Sim-to-real robotic control via dynamics randomization

AI Impact Summary

This change highlights a capability to apply dynamics randomization for sim-to-real transfer in robotic control. It implies training policies with randomized physical parameters (e.g., mass, inertia, friction, delays) to improve real-world robustness, which can reduce real-robot tuning later. Teams should update their simulation pipelines to support parameter randomization and establish real-world validation gates to quantify transfer performance across varied robots and tasks. Expect increased compute and data requirements during training, with careful calibration of randomization ranges to avoid degrading policy performance.