arXiv AI By 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

Proactive Physical AI through Continuous Observation and Timely Intervention

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arXiv AI
Sep 25

Robots That Take Initiative: A Framework for Building and Evaluating Proactive Robots

The paper presents a unified formalism for proactive robot assistance, organized into three levels, and introduces a framework for unprompted proactive assistance. It demonstrates that offline evaluation overestimates performance and proposes a closed-loop evaluation using an adaptive human model. The authors also present GAP, a method that learns from passive observation to anticipate user goals and act, outperforming prior state-of-the-art methods in closed-loop tests.

By Maithili Patel, Sonia Chernova
arXiv Computer Vision
Sep 4

ProAct: Harnessing Streaming Motion Generation and Agentic Reasoning for Real-Time Embodied Social Interaction

ProAct is a dual‑system framework for real‑time embodied social interaction that separates a low‑latency Behavioral System, which streams multimodal interaction and generates continuous non‑verbal motion, from a slower Cognitive System that performs long‑horizon social reasoning and produces proactive intentions. The Cognitive System uses an efficient memory mechanism and a user‑motivation prediction module to decide when to intervene, while the Behavioral System translates these intentions into fluid motion via an intention‑conditioned streaming flow‑matching generator with a disentangled ControlNet branch. The framework is deployed on a physical humanoid robot and validated through real‑world user studies, motion‑generation benchmarks, and a new ProActBench benchmark for proactive trigger detection and restraint.

By Zeyi Zhang, Zixi Kang, Ruijie Zhao, Yusen Feng, Biao Jiang, Hanyu Ji, Libin Liu
arXiv Machine Learning
Oct 16

From Perception to Prediction and Intervention: Benchmarking Proactive Video Assistants in Real-Life Scenarios

The paper introduces “P…IBench”, a benchmark for proactive video understanding that tests daily perception, risk prediction, and intervention in real‑life streaming scenarios. It evaluates 12 video‑language models, finding that current systems struggle to make well‑calibrated response decisions. The authors also create new proactive training data and train “P…I-Assistant-4B”, which shows uneven improvements across the benchmark’s capabilities.

By Boyu Cui, Junhao Luo, Zhihao Wang, Linghan Chen, Zejun Li, Jiwen Zhang, Siyuan Wang, Zhongyu Wei
Hugging Face Trending Papers
Jul 27

DeVA: Decoupled Video-Action Model with physical guidance for robot policy learning

Generalizable robot manipulation requires policies that can anticipate how visual scenes evolve while executing language instructions. While recent Vision-Language-Action models benefit from large-scale pretraining, their predominantly static pretraining objectives provide limited supervision for physical dynamics and temporal causality, leaving control-relevant knowledge to be learned from downstream robot demonstrations.