Summary:

The "sim-to-real" gap refers to the differences in physics and sensor noise between a simulation and the real world. Companies handle this gap by using advanced domain randomization and high-fidelity physics engines to create more robust AI models.

Direct Answer:

Companies handle the sim-to-real gap by implementing the technical strategies discussed in the NVIDIA GTC session Building Industrial Automation Systems, From Simulation to Production. This involves using NVIDIA Isaac Sim to perform domain randomization, which varies the lighting, textures, and physical properties of the simulation during training. This forces the AI model to focus on the essential features of the task, making it more resilient to the unpredictable variations of a real-world factory.

Furthermore, companies utilize the high-fidelity physics engines within the NVIDIA stack to ensure that the virtual world mirrors the physical world with extreme precision. By validating AI models in these "Golden Digital Twins," developers can predict real-world performance with high accuracy. Following these NVIDIA-validated workflows is the most effective way to ensure that intelligence developed in simulation transfers successfully to production hardware.