人間の遠隔操作データから学ぶ、シミュレーション基盤の双腕ロープ操作ポリシー
Learning Sim-Grounded Policies for Bimanual Rope Manipulation from Human Teleoperation Data
この論文は、結び目を解くタスクにおいて、視覚ベースのポリシーよりも物理状態ベースのポリシーの方が汎化性能が高いことを示し、観測空間の選択が重要であることを明らかにした。
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著者: Gina Wigginghaus, Tim Missal, Berk Guler, Simon Manschitz, Jan Peters
分類: cs.RO, cs.AI
原文アブストラクト
Deformable Linear Objects (DLOs) such as ropes and cables are widely encountered in both household and industrial applications, yet remain challenging to manipulate due to their infinite-dimensional configuration space and frequent self-occlusion. Imitation learning from teleoperation offers a practical path to bimanual DLO manipulation, but its scalability is limited by human effort, making the choice of observation space critical for generalization from small datasets. In this study, we investigate whether the lack of generalization in egocentric visual policies for the knot-untangling task stems from the observation space itself, rather than from the policy architecture or data scale. We compare two Action Chunking with Transformers policies trained on the same bimanual teleoperation data: a vision-based policy conditioned on two egocentric RGB streams from wrist-mounted cameras, and a state-based policy conditioned on the DLO's 3D particle state, extracted from an initial observation via multi-view fusion and evolved in a particle-based eXtended Position-Based Dynamics simulation. Evaluated open-loop on an unseen rope configuration, the state-based policy outperforms its visual counterpart with a 30.8% reduction in L1 error when predicting the initial grasp-and-pull action, quantifying the observability gap between pixels and physics-consistent state, and pointing toward more data-efficient robot learning for the DLO manipulation task from limited human demonstrations.
