Robot Learning

Primitive Actions

Primitive actions are the low-level building-block behaviors from which longer robot activities are composed, ranging from atomic motor commands to parameterized skills such as reach, grasp, push, or screw. Formulations include dynamic movement primitives that encode motions as stable dynamical systems, options in hierarchical reinforcement learning, and skill libraries invoked by task planners or, increasingly, by language-model-based high-level policies.

Why it matters for physical AI

Choosing the action abstraction is a core design decision for robot foundation models: composing proven primitives improves reliability and sample efficiency, while end-to-end low-level control preserves flexibility that fixed primitives lose.