让机器人模仿人类操作时的受力，实现更精准的抓取与操控。
ReForce: Learning Force-aware Retargeting for Dexterous Manipulation
- 基于人类动作和受力数据，预测需修正的残差动作以匹配目标受力。
- 在纸杯抓取、夹子操作等任务中，受力追踪误差更低，多指接触更强。
- 适用于实时遥操作和离线数据转换，提升复杂操作的可迁移性。
人类示范为灵巧操作提供了可扩展的数据来源，但由于实体差异，将示范转化为机器人动作仍具挑战。当前的重定向方法多基于运动学，但操作本质由受力决定，受力影响手与物体的交互方式及物体运动。本文提出 ReForce，一种力感知重定向方法，将人类运动与受力转化为能复现预期接触的机器人动作。ReForce 在运动学重定向基础上，通过一个大规模仿真训练的通用力跟踪器预测残差动作，以达到目标受力。该方法支持在线力感知遥操作与离线数据转换。在仿真与真实硬件上，ReForce 在纸杯抓取、夹子操作等高接触任务中均实现了更低的力追踪误差，并增强了多指接触稳定性。
原文摘要 · Abstract (English)
Human demonstrations offer a scalable data source for dexterous manipulation, but transferring them to robot actions remains challenging due to the embodiment gap. Today's retargeting is mostly kinematic, yet manipulation is decided by force, which governs how the hand interacts with the object and how the object moves. In this paper, we present ReForce, a Force-aware Retargeting method that turns human motion and forces into robot actions that reproduce the intended contact. ReForce predicts a residual on the kinematically retargeted action to reach the desired force, using a general force tracker trained on large-scale simulation interactions. It supports both online force-aware teleoperation and offline data translation. In simulation and on real hardware, ReForce achieves lower force-tracking error and stronger multi-finger contact engagement on contact-rich tasks such as paper-cup grasping and tongs manipulation.
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