  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.
