Recent Advances in Reinforcement Learning for Robotics

Abstract

This paper surveys recent advances in reinforcement learning (RL) techniques for robotic control tasks. We analyze the application of deep RL algorithms to manipulation, locomotion, and autonomous navigation problems, highlighting key challenges and promising research directions.

Introduction

Reinforcement learning has emerged as a powerful framework for developing robotic control policies that can adapt to complex, uncertain environments. Recent advances in deep reinforcement learning have enabled robots to learn sophisticated behaviors from high-dimensional sensory inputs.

Key Contributions

• Review of model-based and model-free RL approaches in robotics
• Analysis of sim-to-real transfer techniques
• Comparison of sample efficiency across different algorithms
• Case studies of successful real-world applications

Conclusion

While significant progress has been made in applying RL to robotics, challenges remain in sample efficiency, safety, and generalization. Future work should focus on developing algorithms that can learn effectively from limited data and transfer seamlessly to real-world scenarios.