A Review on Reinforcement Learning for Motion Planning of Robotic Manipulators

Abstract

Effective motion planning is an indispensable prerequisite for the optimal performance of robotic manipulators in any task. In this regard, the research and application of reinforcement learning in robotic manipulators for motion planning have gained great relevance in recent years. The ability of reinforcement learning agents to adapt to variable environments, especially those featuring dynamic obstacles, has propelled their increasing application in this domain. Notwithstanding, a clear need remains for a resource that critically examines the progress, challenges, and future directions of this machine learning control technique in motion planning. This article undertakes a comprehensive review of the landscape of reinforcement learning, offering a retrospective analysis of its application in motion planning from 2018 to the present. The exploration extends to the trends associated with reinforcement learning in the context of serial manipulators and motion planning, as well as the various technological challenges currently presented by this machine learning control technique. The overarching objective of this review is to serve as a valuable resource for the robotics community, facilitating the ongoing development of systems controlled by reinforcement learning. By delving into the primary challenges intrinsic to this technology, the review seeks to enhance the understanding of reinforcement learning’s role in motion planning and provides insights that may suggest future research directions in this domain.