Paper Overview
Field: Machine Learning Authors: Weinan Zhang, Chunhua Shen, Tianfan Xue, Bin Zhao, and many others (Shanghai Jiao Tong University and collaborators) Published: 2026-10-22 arXiv: 2610.27045
Summary
In recent years, physical artificial intelligence (Physical AI) has experienced rapid development, capturing significant attention in both 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.
Unified Framework
The monograph 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.
Positioning of the Field
The work clearly distinguishes Physical AI from related but distinct concepts:
- Conventional digital AI
- Embodied AI
- Cyber-physical systems
- Digital twins
- Physics
- Control
- Cognition
- Learning and reasoning
- Multimodal perception
- Physical data and simulation
- World models
- Embodied foundation models
- Motion control systems
- Multi-agent coordination
- Hardware-software co-design
- Robotics
- Manufacturing
- Infrastructure
- Scientific experiments
- Autonomous services
- Simulation-to-real (sim-to-real) transfer
- Reliability
- Real-time execution
- Evaluation and benchmarking
- Safety and governance
- Long-term deployment
Foundations
It develops the foundations of Physical AI across multiple layers:
Key Methodologies Reviewed
Applications
Covered application domains include:
Critical Topics
The monograph pays special attention to:
Conclusion
By connecting perception, prediction, decision-making, action, and adaptation into a closed loop, the monograph identifies the technical challenges and research directions required to move from narrow automation toward more generalizable Physical AI — and ultimately Physical AGI.
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*Auto-collected on 2026-10-22*