OATH：面向灵巧手的物体无关跟踪，实现基于物理的快速重定向
OATH: Object-Agnostic Tracking for Dexterous Hands, Enabling Rapid Physics-based Retargeting
AI总结：
本研究提出OATH，一种基于残差学习的物体无关灵巧手跟踪方法，可实现快速基于物理的重定向，在DexYCB数据集上保持低跟踪误差，计算效率远高于传统采样方法。
摘要：
将人类演示重定向为动态可行的灵巧手轨迹需要解决复杂的接触物理问题，但现有方法要么忽略物理约束，要么依赖仿真中计算成本高昂的基于采样的优化。强化学习此前已被用于通过接触动力学实现规划与控制；然而，标准策略需要针对特定物体进行训练，无法在任意几何结构间泛化。本研究将基于物理的重定向重新表述为残差学习问题，证明跨物体的零样本泛化依赖于结构化状态表示。具体而言，将观测空间设置为包含局部接触误差信号，该信号引导每个机器人连杆到达物体表面的目标坐标，从而使单一、统一的残差策略能够成功抓取新颖、未见过的物体几何结构。在仿真中基于运动学参考轨迹展开该策略，得到基于物理的重定向器OATH。OATH每1秒参考轨迹的计算耗时为0.4秒，实现了2.4倍于实时的性能，与基于采样的方法相比计算成本降低60至350倍。该系统恢复了58%的预留物体DexYCB轨迹和54%的未见过物体DexYCB轨迹，同时保持低于2厘米的跟踪误差。
英文摘要：
Retargeting human demonstrations into dynamically feasible dexterous hand trajectories requires resolving complex contact physics, yet current methods either omit physical constraints or rely on computationally expensive sampling-based optimization in simulation. Reinforcement learning has previously been used to achieve planning and control through contact dynamics; however, standard policies require object-specific training and fail to generalize across arbitrary geometries. This work reframes physics-based retargeting as a residual learning problem, where we demonstrate that zero-shot generalization across objects relies on structured state representations. Specifically, formulating the observation space to include a localized contact-error signal guiding each robot link to its target coordinate on the object surface enables a single, unified residual policy to successfully grasp novel, unseen object geometries. Rolling out this policy in simulation on a kinematic reference trajectory yields a physics-based retargeter, OATH. Operating at 0.4 seconds of compute per second of reference, OATH achieves 2.4x faster-than-real-time performance and a 60-350x reduction in computational cost compared to sampling-based methods. The system recovers 58% of held-out and 54% of unseen-object DexYCB trajectories while maintaining a sub-2 cm tracking error.
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