Physical AI: A Data-Driven Survey of Foundations, Technologies, and Applications

Abstract

This paper presents a systematic, data-driven literature review of research on Physical Artificial Intelligence (AI) based on the top 100 Google Scholar publications related to the search terms “Physical Artificial Intelligence” and “Physical AI”. The rapid advancement of Physical AI, driven by the convergence of advanced sensor technologies and foundation world models, has resulted in a diverse and fragmented research landscape that lacks comprehensive quantitative overviews. To address this gap, we implement and apply an AI-assisted computational analysis pipeline to this domain. The collected publications are processed using a Large Language Model accessed via a Python-based Application Programming Interface (API), enabling a structured computational analysis of the literature to assist thematic categorization. Based on this approach, the publications are grouped into five data-driven thematic clusters reflecting primary research perspectives within the analyzed sample. Specifically, the identified clusters comprise “Sensor Infrastructure and Architectures”, “Core Learning and Modeling Methodologies”, “Sim-to-Real and Digital Twins”, “Applications”, and “Safety, Governance, and Ethics”. By synthesizing the literature in a structured manner, this work provides a consolidated overview of central research patterns, identifies key operational challenges, and highlights fragmentation across Physical AI research, establishing a solid foundation for future trustworthy autonomous systems.

The authors' abstract, as published at the source. Technologies, 2026 · DOI ↗