A Reinforcement Learning-Based Framework for Robot Manipulation Skill Acquisition
Dong Liu, Zitu Wang, Binpeng Lu, Ming Cong, Honghua Yu, Qiang Zou
Abstract
This paper studies robot manipulation skill acquisition based on a proposed reinforcement learning framework. Robot can learn policy autonomously by interacting with environment with a better learning efficiency. Aiming at the manipulator operation task, a reward function design method based on objects configuration matching (OCM) is proposed. It is simple and suitable for most Pick and Place skills learning. Integrating robot and object state, high-level action set and the designed reward function, the Markov model of robot manipulator is built. An improved Proximal Policy Optimize algorithm with manipulation set as the output of Actor (MAPPO) is proposed as the main structure to construct the robot reinforcement learning framework. The framework combines with the Markov model to learn and optimize the skill policy. A same simulation environment as the real robot is set up, and three robot manipulation tasks are designed to verify the effectiveness and feasibility of the reinforcement learning framework for skill acquisition.