Current as Touch: Proprioceptive Contact Feedback for Compliant Dexterous Manipulation
2026-07-03 • Robotics
Robotics
AI summaryⓘ
The authors developed a way for robot hands to feel and adjust their grip without needing extra touch sensors. They use the motor's electric current and joint positions, which naturally show how much force the robot is using when holding things. Instead of directly measuring force, their method predicts the best joint position to keep a safe and stable grip. This approach works well with existing robot control methods and helps robots handle delicate or tricky tasks more safely and effectively. They tested it on different robot hands and tasks, showing that it improves grip control without extra hardware.
proprioceptionmotor currentcompliancedexterous manipulationjoint statesPD controllergrasp stabilityforce sensingteleoperationpolicy learning
Authors
Chenyang Ma, Yunchao Yao, Zhenyu Wei, Ruogu Li, Daniel Szafir, Mingyu Ding
Abstract
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.