Advancements in Deep Reinforcement Learning and Inverse Reinforcement Learning for Robotic Manipulation: Toward Trustworthy Interpretable and Explainable Artificial Intelligence

Abstract

This article presents a literature review of the past five years of studies using Deep Reinforcement Learning (DRL) and Inverse Reinforcement Learning (IRL) in robotic manipulation tasks. The reviewed articles are examined in various categories including DRL and IRL for perception assembly manipulation with uncertain rewards multitasking transfer learning multimodal and Human-Robot Interaction (HRI). The articles are summarized in terms of the main contributions methods challenges and highlights of the latest and relevant studies using DRL and IRL for robotic manipulation. Additionally summary tables regarding the problem and solution are presented. The literature review then focuses on the concepts of trustworthy AI interpretable AI and explainable AI (XAI) in the context of robotic manipulation. Moreover this review provides a resource for future research on DRL/IRL in trustworthy robotic manipulation.