A Governance Architecture for Safe and Bounded Autonomous Robotics

This paper introduces a governance architecture for safe and bounded autonomous robotics, addressing fundamental challenges in controlling and constraining behavior in AI-driven physical systems. Traditional approaches to safety rely on design-time validation and assumptions about predictable system behavior. As systems become adaptive and learning-enabled, these assumptions break down. The proposed architecture establishes a structured control model in which all system behavior is governed through three distinct layers:(1) Capability Governance(2) Non-Bypassable Execution Control(3) Runtime Safety Enforcement Detailed architectural modules are provided in the following related works: Non-Bypassable Execution Control in Autonomous SystemsCapability Lifecycle Governance in Autonomous SystemsSafety-Bounded Autonomy in Distributed Robotic Systems At the core of this architecture lies non-bypassable execution control, which enforces all system actions at runtime and ensures that no behavior can bypass defined safety constraints. This work further elaborates the architectural structure by defining a three-layer model: Capability Governance LayerDefines and constrains what a system is authorized to execute. Capabilities are explicitly specified, bounded, and version-controlled, forming a formal contract between design-time intent and runtime behavior. Non-Bypassable Execution Control LayerTranslates approved capabilities into executable actions under strict control logic. This layer ensures that pre-authorized and validated actions are enforced at execution, preventing unauthorized behavior even in adaptive or learning systems. Safety Enforcement LayerActs as a non-bypassable supervisory layer that continuously monitors system behavior at runtime and overrides or halts execution if safety, ethical, or operational constraints are violated. A central principle of the architecture is the strict separation between capability definition and execution authority, ensuring that learning, adaptation, or external inputs cannot directly result in uncontrolled actions. This separation is reinforced by non-bypassable control mechanisms, making it structurally impossible for the system to execute actions outside its governed capability space. The architecture is particularly relevant for AI-driven, learning-enabled, and distributed autonomous systems, where traditional control approaches become insufficient due to system complexity and autonomy. By combining concepts from systems architecture, control theory, and governance design, this work provides a scalable framework for building trustworthy autonomous platforms. It establishes a foundation for future developments in areas such as autonomous industrial systems, human–robot interaction, and safety-critical AI applications. This work is part of the Robotics Governance Architecture (RGA) research series, which develops a layered architectural framework for capability governance, non-bypassable execution control, and runtime safety enforcement as foundational principles for safe, bounded, and verifiable autonomous systems. This architecture provides a foundational model for the development of controllable, trustworthy, and safety-bounded autonomous systems.