Robot Learning

Discriminator

Discriminator is the component of an adversarial learning system trained to distinguish real data from generated or agent-produced data, as introduced in generative adversarial networks (Goodfellow et al., 2014). In robot learning, discriminators serve as learned reward functions: Generative Adversarial Imitation Learning (GAIL) rewards the agent for producing state-action pairs the discriminator cannot tell apart from expert demonstrations, and Adversarial Motion Priors apply the same idea to naturalistic locomotion styles.

Why it matters for physical AI

Adversarially learned rewards sidestep manual reward engineering, letting legged robots and characters acquire lifelike, demonstration-consistent behavior from modest motion datasets.