Foundation Models

Vision-Language-Action Model (VLA)

A vision-language-action model (VLA) is a foundation model that maps camera observations and natural-language instructions directly to robot actions, typically created by fine-tuning a pretrained vision-language model on large robot demonstration datasets. RT-2 established the paradigm by expressing actions as text tokens; subsequent models such as OpenVLA and Pi0 train on cross-embodiment corpora like Open X-Embodiment and add continuous action heads such as flow matching.

Why it matters for physical AI

VLAs are the leading candidate architecture for general-purpose robot policies, transferring web-scale semantic knowledge into control and improving predictably with more diverse robot data, which reframes manipulation as a data-scaling problem.