arXiv Robotics — research abstracts· Zhenhua Yang, Jiafeng Liang, Xingyu Zhou, Yi Jiang, Xiayu Cao, Zui Chen, Luqi Wang, Haiyang Sun, Yufei Xiang, Chang Li, Rongchuan Mu, Bowen Chi, Zihe Tong, Mingyu Li, Huijie Ma, Yiqiao Yang, Baoqi Ren, Zhiming Ma, Zhihao Zhu, Jiahao Ji, Zhuoyao Wang, Qianyu Wang, Xiangyu Liu, Wang Zixiang, Shunliang Zhang, Ming Liu, Xiaocheng Feng, Duyu Tang, Bing Qin·· 1 days agoEditorial score63
Proactive Physical AI through Continuous Observation and Timely Intervention
Proactive Physical AI through Continuous Observation and Timely Intervention
Summary
This research introduces ProAgent, an end-to-end system for Proactive Physical AI that continuously observes the physical world and initiates timely interventions. The system combines a proactive timing model (StreamingJev) with a Response Model for physical execution, supported by an automated data pipeline for training. Experiments show improved intervention timing and reduced false triggers compared to baseline methods.
Source: arXiv Robotics — research abstracts · Read original article ↗
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Source：arXiv Robotics — research abstracts · arxiv.org