电流即触觉：用于柔顺灵巧操作的本体感受接触反馈
Current as Touch: Proprioceptive Contact Feedback for Compliant Dexterous Manipulation
AI总结：
研究提出基于本体感受的框架，从电机电流和关节状态学习接触感知线索，预测柔顺参考位置，兼容主流遥操作和策略学习流程，实验表明无需外部触觉或力传感器也能实现稳定柔顺抓取等。
摘要：
柔顺性对灵巧操作至关重要，但现有解决方案常依赖昂贵、易碎且难部署的外部触觉或力传感器。我们提出一个由本体感觉驱动的框架，该框架从电机电流和关节状态中学习接触感知的柔顺线索。由于电机电流与执行器扭矩密切相关，它提供了一个无需额外传感硬件就能感知接触力、物体阻力和抓握稳定性的内在信号。我们的方法不是估计外部扳手或命令扭矩，而是预测一个柔顺参考位置：一个标准PD控制器的理想关节位置目标，其引起的位置误差会产生适当的抓握力。这种基于位置的公式与主流的遥操作和策略学习管道兼容，同时使机器人能够根据实时本体感觉反馈调整相互作用力。因此，电机电流不仅用作力的代理，还用作可学习的本体感觉接触信号，用于柔顺参考预测。在多个灵巧手和接触丰富的任务上进行的实验，包括易碎物体处理、持续表面接触、薄物体检索和动态负载适应，展示了稳定的柔顺抓握、更安全高效的遥操作，以及在没有外部触觉或力传感器的情况下改进的下游策略学习。
英文摘要：
Compliance is essential for dexterous manipulation, yet existing solutions often rely on external tactile or force sensors that are costly, fragile, and difficult to deploy on low-cost robot hands. We propose a proprioception-driven framework that learns contact-aware compliance cues from motor current and joint states. Since motor current is closely related to actuator torque, it provides an intrinsic signal for perceiving contact force, object resistance, and grasp stability without additional sensing hardware. Rather than estimating external wrenches or commanding torque, our method predicts a compliance reference position: an ideal joint-position target for a standard PD controller whose induced position error generates appropriate grasping force. This position-based formulation is compatible with mainstream teleoperation and policy-learning pipelines, while enabling the robot to adapt interaction forces from real-time proprioceptive feedback. Thus, motor current serves not only as a force proxy but also as a learnable proprioceptive contact signal for compliance reference prediction. Experiments on multiple dexterous hands and contact-rich tasks, including fragile object handling, sustained surface contact, thin-object retrieval, and dynamic load adaptation, show stable compliant grasping, safer and more efficient teleoperation, and improved downstream policy learning without external tactile or force sensors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
