Foundation Models

Tokenization

Tokenization is the conversion of raw data into discrete tokens that a transformer can consume, spanning subword text tokens, image patches, and, in robotics, discretized states and actions. Robot policies tokenize continuous actions by binning each dimension, as in RT-1 and RT-2, or by learning compressed action codes with methods like FAST used in pi0-style models, letting a single sequence model treat perception, language, and control uniformly.

Why it matters for physical AI

How actions and observations are tokenized governs the precision, inference speed, and cross-embodiment flexibility of robot foundation models, and action tokenization schemes are an active design axis distinguishing current vision-language-action architectures.