Deep reinforcement learning-based rehabilitation robot trajectory planning with optimized reward functions

• Published in: Advances in mechanical engineering Vol. 13; no. 12; p. 168781402110670
• Main Authors: Wang, Xusheng, Xie, Jiexin, Guo, Shijie, Li, Yue, Sun, Pengfei, Gan, Zhongxue
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.12.2021; Sage Publications Ltd; SAGE Publishing
• Subjects: Convergence; Deep learning; Efficiency; Optimization; Rehabilitation; Rehabilitation robots; Robots; Trajectory planning; Working conditions; Rehabilitation robot; deep reinforcement learning; reward function; trajectory planning
• ISSN: 1687-8132; 1687-8140; 1687-8140
• DOI: 10.1177/16878140211067011

Deep reinforcement learning (DRL) provides a new solution for rehabilitation robot trajectory planning in the unstructured working environment, which can bring great convenience to patients. Previous researches mainly focused on optimization strategies but ignored the construction of reward functions, which leads to low efficiency. Different from traditional sparse reward function, this paper proposes two dense reward functions. First, azimuth reward function mainly provides a global guidance and reasonable constraints in the exploration. To further improve the efficiency, a process-oriented aspiration reward function is proposed, it is capable of accelerating the exploration process and avoid locally optimal solution. Experiments show that the proposed reward functions are able to accelerate the convergence rate by 38.4% on average with the mainstream DRL methods. The mean of convergence also increases by 9.5%, and the percentage of standard deviation decreases by 21.2%–23.3%. Results show that the proposed reward functions can significantly improve learning efficiency of DRL methods, and then provide practical possibility for automatic trajectory planning of rehabilitation robot.