A Robot Pick and Place Skill Learning Method Based on Maximum Entropy and DDQN Algorithm. Issue 1 (1st February 2022)

• Record Type: Journal Article
• Authors: Wu, Peiliang; Zhang, Yan; Li, Yao; Mao, Bingyi; Chen, Wenbai; Gao, Guowei
• Is Part Of: Journal of physics. Volume 2203:Issue 1(2022)
• Publication Date: 2022-02-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2203/1/012063
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Pick and place (PAP) skill learning is a fundamental ability of intelligent robots, such as home service robot. Due to the NP-hard nature of the PAP problem, it takes a long time for an intelligent robot to learn the PAP skill based on current methods. In order to improve the learning efficiency of robot PAP skills, this paper proposes a Soft-DDQN-based PAP skill learning method. Firstly, the Soft-DDQN is proposed by introducing maximum entropy into robot DDQN framework, and the learning goal of Soft-DDQN is to maximize reward and information entropy. Secondly, PAP problem is modelled as a discrete form and Soft-DDQN is applied to solve the PAP problem. Finally, in order to verify the efficiency of Soft-DDQN-based PAP skill learning, comparisons have been given from two standard perspectives and shown that Soft-DDQN improves efficiency of PAP skill learning evidently.