The programming of robotic assembly tasks is a key component in manufacturing and automation. Force-sensitive assembly, however, often requires reactive strategies to handle slight changes in positioning and unforeseen part jamming. Learning such strategies from human performance is a promising approach, but faces two common challenges: the handling of low part clearances which is difficult to capture from demonstrations and learning intuitive strategies offline without access to the real hardware. We address these two challenges by learning probabilistic force strategies from data that are easily acquired offline in a robot-less simulation from human demonstrations with a joystick. We combine a Long Short Term Memory (LSTM) and a Mixture Density Network (MDN) to model human-inspired behavior in such a way that the learned strategies transfer easily onto real hardware. The experiments show a UR10e robot that completes a plastic assembly with clearances of less than 100 micrometers whose strategies were solely demonstrated in simulation.

翻译：机器人装配任务的编程是制造与自动化中的关键环节。然而，力敏装配通常需要反应式策略以应对定位的细微偏差及不可预见的零件卡滞。从人类操作中学习此类策略是一种有前景的方法，但面临两个常见挑战：低零件间隙的处理（难以从示教中捕获）以及离线学习直观策略（无需接触真实硬件）。我们通过从易获取的离线数据（通过游戏杆在无机器人仿真环境中的人类示教）学习概率力控制策略来解决这两个挑战。我们结合长短期记忆网络（LSTM）与混合密度网络（MDN）对人类启发性行为进行建模，使所学策略能轻松迁移至真实硬件。实验表明，UR10e机器人成功完成了间隙小于100微米的塑料零件装配，其策略完全基于仿真示教。