Natural Residual Reinforcement Learning for Bicycle Robot Control

Abstract

This work focuses on motion control of the bicycle robot by using the proposed NRRL algorithm. Unlike the traditional RL algorithm, decomposing the main tasks into subtasks manually and introducing qualitative prior knowledge to the agent have been applied in the NRRL algorithm. Simulation results show that better performance and better sample efficiency of the proposed NRRL algorithm have been achieved in terms of balance control and path tracking of bicycle robot. It's believed that the NRRL algorithm is available on the real physical bicycle robot, and the deployment of the algorithm will be realized soon, as the real physical bicycle robot has been constructed currently.