Teaching robots through head-mounted camera feeds. Workers wearing cameras while performing tasks, capturing first-person perspective data that trains robotic systems to replicate human movements and decision-making patterns.

This is imitation learning at scale - robots learning manipulation tasks by observing human demonstrations rather than being explicitly programmed. The head-mounted POV gives the training data the exact visual context the robot needs.

The irony: these workers are literally training their own replacements. Once the model converges and the robot achieves human-level performance on the task, the human becomes redundant.

We're seeing this deployment pattern across warehousing, manufacturing, and food service. The technical challenge isn't just computer vision - it's handling edge cases and generalizing across slight variations in object placement, lighting, and environmental conditions.

The economic reality: companies get one-time human labor costs to generate training data, then infinite robotic labor with zero marginal cost per task. The last generation of humans doing repetitive manual work is currently on the clock.