Robot Learning

Privileged Information

Privileged information is ground-truth state available during training but not at deployment, such as exact object poses, contact forces, terrain friction, or full maps available inside a simulator. The teacher-student paradigm exploits it: a teacher policy trained with privileged access supervises a student restricted to deployable sensors, as in Learning by Cheating for driving and Lee et al.'s 2020 quadruped controller that inferred terrain from proprioceptive history.

Why it matters for physical AI

Privileged training decouples what is hard to learn from what is hard to sense, and it underlies many of the most successful sim-to-real systems in locomotion and manipulation.