Fast programming of Peg-in-hole Actions by human demonstration

Abstract

Peg-in-Hole actions play an important role in industrial assembly. In this paper, we present a system which performs such actions after learning by human demonstration. Strategies for getting robust human-demonstrated PiH trajectories are investigated, and a number of PiH experiments are conducted to verify the performance of the proposed approach. The results show that our approach leads to a high PiH success rate and allows the robot to fine-tune the PiH trajectories to achieve faster performance.