论文/arXiv 2610.23849·12 天前

盲精密装配：面向多样零件的本体感知 Sim-to-Real 插销入孔

研究仅凭关节本体感知的盲装配策略，用仿真强化学习训练，并在多类装配仿真和真机插销中验证。

AI 整理

• 硬件零部件
• 本体 SDK 与控制
• 仿真与训练

原文链接：Blind Precise Assembly: Proprioceptive Sim-to-Real Peg-in-Hole for Diverse Parts（arXiv 2610.23849 · 发布日期：2026-10-16）

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

研究仅凭关节本体感知的盲装配策略，用仿真强化学习训练，并在多类装配仿真和真机插销中验证。

核心要点

• 问题在于精密接触装配是否可不依赖视觉、力矩或触觉，仅用关节感知完成。
• 方法使用短时关节位置、速度和动作历史，在随机零件、抓取和接触的仿真中强化学习。
• 策略学会插销、齿轮啮合和螺母穿线；真机零样本插入不同形状尺寸插销成功率为73%。
• 仿真对比显示盲策略对未见形状和尺寸泛化更好但可能更慢，并开源MuJoCo装配生成器。

验证范围与局限：真机验证仅摘要报告插销入孔；齿轮啮合和螺母穿线为仿真训练结果。

英文摘要 (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/.

作者：Antonia Bronars, Younghyo Park, Kushal S. Kedia, Pulkit Agrawal

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2610.23849：Blind Precise Assembly: Proprioceptive Sim-to-Real Peg-in-Hole for Diverse Parts访问 2026-10-21