Beyond Verbs and Trajectories: Physical Action Primitives through Local Interaction Laws

Similar actions need not yield reusable experience: interactions with similar action labels, trajectories, and nominal outcomes can respond differently when interventions change. We introduce Physical Action Primitives (PAPs), reusable action knowledge objects that preserve local interaction laws and how physical responses vary with intervention. A PAP retains this relationship as a persistent physical operator, supporting outcome prediction and intervention selection for new requests and contexts. Its explicit physical interface allows execution implementations to be replaced or calibrated while the response knowledge is retained. Independent physical audits in matched manipulation contexts show that PAP-based organization reduces audit discrepancy area by 26.7% relative to semantic organization and 19.1% relative to trajectory organization. Closely matched results from affine and nonlinear constructions support this organizing principle across model classes. Retained PAPs predict responses beyond their source probes, guide repeated invocation, and carry source information whose controlled exchange changes control accuracy. Real-robot experiments on a Franka Panda demonstrate prediction and invocation from real interaction evidence without real-world fine-tuning. These results establish a basis for robot experience to accumulate as libraries of persistent physical operators that planning, execution, and adaptation can access through a common interface. Code: https://anonymous.4open.science/r/PAP-B093/