Sim to Real Reinforcement Learning

Sim-to-real reinforcement learning (RL) aims to train robots in simulation and successfully transfer those learned behaviors to the real world, overcoming the "reality gap" between simulated and physical environments. Current research focuses on improving simulation fidelity using techniques like 3D Gaussian splatting for realistic rendering and incorporating tactile feedback for enhanced manipulation skills, often employing deep RL algorithms like PPO and leveraging teacher-student learning paradigms for more robust and sample-efficient training. This field is crucial for accelerating robotic development, enabling faster and cheaper training of complex tasks like assembly, locomotion, and manipulation, ultimately leading to more capable and adaptable robots.