  Industrial robot manipulators are not able to match the precision and speed
with which humans are able to execute contact rich tasks even to this day.
Therefore, as a means overcome this gap, we demonstrate generative methods for
imitating a peg-in-hole insertion task in a 6-DOF robot manipulator. In
particular, generative adversarial imitation learning (GAIL) is used to
successfully achieve this task with a 10 um, and a 6 um peg-hole clearance on
the Yaskawa GP8 industrial robot. Experimental results show that the policy
successfully learns within 20 episodes from a handful of human expert
demonstrations on the robot (i.e., < 10 tele-operated robot demonstrations).
The insertion time improves from > 20 seconds (which also includes failed
insertions) to < 15 seconds, thereby validating the effectiveness of this
approach.
