让机器人安全高效完成高精度工业装配任务
SHaRe-RL: Structured, Interactive Reinforcement Learning for Contact-Rich Industrial Assembly Tasks
- 将操作技能分解为可复用的模块，结合人类示范与实时修正
- 在0.2-0.4毫米间隙下实现可靠插入，训练时间符合工业实际需求
- 无需机器人或强化学习背景，工程师经验即可助力模型优化
高混合低批量（HMLV）工业装配在中小企业中常见，需兼具高精度、安全性和可靠性，同时适应产品变化与环境不确定性。现有机器人系统难以满足要求：人工编程难维护且成本高，基于学习的方法在接触密集任务中样本效率低且探索不安全。为此，本文提出SHaRe-RL框架，融合多源先验知识：（i）将技能结构化为操作基元，（ii）引入人类示范与在线修正，（iii）通过轴向柔顺性约束交互力。实验在具有0.2–0.4 mm间隙的工业Harting连接器插入任务上验证，SHaRe-RL可在合理时间预算内实现稳定性能。结果表明，工艺经验（无需机器人或强化学习知识）能有效促进学习，推动强化学习在工业装配中的更安全、鲁棒与经济部署。
原文摘要 · Abstract (English)
High-mix low-volume (HMLV) industrial assembly, common in small and medium-sized enterprises (SMEs), requires the same precision, safety, and reliability as high-volume automation while remaining flexible to product variation and environmental uncertainty. Current robotic systems struggle to meet these demands. Manual programming is brittle and costly to adapt, while learning-based methods suffer from poor sample efficiency and unsafe exploration in contact-rich tasks. To address this, we present SHaRe-RL, a reinforcement learning framework that leverages multiple sources of prior knowledge. By (i) structuring skills into manipulation primitives, (ii) incorporating human demonstrations and online corrections, and (iii) bounding interaction forces with per-axis compliance, SHaRe-RL enables efficient and safe online learning for long-horizon, contact-rich industrial assembly tasks. Experiments on the insertion of industrial Harting connector modules with 0.2-0.4 mm clearance demonstrate that SHaRe-RL achieves reliable performance within practical time budgets. Our results show that process expertise, without requiring robotics or RL knowledge, can meaningfully contribute to learning, enabling safer, more robust, and more economically viable deployment of RL for industrial assembly.
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