Residual reinforcement learning for logistics cart transportation. (18th April 2022)

• Record Type: Journal Article
• Authors: Matsuo, Ryosuke; Yasuda, Shinya; Kumagai, Taichi; Sato, Natsuhiko; Yoshida, Hiroshi; Yairi, Takehisa
• Is Part Of: Advanced robotics. Volume 36:Number 8(2022)
• Page Start: 404
• Page End: 421
• Publication Date: 2022-04-18
• Subjects: Reinforcement learning -- logistics; Robotics -- Periodicals; Robotics -- Japan -- Periodicals; Robotics; Japan; Periodicals; 629.89205
• DOI: 10.1080/01691864.2022.2046504
• Languages: English
• ISSNs: 0169-1864

Abstract

Abstract : Autonomous logistics cart transportation is a challenging problem because of the complicated dynamics of the logistics cart. In this paper, we tackle the problem by using two robots system with reinforcement learning. We formulate the problem as the problem of making a logistics cart track an arc trajectory. Our reinforcement learning (RL) controller consists of a feedback controller and residual reinforcement learning. The feedback controller regards a logistics cart as a virtual leader and robots as followers, and the robots' position and velocity are controlled to maintain the formation between the logistics cart and the robots. Residual reinforcement learning is used to modify the other model's output. Simulation results showed that the residual reinforcement learning controller trained in a physical simulation environment performed better than other methods, especially under the condition with a large trajectory curvature. Moreover, the residual reinforcement learning controller can be transferred to a real-world robot without additional learning in a real-world environment. GRAPHICAL ABSTRACT: UF0001