The paper introduces the concept of Physical AI—systems that understand and act within the physical world, where interactions are continuous, uncertain, and irreversible. It surveys trustworthy principles specific to Physical AI, outlines the role of physics in AI, and maps the end‑to‑end life cycle across five core stages, culminating in the Trustworthy Physical AI Operationalization (T‑PAIO) and the broader Trustworthy Physical AI (T‑PAI) framework.
By Wang Yang, Hongxuan Liu, Xinghui Xu, Arjun Menon, Xiaoran Cai, Yunyu He, Jingzong Zhou, Mengzhong Ma, Xinpeng Wei, Nathaniel Dennler, Yi Yu, Shaobo Wang, Cheng Peng, Aoran Jiao, Alexei Korolev, Ashis G. Banerjee, Yanyan Zhang, Kai Ye, Xinpeng Li, Chengquan Guo, Jingjing Fu, Marius Urbonas, Traian Tus, Gaoyue Zhou, George Ortiz, Irmak Guzey, Silei Ren, Lars Johannsm eier, Rohit Sharma, Felix Feng, Yoshua Bengio, Peng Qi
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Building operations are energy-inefficient. Artificial Intelligence (AI)-driven control systems promise benefits through optimization and predictive control, but deploying them in real buildings revea...
Artificial general intelligence ultimately requires agents that can reason and act in the physical world. Action models, vision-language-action policies, and world models have advanced this goal, while World Action Models (WAMs) are particularly promising because they connect candidate interventions with predicted consequences.
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By Beena
arXiv:2609.37109v1 Announce Type: cross
Abstract: The rapid, unpredictable advancements in AI system capabilities has seen regulators take adaptive and experimental approaches to policymaking. Establ...
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