SD-PDMD: Deep Reinforcement Learning for Robotic Trajectory Imitation

Yue Li, Jiexin Xie, Yang Li, Shijie Guo

Abstract: Reinforcement Learning (RL) with Skill Diversity (SD) is an appealing method of imitating expert trajectory. Numeral papers have presented methods of SD in several imitation learning scenarios. However, it is still a gap between SD and the practical implementation of robot trajectory imitation. The poor performance of matching algorithm often leads to imitation failures as well. To bridge the gaps and remedy the drawbacks, this paper proposed a robotic trajectory imitation method SD-PDMD with no-reward function reinforcement learning. A RL frame for robot trajectory imitation with SD is constructed to generate potential imitation trajectories, and then an optimal similarity algorithm based on predefined similarity measurements (PDMD) is proposed to match the expert trajectory with the most similar generated trajectory. Extensive experiments illustrate that the proposed SD-PDMD can effectively complete the robot trajectory imitation task, the performance of PDMD for similarity matching is also better than the traditional Euclidean Distance with an improvement of 5.7%-9.2%. The code and video can be obtained at: https://github.com/Nursing-Robot-Laboratory/SD-PDMD