Real Environments Expose Edge Cases Early

Lighting changes, partial occlusions, background motion, and environmental noise all influence how AI systems interpret the world. Video captured in real environments exposes these challenges during training instead of after deployment, reducing costly retraining cycles.

This is particularly relevant for navigation and spatial awareness models, where even small perception errors can cascade into system-level failures.