Robot Learning

Policy Learning

Policy learning is the umbrella term for acquiring robot control policies from data rather than hand-engineering them, spanning reinforcement learning from trial and error, imitation learning from demonstrations, and offline learning from logged experience. It contrasts with classical model-based control in optimizing behavior end to end from sensors to actions, though hybrid systems that learn components within engineered stacks are common.

Why it matters for physical AI

Learning policies from data is the enabling idea of physical AI: it replaces task-by-task engineering with data collection and training, making capability scale with datasets and compute rather than expert time.