Robot Learning

Learned Reward

A learned reward is a reward function acquired from data rather than hand-engineered, typically inferred from demonstrations via inverse reinforcement learning, from human preference comparisons, or from success classifiers trained on labeled outcomes. Vision-language models are increasingly used to produce reward signals zero-shot by scoring how well observations match a task description. The learned reward then supervises reinforcement learning or trajectory ranking.

Why it matters for physical AI

Hand-specifying rewards for contact-rich, real-world tasks is brittle and slow; learned rewards let robots improve autonomously from raw experience, a key ingredient for scaling reinforcement learning beyond simulation.