  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 as
demonstrations for IL or experiences for RL due to safety considerations in
environments with obstacles. We target this challenge by proposing the
self-imitation learning by planning plus (SILP+) algorithm, which efficiently
embeds experience-based planning into the learning architecture to mitigate the
data-collection problem. The planner generates demonstrations based on
successfully visited states from the current RL policy, and the policy improves
by learning from these demonstrations. In this way, we relieve the demand for
human expert operators to collect demonstrations required by IL and improve the
RL performance as well. Various experimental results show that SILP+ achieves
better training efficiency higher and more stable success rate in complex
motion planning tasks compared to several other methods. Extensive tests on
physical robots illustrate the effectiveness of SILP+ in a physical setting.
