Robot Learning

Variational Autoencoder (VAE)

A variational autoencoder (VAE) is a generative model, introduced by Kingma and Welling in 2013, that learns a probabilistic latent representation of data by jointly training an encoder and decoder to maximize the evidence lower bound (ELBO). Conditional VAEs (CVAEs) are widely used in robot learning to model multimodal action distributions; the ACT policy from the ALOHA project, for example, uses a CVAE over action chunks.

Why it matters for physical AI

Demonstration data is inherently multimodal because humans solve the same task in different ways, and VAE-style latent variables let imitation policies capture that variability instead of averaging conflicting demonstrations into invalid actions.