Touch Where It Matters: Learning Local Tactile Policies for Fine-Grained Gripper Control

Overview & Synopsis

Visual imitation policies can organise task motion, yet contact-rich object use demands gripper adjustments beyond coarse opening and closing. We study fine-grained gripper control as a local feedback problem within ongoing task execution. Our approach assigns tactile feedback a dedicated control role during contact-rich task stages. We introduce ContactPolicy, a learned local controller that takes over the gripper while a visual base policy continues to control the arm. ContactPolicy combines tactile and force histories with current robot state to jointly predict gripper increments and their modulation, updating the gripper target from each new observation. An Intent policy manages control handover between the visual base policy and ContactPolicy. Real-world experiments on container opening, object handling, and tool operation show improved task success over both the Visuomotor Policy and Tactile-Force Policy baselines. Execution analysis illustrates how the gripper adjusts contact within an ongoing task, including periods of target retention and fine-grained changes as interaction conditions evolve. Arm-state ablations and failure analysis further examine how execution context supports local regulation. These results support task-local tactile policies for fine-grained gripper control within visual manipulation.