Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI
🤖 AI Summary
Current AI systems exhibit a fundamental disconnect between physics-aware perception and symbolic physical reasoning, hindering the deep integration of physical laws into artificial intelligence. To address this, we propose the first unified framework that bridges perception, reasoning, modeling, and interaction—thereby realizing a next-generation world model grounded in both physical priors and embodied causal inference. Our approach formally unifies *theoretical physics reasoning* (axiomatic, formal deduction) with *practical physics understanding* (causal modeling grounded in embodied interaction), systematically integrating symbolic reasoning, generative modeling, multimodal perception, and first-principles physics modeling. The resulting structured physics-AI knowledge system is released as an open-source resource library. Empirical evaluation demonstrates substantial improvements in model interpretability, cross-scenario generalizability, and physical consistency—establishing foundational progress toward safe, generalizable, and verifiable physics-integrated intelligence.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Bridging physical perception and symbolic reasoning in AI
Integrating physical laws into embodied intelligence systems
Developing unified frameworks for physics-grounded world models
Innovation
Methods, ideas, or system contributions that make the work stand out.
Integrating physical laws into AI systems
Bridging physical perception and symbolic reasoning
Developing physics-grounded world models for prediction
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