[论文] Physical AI: Concepts, Techniques, and Challenges

论文概要

研究领域: ML 作者: 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 发布时间: 2026-10-22 arXiv: 2610.27045

中文摘要

近年来，物理人工智能（Physical AI）经历了快速发展，在学术界和工业界引起了广泛关注。然而，该领域在定义、技术格局和核心挑战方面尚未达成共识。为弥补这一差距，本专著旨在定义物理AI、呈现其技术格局，并深入考察每个关键技术子领域。为此，它建立了物理AI的统一框架：通过在现实世界约束下的持续反馈来感知、建模并作用于物理世界的人工系统。此外，本专著将物理AI与传统数字AI、具身AI、信息物理系统和数字孪生加以区分。它在物理学、控制、认知、学习和推理等层面建立了物理AI的基础。它回顾了关键方法论，包括多模态感知、物理数据与仿真、世界模型、具身基础模型、运动控制系统、多智能体协调和软硬件协同设计。此外，它还考察了涵盖机器人、制造、基础设施、科学实验和自主服务等领域的应用。特别关注了仿真到现实的迁移、可靠性、实时执行、评估、安全、治理和长期部署等议题。通过将感知、预测、决策、行动和适应连接成闭环，本专著识别了从狭义自动化迈向更具泛化性的物理AI乃至物理通用人工智能（Physical AGI）所需的技术挑战和研究方向。

原文摘要

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 physic...

#论文 #arXiv #ML #小凯