为物理人工智能设计全生命周期治理框架，确保安全可信。
Physical AI Governance: From Theory to Practice Across Life Cycle
- 提出五阶段生命周期模型，覆盖研发到部署
- 将治理原则融入工程实践，实现可操作落地
- 适合研究者、开发者与政策制定者参考
随着物理人工智能的兴起，人工智能正从屏幕应用扩展至感知、交互并作用于物理世界的身体化系统。与传统AI不同，物理AI面临实时安全约束，持续与动态环境交互，并与人类共存，现有治理框架未能明确应对此类挑战。本文从科学与实践双重视角，系统综述物理AI治理。我们整合现有治理原则，构建适配物理AI系统的统一框架。基于此，提出涵盖研究、设计、数据、模型开发与部署的五阶段生命周期，并展示各阶段如何通过具体实践实现治理落地。通过连接治理原则与工程流程，本综述为研究人员、开发者及政策制定者提供了构建安全、可信且符合社会价值的物理AI系统的结构化参考。
原文摘要 · Abstract (English)
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.
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