Robot Learning

GAN (Generative Adversarial Network)

A GAN is a generative model architecture, introduced by Goodfellow et al. (2014), in which a generator network learns to produce samples that a jointly trained discriminator network cannot distinguish from real data, framed as a two-player minimax game. GANs produced the first high-fidelity neural image synthesis and spawned variants like CycleGAN for unpaired image translation. In robotics they have been used for sim-to-real image adaptation, data augmentation, and adversarial imitation.

Why it matters for physical AI

Although diffusion and flow models now dominate generation, adversarial training remains influential in robotics for domain adaptation and imitation, where matching distributions matters more than likelihood.