Blind Precise Assembly: Proprioceptive Sim-to-Real Peg-in-Hole for Diverse Parts
Abstract
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 for precise, contact-rich assembly, where the robot must find a millimeter-scale opening, mesh gear teeth, or catch a thread through light contact? We study this question by training blind assembly policies that observe only a short history of joint positions, velocities, and their own actions, without visual, force, torque, or tactile sensing. The poses of both the fixture and the part are unknown to the policy, and the grasp is randomized substantially. The policy must therefore assemble the parts through contact, sensed exclusively through its own proprioception. Trained with reinforcement learning entirely in simulation over randomized parts, grasps, and contacts, such policies learn peg insertion, gear meshing, and nut threading. The peg insertion policy transfers zero-shot to the real world and inserts pegs of different shapes and sizes in 73% of trials. Controlled comparisons with matched visual policies in simulation show that blind policies generalize far better to unseen part shapes and sizes, though they can be slower. Training such policies needs thousands of different assemblies, each simulated with its 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. Videos and interactive simulations are at https://younghyopark.me/blind-proprioceptive-assembly/.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper demonstrates that proprioceptive sensing alone is sufficient for precise, contact-rich robotic assembly tasks like peg insertion, gear meshing, and nut threading, achieving zero-shot sim-to-real transfer and superior generalization to unseen parts compared to visual policies. By introducing a rigorous simulation framework with exact convex collision geometry and a novel contact-supervised RL training method, the authors provide a robust, sensor-light alternative to vision-based manipulation, significantly advancing the state of the art in blind dexterity and sim-to-real transfer for precision tasks.
Methodology
The paper proposes a novel approach to precise robotic assembly using only proprioceptive sensing (joint positions, velocities, and actions), explicitly excluding vision, force, torque, or tactile sensors. The core method involves training Reinforcement Learning (PPO) policies in simulation with extensive domain randomization of part geometry, grasp, and contact properties. A key technical contribution is the "contact supervision" auxiliary loss, which trains the policy's latent representation to predict contact states (free, touching, aligned) without providing privileged information to the actor, thereby accelerating learning from sparse rewards. Additionally, the authors introduce "AssemblyGen," an open-source procedural generator for MuJoCo that creates exact convex collision geometries for tight-clearance assemblies (pegs, gears, nuts), addressing a significant simulation bottleneck where standard convex decomposition or SDF methods fail to preserve sub-millimeter clearances accurately.
Results & evaluation
The experimental evaluation is rigorous and comprehensive. The authors demonstrate that blind policies achieve near-perfect success rates on in-distribution tasks and, crucially, generalize significantly better to unseen part shapes and sizes compared to matched visual policies. The zero-shot sim-to-real transfer is a strong result, with the peg insertion policy achieving 73% success on real hardware without fine-tuning, including on out-of-distribution pegs. The ablation studies effectively isolate the contribution of the contact supervision loss and the collision geometry generator. The comparison against scripted spiral search baselines highlights the efficiency of the learned reactive search strategy.
Reproducibility
The paper provides high reproducibility standards. It releases an open-source generator for the simulation assets, which is a major barrier in this field. Detailed hyperparameters, architecture descriptions, and training ranges are provided in the appendix. The use of standard simulators (MuJoCo) and common algorithms (PPO) further aids reproducibility. The specific hardware setup (Flexiv Rizon 4S, Robotiq 2F-85) is clearly defined.
Limitations
The primary limitation is the assumption that the robot starts in contact with the fixture; the policy does not handle the initial approach phase from a distance. The behavior is local and cannot recover from large deviations (e.g., if the part slips out of the grasp). The real-world evaluation is limited to peg insertion; gear meshing and nut threading were only validated in simulation. The reliance on precise simulation of friction and contact dynamics, despite domain randomization, remains a potential source of sim-to-real gap for other tasks.
Broader impact
This work challenges the prevailing assumption that precise manipulation requires vision or force sensing. By demonstrating that proprioception alone is sufficient for millimeter-scale assembly, it opens new avenues for robust, low-cost robotic systems that do not require expensive sensors or complex calibration. The released simulation tools will likely benefit the broader robotics community working on contact-rich tasks. The finding that blind policies generalize better to unseen geometries than visual policies is a significant insight for the design of robust manipulation policies. The paper demonstrates that proprioceptive sensing alone is sufficient for precise, contact-rich robotic assembly tasks like peg insertion, gear meshing, and nut threading, achieving zero-shot sim-to-real transfer and superior generalization to unseen parts compared to visual policies. By introducing a rigorous simulation framework with exact convex collision geometry and a novel contact-supervised RL training method, the authors provide a robust, sensor-light alternative to vision-based manipulation, significantly advancing the state of the art in blind dexterity and sim-to-real transfer for precision tasks.
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