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arXiv AI 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

Towards Trustworthy Physical AI: From Theory to Practice Across Life Cycle

Read the original on arXiv AI →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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