Modern robotics systems increasingly rely on multiple data modalities to understand their surroundings, interpret complex environments, and perform physical tasks with precision.

Synthetic data helps scale robot training by generating diverse, controllable scenarios, reducing physical data collection demands, enabling edge-case training, and improving embodied AI performance through simulation and real-world data.