The rapid advancement of embodied intelligence and world models has intensified efforts to integrate physical laws into AI systems, yet physical perception and symbolic physics reasoning have developed along separate trajectories without a unified bridging framework. This work provides a comprehensive overview of physical AI, establishing clear distinctions between theoretical physics reasoning and applied physical understanding while systematically examining how physics-grounded methods enhance AI's real-world comprehension across structured symbolic reasoning, embodied systems, and generative models. Through rigorous analysis of recent advances, we advocate for intelligent systems that ground learning in both physical principles and embodied reasoning processes, transcending pattern recognition toward genuine understanding of physical laws. Our synthesis envisions next-generation world models capable of explaining physical phenomena and predicting future states, advancing safe, generalizable, and interpretable AI systems. We maintain a continuously updated resource at https://github.com/AI4Phys/Awesome-AI-for-Physics.
@article{arxiv.2510.04978,
title = {Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI},
author = {Kun Xiang and Terry Jingchen Zhang and Yinya Huang and Jixi He and Zirong Liu and Yueling Tang and Ruizhe Zhou and Lijing Luo and Youpeng Wen and Xiuwei Chen and Bingqian Lin and Jianhua Han and Hang Xu and Hanhui Li and Bin Dong and Xiaodan Liang},
journal= {arXiv preprint arXiv:2510.04978},
year = {2026}
}