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.

翻译：高混合低批量（HMLV）工业装配在中小型企业中十分常见，它既需要达到大批量自动化所要求的高精度、安全性与可靠性，又必须保持对产品差异和环境不确定性的灵活适应能力。现有机器人系统难以同时满足这些要求。手动编程方法适应性差且调整成本高昂，而基于学习的方法则在样本效率低下以及高接触性任务中的不安全探索方面存在不足。为此，我们提出了SHaRe-RL，一个能够利用多种先验知识的强化学习框架。通过（i）将技能结构化为操作基元，（ii）整合人类示范与在线修正，以及（iii）利用轴向顺应性约束交互力，SHaRe-RL能够为长时程、高接触性的工业装配任务实现高效安全的在线学习。在间隙为0.2-0.4毫米的工业Harting连接器模块插入任务上的实验表明，SHaRe-RL能够在实际时间预算内实现可靠的性能。我们的研究结果表明，无需机器人学或强化学习专业知识，仅凭工艺经验便能对学习过程做出实质性贡献，从而推动强化学习在工业装配中实现更安全、更鲁棒且更具经济可行性的部署。