REINFORCEMENT LEARNING FOR ASSEMBLY ROBOTS: A REVIEW

This paper provides a comprehensive introduction to Reinforcement Learning (RL), summarizes recent developments that showed remarkable success, and discusses their potential implications for the field of robotics. RL is a promising approach to develop hard-to-engineer adaptive solutions for complex and diverse robotic tasks. In this paper RL core elements are reviewed, existing frameworks are presented, and main issues that are limiting the application of RL for real-world robotics, such as sample inefficiency, transfer learning, generalization, and reproducibility are discussed. Multiple research efforts are currently being directed towards closing the sim-to-real gap and accomplish more efficient policy transfers methods, making the agents/robots learn much faster and more efficiently. The focus of this work is to itemize the various approaches and algorithms that center around the application of RL in robotics. Finally, an overview of the current state-of-the-art RL methods is presented, along with the potential challenges, future possibilities, and potential development directions.