Robotic Arm Motion Planning Based on Residual Reinforcement Learning

Abstract

The application of reinforcement learning algorithms to motion planning is a research hotspot in robotics in recent years. However, training reinforcement learning agents from scratch has low training efficiency and difficulty in convergence. In this paper, a robot motion planning method based on residual reinforcement learning is proposed. This method divides the agent's policy of motion planning into initial policy and residual policy. The initial policy is composed of a neural network motion planner responsible for guiding the training direction of residual policy. The residual policy is composed of Proximal Policy Optimization (PPO) algorithm in reinforcement learning. A motion planning experiment is carried out in a simulation environment, and the result shows that the method can successfully perform motion planning. The comparison experiment between PPO and the proposed algorithm demonstrates that the proposed algorithm has better motion planning performance.