations are occluded by environmental objects that cannot be removed. Furthermore, robotic perception tends to be imperfect and noisy. Previous techniques for inverse reinforcement learning (IRL) take the approach of either omitting the missing portions or inferring it as part of expectation-maximization, which tends to be slow and prone to local optima. We present a new method that generalizes the well-known Bayesian maximum-a-posteriori (MAP) IRL method by marginalizing the occluded portions of the trajectory. This is additionally extended with an observation model to account for perception noise. We show that the marginal MAP (MMAP) approach significantly improves on the previous IRL technique under occlusion in both formative evaluations on a toy problem and in a summative evaluation on an onion sorting line task by a robot.
