Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI

• URL: http://arxiv.org/abs/2510.04978v3
• Date: Sun, 19 Oct 2025 02:43:11 GMT
• Title: Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI
• Authors: Kun Xiang, Terry Jingchen Zhang, Yinya Huang, Jixi He, Zirong Liu, Yueling Tang, Ruizhe Zhou, Lijing Luo, Youpeng Wen, Xiuwei Chen, Bingqian Lin, Jianhua Han, Hang Xu, Hanhui Li, Bin Dong, Xiaodan Liang,
• Abstract summary: We advocate for intelligent systems that ground learning in both physical principles and embodied reasoning processes.<n>Our synthesis envisions next-generation world models capable of explaining physical phenomena and predicting future states.
• Score: 57.44526951497041
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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