Papers

Learning Reflexive Behavior for Contact-Rich Manipulation

This preprint introduces a learned proprioceptive reflex policy that sits between task-level commands and joint control. Trained in simulation on spring, plane, and rail interaction primitives, it keeps contact forces near thresholds when commands or geometry are imperfect; on real peg insertion with 0.02 mm clearance, it raises success from at most 22% to 36–58% and uses less than half the mean estimated contact force of IK+PD. The result matters for assembly, humanoid lifting, and surface following as a reusable low-level execution layer under planners, learned policies, and teleoperation.