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.
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