Physical AI: Concepts, Techniques, and Challenges

Authors: Weinan Zhang , Chunhua Shen , Tianfan Xue , Bin Zhao , Bingqi Jiang , Zihan Guo , Zicai Cui , Yang Li , Ming Zhou , Xinyuan Chen , Lihe Ding , Xiao Chen , Yujin Wang , Shi Guo , Junting Dong , Tao Lu , Xing Shen , Haoyu Guo , Yao Mu , Haiyong Yu , Haoxiang Ma , Fuxian Huang , Qiaojun Yu , Yunsong Zhou , Kailin Li , Zirui Wang , Yufei Xue , Weishuai Zeng , Tao Huang , Zhigang Wang , Junli Liu , Pengyuan Wu , Pingrui Zhang , Haotian Wang , Dongzhan Zhou , Jiafei Cao , Feng Yuan , Xinyi Ye , Quanli Xuan , Mulin Yu , Shangding Gu , Kaiwen Song , Dong Wang , Jiarui Li , Kehui Liu , Zhongjie Jia , Dongjiang Li , Jiacheng Bao , Haoran Yang , Zhaoqing Chen , Yiming Sun , Bowen Zhou

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Abstract: In recent years, physical artificial intelligence (physical AI) has experienced rapid development, capturing significant attention in academia and industry. However, the field has yet to reach consensus on its definition, technical landscape, and core challenges. To address this gap, this monograph seeks to define physical AI, present its technical landscape, and examine each key technical subfield in depth. In doing so, it establishes a unified framework for Physical AI: artificial systems that perceive, model, and act upon the physical world through continuous feedback under real-world constraints. Further, this monograph distinguishes physical AI from conventional digital AI, embodied AI, cyber-physical systems, and digital twins. It develops the foundations of physical AI across physics, control, cognition, learning, and reasoning. It reviews key methodologies, including multimodal perception, physical data and simulation, world models, embodied foundation models, motor-control systems, multi-agent coordination, and hardware-software co-design. Furthermore, it examines applications spanning robotics, manufacturing, infrastructure, scientific experimentation, and autonomous services. Particular attention is given to sim-to-real transfer, reliability, real-time execution, evaluation, safety, governance, and long-term deployment. By connecting perception, prediction, decision-making, action, and adaptation in a closed loop, this monograph identifies the technical challenges and research directions required to advance from narrow automation toward more generalizable physical AI and, ultimately, physical artificial general intelligence (Physical AGI).

Comments:

Monograph, 12 chapters, 388 pages, 64 figures

Subjects:

Artificial Intelligence (cs.AI) ; Robotics (cs.RO)

Cite as:

arXiv:2610.27045 [cs.AI]

(or

arXiv:2610.27045v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.27045

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arXiv-issued DOI via DataCite (pending registration)

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From: Weinan Zhang [ view email ]

[v1]

Mon, 19 Oct 2026 17:50:11 UTC (22,937 KB)

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