VAE

Variational Autoencoder — a generative model that learns a probabilistic latent space by encoding inputs into a distribution (mean + variance) and decoding samples from this distribution. The ELBO objective balances reconstruction quality and latent space regularity. VAEs are used in robot learning for: latent state representation, ACT policy architecture (CVAE encoder), and generative world models.