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 interacting with dynamic environments, and coexisting 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, demonstrating 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.
Blogger's Review: The governance framework for Physical AI provides crucial guidance for future technological development, emphasizing the integration of safety and societal values. The design of its five-stage lifecycle greatly enriches the operationality of governance practices, contributing to a more comprehensive technical standard system.