cs.AI updates on arXiv.org 07月02日 12:03
Adapt Your Body: Mitigating Proprioception Shifts in Imitation Learning
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本文提出一种针对机器人模仿学习中的自感知状态偏差问题的解决方案,通过利用部署过程中的运行数据,通过Wasserstein距离量化专家和运行自感知状态间的差异,并添加噪声以减小差距,提高学习性能。

arXiv:2506.23944v2 Announce Type: replace-cross Abstract: 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.

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机器人模仿学习 自感知状态 领域适应 Wasserstein距离 分布偏差
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