Foundation Models

Action Head

An action head is the output module of a learned robot policy that maps backbone features to executable actions. Designs vary widely: discrete token heads that emit binned actions autoregressively (as in RT-2), regression heads producing continuous values, and generative heads based on diffusion or flow matching that sample from multimodal action distributions. The choice of head strongly affects inference speed, action fidelity, and the ability to represent multiple valid behaviors.

Why it matters for physical AI

Head architecture is one of the most consequential design decisions in vision-language-action models, determining whether a policy can express multimodal strategies and run within real-time control budgets.