Robotic arm reinforcement learning control method based on autonomous visual perception

Abstract

The traditional robotic arm control methods are often based on artificially preset fixed trajectories to control them to complete specific tasks, which rely on accurate environmental models, and the control process lacks the ability of self-adaptability. Aiming at the above problems, we proposed an end-to-end robotic arm intelligent control method based on the combination of machine vision and reinforcement learning. The visual perception uses the YOLO algorithm, and the strategy control module uses the DDPG reinforcement learning algorithm, which enables the robotic arm to learn autonomous control strategies in a complex environment. Otherwise, we used imitation learning and hindsight experience replay algorithm during the training process, which accelerated the learning process of the robotic arm. The experimental results show that the algorithm can converge in a shorter time, and it has excellent performance in autonomously perceiving the target position and overall strategy control in the simulation environment.