Reinforcement Learning Methods for Assistive and Rehabilitation Robotic Systems: A Survey

Abstract

Advancements in robotic systems aimed at improving mobility for individuals with disabilities have required more sophisticated control and navigation methods. Traditional control approaches often lack the complexity and adaptability needed for the high-dimensional nature of human activities. Consequently, reinforcement learning (RL) has emerged as a dynamic and effective framework for managing robotic actions in complex and unpredictable human environments. This article reviews the integration of RL in robotic systems for enhancing the mobility of individuals with disabilities, addressing the limitations of traditional control methods in complex and unpredictable environments. We critically analyze various RL algorithms, discussing their advantages and challenges in assistive and rehabilitation applications. The study highlights the ongoing development of these algorithms, presenting current research directions, future prospects, and key challenges to achieving higher autonomy in assistive robots. Our findings underscore the potential of RL to improve adaptability and effectiveness in robotic control and navigation, offering insights into advancing these technologies for practical implementations.