Sim to Real Learning

Sim-to-real learning aims to train robots in simulation and then successfully deploy those learned skills in the real world, overcoming the inherent discrepancies between simulated and real environments. Current research focuses on improving the robustness of this transfer, employing techniques like generative adversarial networks (GANs) for domain adaptation, reinforcement learning (RL) for policy optimization, and representation learning to extract transferable skills. This approach significantly reduces the need for extensive real-world data collection, accelerating robotic development and enabling safer, more efficient training for complex tasks like manipulation, locomotion, and navigation.