Robot Learning

Hierarchical Policy

A hierarchical policy is a control architecture in which a high-level policy selects subgoals, skills, or latent commands at a coarse timescale, and one or more low-level policies translate them into motor actions at a fine timescale. Modern instantiations pair a slow vision-language model for reasoning with a fast reactive controller, as in dual-system architectures like Figure's Helix and NVIDIA GR00T N1.

Why it matters for physical AI

Splitting deliberation from reflexes lets large models reason at a few hertz while motor control runs at hundreds, making long-horizon tasks tractable and inference budgets manageable on real robots.