身体感覚のずれを克服する模倣学習のドメイン適応
Adapt Your Body: Mitigating Proprioception Shifts in Imitation Learning
模倣学習において訓練時と実環境での固有感覚（proprioception）の分布ずれが性能を低下させる問題を特定し、Wasserstein距離に基づくノイズ付加で分布を整合させる手法を提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Fuhang Kuang, Jiacheng You, Yingdong Hu, Tong Zhang, Chuan Wen, Yang Gao
分類: cs.RO, cs.AI
原文アブストラクト
Imitation learning models for robotic tasks typically rely on multi-modal inputs, such as RGB images, language, and proprioceptive states. While proprioception is intuitively important for decision-making and obstacle avoidance, simply incorporating all proprioceptive states leads to a surprising degradation in imitation learning performance. In this work, we identify the underlying issue as the proprioception shift problem, where the distributions of proprioceptive states diverge significantly between training and deployment. To address this challenge, we propose a domain adaptation framework that bridges the gap by utilizing rollout data collected during deployment. Using Wasserstein distance, we quantify the discrepancy between expert and rollout proprioceptive states and minimize this gap by adding noise to both sets of states, proportional to the Wasserstein distance. This strategy enhances robustness against proprioception shifts by aligning the training and deployment distributions. Experiments on robotic manipulation tasks demonstrate the efficacy of our method, enabling the imitation policy to leverage proprioception while mitigating its adverse effects. Our approach outperforms the naive solution which discards proprioception, and other baselines designed to address distributional shifts.
