Latent Action Representation

Compressing high-dimensional robot actions into a compact latent space using a VAE or VQ-VAE, then training policies to predict latent codes rather than raw actions. Latent action representations reduce the effective action dimensionality, making policy learning easier and enabling better modeling of multi-modal distributions. Used in language-conditioned and video-predicted robot learning.