点击次数：

论文类型：期刊论文

发表时间：2020-01-01

发表刊物：IEEE ACCESS

收录刊物：SCIE

卷号：8

页面范围：108429-108437

ISSN号：2169-3536

关键字：Reinforcement learning; Robot sensing systems; Task analysis; Service robots; End effectors; Robot skill acquisition; reinforcement learning; reword function; MAPPO

摘要：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.