arXiv · cs.AI · Yang Wang, Hongxuan Liu, Xinghui Xu, Arjun Menon, Xiaoran Cai, Yunyu He, Alex Tarvo, Jingzong Zhou, Mengzhong Ma, Xinpeng Wei, Yi Yu, Shaobo Wang, Cheng Peng, Aoran Jiao, Alexei Korolev, Yanyan Zhang, Kai Ye, Xinpeng Li, Chengquan Guo, Jingjing Fu, Nicholas Bai, Yongjun He, Junru Ren, Silei Ren, Mohamad Louai Shehab, Keshu Cai, Nathaniel Dennler, Traian Tus, Gaoyue Zhou, Abdullah Garra, Mason Nakamura, George Ortiz, Marius Urbanas, Lars Johannsmeier, Rohit Sharma, Suraj Deshmukh, Michael Spranger, Vinayak Gupta, Xu Chen, Ashis G. Banerjee, Yuxiang Feng, Yoshua Bengio, Peng Qi · 5 小时前模型评分41
论文提出可信物理智能框架 T-PAIO 与 T-PAI 原则
Towards Trustworthy Physical Intelligence: From Theory to Practice Across Life Cycle
导读
一篇 arXiv 论文针对物理智能提出可信物理智能操作化框架（T-PAIO，Trustworthy Physical Intelligence Operationalization）和可信物理智能（T-PAI）原则，覆盖物理智能全生命周期的五个核心阶段。
分类置信度
中 · 待人工复核
- 存在未解决的相邻类别：embodied-ai，需要人工复核。
- 只有单一媒体来源，需要人工复核。
来源级别：媒体与个人
事件类型：研究发布
证据摘录
- “To address this gap, our paper develop a foundation for understanding, operationalizing, and governing trustworthy physical intelligence.”
来源：arXiv · cs.AI · arxiv.org