Sim2Real Gap

The "Sim2Real gap" refers to the discrepancy between the performance of machine learning models trained in simulated environments and their performance in the real world. Current research focuses on bridging this gap through techniques like physically-based simulation, domain randomization, and the use of natural language descriptions to create domain-invariant representations. These efforts aim to improve the transferability of models trained on synthetic data to real-world applications, particularly in robotics and autonomous systems. Overcoming the Sim2Real gap is crucial for accelerating the deployment of AI in safety-critical domains, where extensive real-world data collection is costly and potentially dangerous.