Robotics and autonomous systems often perform well in controlled environments but struggle once deployed in real-world conditions. One of the primary reasons is a lack of high-quality, real-world video data during training. Simulated and synthetic datasets are valuable, but they cannot fully capture the unpredictability, variability, and nuance of human behavior in physical environments.
Video data provides temporal context that static images cannot. Movement patterns, timing, object interaction, and spatial awareness all unfold over time. Without exposure to authentic motion and behavior, models tend to overfit to idealized scenarios and fail when confronted with real human activity.
