外观
论文arXiv 2610.1696716 天前
通过持续观察与及时干预实现主动式 Physical AI
原文链接：Proactive Physical AI through Continuous Observation and Timely Intervention（arXiv 2610.16967 · 发布日期：2026-10-12）
Proactive Physical AI through Continuous Observation and Timely Intervention
提出ProAgent，通过持续观察、主动时机模型与响应模型，在实验和真机评估中实现及时物理干预。
核心要点
- 许多真实场景中，重要任务会在用户明确表达前从动态状态中出现。
- ProAgent结合StreamingJev与响应模型，判断是否和何时干预并生成回应或调用设备。
- 自动数据管线将视频转为主动时机监督信号，支持规模化学习。
- 实验显示其较源骨干基线干预时机更准、误触发更少；真机评估展示物理执行能力。
验证范围与局限：验证范围包括显式目标、常设角色下的实验和真机评估，摘要未说明具体任务、设备数量或机器人型号。
英文摘要 (Abstract)
Physical AI is commonly formulated around an explicitly specified task: given a goal or instruction, a system perceives, reasons, and acts. In many real-world settings, however, important tasks emerge from continuously changing world states before a person explicitly formulates them. We study this complementary problem as Proactive Physical AI, where an AI system continuously observes the physical world, identifies situations that warrant intervention, and initiates appropriate actions at the right time. We develop ProAgent, an end-to-end system built around two components: a Jev-style proactive timing model (dubbed StreamingJev) that determines whether and when intervention is needed, and a Response Model that generates responses for people or invokes available device capabilities for physical execution. To support learning at scale, we develop an automated data pipeline that converts videos into supervision for proactive timing. Experiments demonstrate strong proactive performance under both explicit objectives and standing roles, combining more accurate intervention timing with fewer false triggers than the source-backbone baseline. Real-robot evaluations further demonstrate ProAgent's ability to identify emerging needs and translate them into appropriate physical actions.
作者：Zhenhua Yang, Jiafeng Liang, Xingyu Zhou, Yi Jiang, Xiayu Cao, Zui Chen, Luqi Wang, Haiyang Sun, Yufei Xiang, Chang Li
来源
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