Blind Precise Assembly: Proprioceptive Sim-to-Real Peg-in-Hole for Diverse Parts

Every robot already carries high-resolution contact sensors: its own joints. Legged robots and humanoids exploit them to walk, run, and track whole-body motions without vision, but there the contacts are large and forceful. Is joint sensing also enough enough for precise, contact-rich assembly, where the robot must find a millimeter-scale opening through light contact? We study this question by training a blind insertion policy that observes only a short history of joint positions, velocities, and its own actions, without visual, force, torque, or tactile sensing. The poses of both the hole and the peg are unknown to the policy, and the grasp is randomized substantially. The policy must therefore find the opening through contact, sensed exclusively through its own proprioception. We train this behavior with reinforcement learning entirely in simulation over randomized parts, grasps, and contacts, and it transfers zero-shot to a real robot and inserts pegs of different shapes and sizes in 74% of trials. Controlled comparisons with matched visual policies in simulation show that although blind policies are slower, they generalize far better to unseen part shapes and sizes. Training such policies needs thousands of different assemblies, each simulated with its millimeter clearance intact, so we also release an open-source generator that builds them procedurally from exact convex pieces for MuJoCo; it is more accurate and faster than convex decomposition or MuJoCo's signed-distance collision.