We added extra experiments in simulation to evaluate the best-performing policy in environments with unseen obstacles. Here the pdf file describes the experiment design and shows the experimental settings and results in a figure and a table. A brief analysis of the results has been provided. We have also attached a video capturing part of the testing process in Gazebo

Luo, Sha

Schomaker, Lambert

English

Luo Sha

Schomaker Lambert

NEUROSURGERY ENTHUSIASTIC WOMEN SOCIETY

Supplementary material for paper: Reinforcement Learning in Robotic Motion Planning by Combined Experience-based Planning and Self-Imitation Learning

High-quality and representative data is essential for both Imitation Learning (IL)- and Reinforcement Learning (RL)-based motion planning tasks.For real robots, it is challenging to collect enough qualified data either asdemonstrations for IL or experiences for RL due to safety consideration inenvironments with obstacles. We target this challenge by proposing the selfimitation learning by planning plus (SILP+) algorithm, which efficiently embeds experience-based planning into the learning architecture to mitigate thedata-collection problem. The planner generates demonstrations based on successfully visited states from the current RL policy, and the policy improvesby learning from these demonstrations. In this way, we relieve the demandfor human expert operators to collect demonstrations required by IL and improve the RL performance as well. Various experimental results shows thatSILP+ achieves better training efficiency, higher and more stable successrate in complex motion planning tasks compared to several other methods.Extensive tests on physical robots illustrate the effectiveness of SILP+ in aphysical setting, retaining a success rate of 90% where the next-best contender drops from 87% to 75% in the Sim2Real transition.<br/

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