Robot Learning

Apprenticeship Learning

Apprenticeship learning is learning to perform a task from expert demonstrations by recovering the reward function the expert appears to optimize, then computing a policy against it, rather than mimicking actions directly. Formalized by Abbeel and Ng in 2004 via inverse reinforcement learning with feature matching, it can outperform the demonstrator and generalize beyond demonstrated states, at the price of solving repeated reinforcement learning subproblems.

Why it matters for physical AI

Reward-recovery approaches address a core weakness of behavior cloning, distribution drift, and their intellectual descendants inform modern preference-based and adversarial imitation methods used to shape robot behavior.