Dexterous grasping depends on contact regulation, not motion alone. Stable manipulation requires fingers to maintain appropriate object loading as contacts slip, deform, or become visually occluded. Existing cross-embodiment dexterous policies unify motion through retargeted hand poses or latent actions, but force feedback remains tied to each hand's sensing and actuation, limiting transfer. This work introduces a cross-embodiment force-position interface for contact-aware manipulation across heterogeneous dexterous hands. Motion intent is represented in a shared hand-pose latent, while each hand's effort signal is calibrated through system identification into physical joint torque in N.m. These torques are mapped to fingertip forces and compact per-finger load descriptors, giving the policy comparable observations of where the hand should move and how the object is loaded. Using this interface, a flow-matching visuomotor policy is trained on vision, proprioception, and calibrated contact, with structured visual masking that encourages reliance on force under grasp-relevant occlusion. The same calibrated signal drives a hybrid force-position controller for demonstration collection and execution, keeping force targets consistent across training and deployment. Experiments across structurally different hands show that calibrated contact feedback enables transferable compliant grasping, with learned primitives reusable in long-horizon manipulation pipelines.

翻译：灵巧抓取的关键在于接触调控，而非单纯的运动控制。稳定的操作要求手指在接触滑动、变形或视觉遮挡时，维持适当的物体载荷。现有跨本体的灵巧策略通过重定向手部姿态或隐式动作统一运动，但力反馈仍受限于各手部的传感与驱动能力，从而制约了策略迁移。本研究提出一种跨本体力-位接口，实现异构灵巧手间的接触感知操作。运动意图以共享的手部姿态隐变量表示，同时通过系统辨识将各手部的驱动力信号校准为物理关节力矩（单位：N·m）。这些力矩被映射为指尖力及紧凑的指端载荷描述符，使策略获得关于手部运动目标与物体载荷状态的等效观测。基于该接口，训练一个流匹配视觉运动策略，输入信号包括视觉、本体感知及校准后的接触信息，并通过结构化视觉遮蔽机制，在抓取相关遮挡条件下增强策略对力的依赖性。同一校准信号驱动混合力-位控制器，用于示范采集与执行，确保训练与部署阶段的力指令一致性。针对结构差异显著的多款手部进行的实验表明，校准后的接触反馈可实现可迁移的柔顺抓取，且学习得到的基本动作可复用于长时操作任务链。