Robotics · 2026-10-21
Physical AI: Concepts, Techniques, and Challenges
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
AI-assisted summary based on the paper. Consult the linked source for methods and limitations. Benchmark numbers appear after editor verification.
TL;DR
The authors define Physical AI as an artificial system’s capacity to perceive physical states, maintain task-relevant representations of physical relationships and action consequences, and select/execute interventions via physical interfaces using feedback under physical, operational, and computational constraints. They frame Physical AI as a persistent closed loop (perception→modeling→prediction→decision/planning/control→execution→feedback) and contrast it with digital AI, embodied AI, CPS, and digital twins.
Why it matters
Physical AI is positioned as a system-level capability layer for closed-loop interaction with real physical processes, not a specific model architecture. The monograph organizes key components (state perception, world models, decision/planning & control, and closed-loop feedback/adaptation) and highlights evaluation needs beyond prediction accuracy, emphasizing closed-loop performance, reliability, real-time execution, safety, governance, and long-term deployment.
Method
- Defines Physical AI as closed-loop intervention using feedback to achieve objectives under physical, operational, and computational constraints.
- Frames Physical AI as a cycle: perception, modeling, prediction, decision-making, execution, and feedback.
- Organizes core elements as: physical state perception/estimation, physical world models, decision-making with planning & control, and closed-loop feedback with multiscale adaptation.
Limitation
The monograph states that physical AI has yet to reach consensus on its definition, technical landscape, and core challenges.
Abstract (from arXiv)
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).
Related papers
- Towards Agentic Robots: Model, Data, Environment, and Harness
- Gym-Anything for Robotics: Scaling physical simulations with digital agents
- Proactive Physical AI through Continuous Observation and Timely Intervention
- RobotWorld: Benchmarking Multimodal Agents for Robot Use Across Diverse Tasks and Embodiments
- Recursive Self-Improvement in Physical AI: Mechanisms, Evidence, and Evaluation
Publication record
Bibliographic evidence describes where and when a record appeared. It does not validate scientific claims.
First public release: 2026-10-19 · Indexed by Pub-AI: 2026-10-21 UTC
Physical AI: Concepts, Techniques, and Challenges
Preprint posted: 2026-10-19 · Original record ↗
- arxiv
- 2610.27045
Explicit source versions (1)
- v1 · 2026-10-19
- Preprint available
Source date: 2026-10-19 · First detected: 2026-10-25 UTC · Historical baseline
Source evidence (3)
- Official evidence ↗ · OAI arXiv created; not header datestamp (official metadata)
- Official evidence ↗ · atom:published (official metadata)
- Official evidence ↗ · atom:published (official metadata)
Abstract · arxiv
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).