从人类遥控数据中学习双臂绳子操作的模拟 grounded 策略
Learning Sim-Grounded Policies for Bimanual Rope Manipulation from Human Teleoperation Data
AI总结：
本文研究了基于视觉的策略在解结任务中泛化能力不足是否源于观察空间而非策略架构或数据规模，通过比较两种基于动作分块与变压器的策略，发现基于物理状态的策略在预测初始抓取和拉拽动作时L1误差降低了30.8%。
摘要：
可变形线性物体（DLOs）如绳子和电缆在家庭和工业应用中广泛存在，但因其无限维的配置空间和频繁的自我遮挡而难以操控。从遥控学习双臂DLO操控提供了实用路径，但其可扩展性受限于人力，使得观察空间的选择对从小数据集泛化至关重要。本文研究了在解结任务中基于视觉的策略泛化能力不足是否源于观察空间本身而非策略架构或数据规模。我们比较了两种基于动作分块与变压器的策略，均训练于相同双臂遥控数据：一种基于两个装在腕部相机上的眼动RGB流的视觉策略，另一种基于DLO的3D粒子状态的策略，该状态通过多视角融合从初始观察中提取，并在基于粒子的扩展位置基于动力学模拟中演化。在未见过的绳子配置上进行开环评估，基于状态的策略在预测初始抓取和拉拽动作时，L1误差减少了30.8%，量化了像素与物理一致状态之间的可观察性差距，并指出了从有限人类演示中更高效地学习DLO操控任务的机器人学习方向。
英文摘要：
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.
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