AI Capability Governance Framework for Autonomous Robotic Systems

Autonomous robotic systems increasingly integrate artificial intelligence capabilities operating in dynamic and safety-critical environments. As robot fleets scale and learning-based behaviors are deployed in real-world environments, new governance mechanisms are required to ensure safe capability deployment and operational compliance. This paper proposes a capability-centric governance architecture for autonomous robotic systems. The framework introduces a lifecycle-based capability management model enabling controlled deployment, authorization, and runtime supervision of AI capabilities across robotic fleets. The proposed architecture separates governance policies, execution authorization mechanisms, and runtime safety enforcement layers. This separation allows scalable governance of robotic capabilities while maintaining safety and policy compliance in distributed robotic systems. A formal model of capability governance is introduced, including a lifecycle state model, authorization function, runtime safety constraints, and a fleet governance model. The architecture provides a conceptual foundation for capability governance in future autonomous robotic platforms. The work contributes to the emerging intersection of robotics systems engineering, AI safety, and autonomous systems architecture, and outlines future research directions for scalable governance of learning-enabled robotic systems.