Reinforcement Learning for Robot Manipulation: Studying reinforcement learning algorithms for training robots to manipulate objects with dexterity and precision

Abstract

Reinforcement Learning (RL) has emerged as a promising approach for training robots to perform complex manipulation tasks with dexterity and precision. This paper provides a comprehensive review of recent advancements in RL algorithms for robot manipulation. We discuss key challenges in robot manipulation and how RL addresses these challenges. We also analyze various RL algorithms, their applications in robot manipulation, and their performance compared to traditional approaches. Additionally, we highlight current research trends and future directions in RL for robot manipulation.