Sim to Real Transfer

Sim-to-real transfer aims to bridge the gap between simulated and real-world environments for training robots and other autonomous agents, enabling efficient learning without extensive real-world data collection. Current research focuses on improving transferability through techniques like domain randomization, model-based and model-free reinforcement learning, and the use of generative adversarial networks (GANs) and transformers for improved representation learning and handling of uncertainties. Successful sim-to-real transfer significantly accelerates the development and deployment of autonomous systems across various applications, from robotic manipulation and locomotion to autonomous driving and surgical robotics.