From Structural Dynamics to Policy: Structural Intelligence as a Design Framework for Robots

Current AI systems are largely optimized for coherence and user satisfaction, yet embodied robots must survive constraint, friction, delayed costs, and irreversible consequences. This paper proposes Structural Intelligence (SI) as a design framework for robots by translating the SI corpus into an engineering architecture: contact-coupled correction, answerability, binding, intrusion resistance, and invariance constraints. I treat structural dynamics as the governing layer of agency under pressure and derive a module-level mapping from SI variables to robot control components. Intrusion is defined as occupancy of action selection by non-sovereign subpolicies, including reward hacking, adversarial inputs, and goal drift. I argue that robust robotic agency requires a cockpit function for metacontrol, durable learning with trace, and non-tradable orientation constraints that prevent total programmability. The paper concludes with evaluation criteria and falsifiable predictions: intrusion-resistant robots should demonstrate lower relapse into reward hacking, faster correction under real constraint, and improved stability of behavior across environments and social manipulation attempts.