Reinforcement Learning in Robotics: Applications and Innovations

Abstract

Reinforcement learning (RL) has emerged as a powerful tool for enabling robots to learn from their environments through trial and error. By interacting with the world, robots can optimize their actions to achieve desired outcomes in complex, dynamic environments. This article explores the applications and innovations of reinforcement learning in robotics, highlighting its role in enhancing robotic autonomy, decision-making, and adaptability. Key areas such as robot manipulation, navigation, and human-robot interaction are discussed, alongside the challenges and future directions of RL in robotics. The article also examines the integration of RL with other machine learning techniques and real-world applications, ranging from industrial automation to healthcare.