Reality Gap

The "reality gap" refers to the discrepancies between simulated and real-world environments, hindering the transfer of knowledge learned in simulation to real-world applications, particularly in robotics and autonomous systems. Current research focuses on bridging this gap using techniques like domain randomization, meta-learning, and physics-aware machine learning, often incorporating neural networks (including transformers and graph neural networks) to improve model robustness and generalization. Successfully closing this gap is crucial for accelerating the development and deployment of AI-powered systems in diverse fields, from autonomous driving and robotics to quantum computing and planetary exploration.