Robot Learning

Vector Quantization (VQ)

Vector quantization (VQ) is the mapping of continuous vectors to the nearest entry in a learned discrete codebook, popularized in deep learning by the VQ-VAE. In robot learning it is used to tokenize continuous observations and actions into discrete symbols so that transformer policies can model behavior with categorical prediction heads; VQ-BeT, for example, quantizes action chunks to capture multimodal demonstration data.

Why it matters for physical AI

Discretizing actions lets robot policies borrow the full machinery of language models, including next-token prediction and autoregressive sampling, which has become a dominant recipe in vision-language-action architectures.