Driving · 2026-10-14
Proactive Physical AI through Continuous Observation and Timely Intervention
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
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TL;DR
The authors study Proactive Physical AI, where an AI system continuously observes the physical world, detects when intervention is needed, and initiates timely responses or device actions without a predefined task. They develop ProAgent with StreamingJev (proactive timing) and a Response Model (person/device responses), plus an automated video-to-supervision pipeline for training. Experiments show better intervention timing and fewer false triggers than a source-backbone baseline, including real-robot results.
Why it matters
Timely intervention is a core requirement for physical AI in settings where tasks emerge from evolving world states before any explicit instruction. ProAgent separates intervention timing from response generation, enabling continuous observation while producing responses or executing actions. The paper reports improved intervention timing (F1/PA/PT) and time-aware response quality (Outcome/PAUC), plus embodied robot evaluation across different assistance categories.
Method
- Develop ProAgent: StreamingJev (Jev-style proactive timing) predicts whether/when to intervene, while a Response Model generates person-facing text or typed device function calls; models run asynchronously so observation continues during response/execution.
- Create an event-driven automated data pipeline converting Internet videos into supervision for both intervention and non-intervention moments, producing 224,268 causal decision sequences from 20,258 source videos.
- Use efficient streaming inference for StreamingJev with Incremental Caching, Dynamic Token Dropping, and Prefix-constrained Decoding to reduce repeated computation as observations arrive.
Abstract (from arXiv)
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.
Related papers
- EmbodiedRSI: Active Continual Robot Learning Through Hypothesis-Guided Co-Evolution
- Towards Agentic Robots: Model, Data, Environment, and Harness
- Toward Recursive Self-Improvement in Physical AI
- Recursive Self-Improvement in Physical AI: Mechanisms, Evidence, and Evaluation
- EmbodiedEvo: Self-improving Coding Agents for Physical Intelligence
Publication record
Bibliographic evidence describes where and when a record appeared. It does not validate scientific claims.
First public release: 2026-10-12 · Indexed by Pub-AI: 2026-10-14 UTC
Proactive Physical AI through Continuous Observation and Timely Intervention
Preprint posted: 2026-10-12 · Original record ↗
- arxiv
- 2610.16967
Explicit source versions (1)
- v1 · 2026-10-12
- Preprint available
Source date: 2026-10-12 · First detected: 2026-10-25 UTC · Historical baseline
Source evidence (1)
- Official evidence ↗ · atom:published (official metadata)
Abstract · arxiv
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.