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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 to 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 <tex>$6 \mu\mathrm{m}$</tex> 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., &#x003C; 10 tele-operated robot demonstrations). The insertion time improves from &#x003E; 20 seconds (which also includes failed insertions) to &#x003C; 15 seconds, thereby validating the effectiveness of this approach.

Imitation Learning for High Precision Peg-in-Hole Tasks