Sim to Real

Sim-to-real transfer aims to bridge the gap between simulated and real-world environments for training robots and other autonomous systems. Current research focuses on improving the realism of simulations through techniques like domain randomization and residual physics models, as well as developing robust algorithms for transferring learned policies from simulation to real-world deployment, often employing reinforcement learning and contrastive learning methods. This research is crucial for accelerating the development of robust and reliable autonomous systems across various applications, from robotic surgery and manipulation to autonomous driving, by significantly reducing the reliance on expensive and time-consuming real-world data collection.