Physical AI Governance: From Theory to Practice Across Life Cycle

2026-07-24

AI Summary

Existing AI governance frameworks struggle to address the unique challenges posed by embodied intelligence in real-time safety, dynamic interaction, and human-AI coexistence. This work proposes the first five-stage, full-lifecycle governance model specifically tailored for embodied intelligence—spanning research, design, data, development, and deployment—and systematically integrates governance principles into engineering practice. Through a systematic literature review, principle-to-practice mapping, lifecycle modeling, and case studies, the study develops an actionable implementation guide that bridges the gap between abstract governance theory and practical engineering execution. The resulting framework offers researchers, developers, and policymakers a structured reference to advance the development of embodied intelligent systems that are safe, trustworthy, and aligned with societal values.

Abstract

With the emergence of Physical AI, artificial intelligence is extending beyond screen-based applications to embodied systems that perceive, interact with, and act in the physical world. Unlike traditional AI, Physical AI operates under real-time safety constraints, continuously interacts with dynamic environments, and coexists with humans, introducing governance challenges that existing AI governance frameworks do not explicitly address. This paper presents a comprehensive survey of Physical AI governance from both scientific and operational perspectives. We synthesize existing governance principles and organize them into a unified governance framework tailored to physical AI systems. Building on this foundation, we propose a five-stage Physical AI lifecycle comprising research, design, data, model development, and deployment, and demonstrate how governance can be operationalized across each stage through concrete implementation practices. By connecting governance principles with engineering workflows, this survey provides a structured reference for researchers, developers, and policymakers to build Physical AI systems that are safe, trustworthy, and aligned with societal values.

Problem

• Physical AI
• AI governance
• safety constraints
• human-AI coexistence
• dynamic environments

Innovation

• Physical AI
• AI governance
• lifecycle framework
• operationalization
• trustworthy AI