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
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:2607. 11523v1 Announce Type: cross Abstract: When should an intelligent assistant speak up without being asked?
By Gong Sitong, Tianyu Yan, Caixin Kang, Bo Zheng, Xiang Ruan, Huchuan Lu, Kaipeng Zhang, Yoichi Sato, Yifei Huang
When should an intelligent assistant speak up without being asked? Continuous egocentric video offers rich, evolving context that enables a new form of assistance: one that is proactive rather than merely reactive.
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
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.
arXiv:2609.12541v1 Announce Type: new
Abstract: We demonstrate that a general-purpose agent can directly drive a physical robot throughout task execution without any task-specific or environment-spec...
By Mengzhao Jia, Yang Lin, Xixin Zhang, Zhihan Zhang, Xiaobai Liu, Meng Jiang
arXiv:2603. 15600v2 Announce Type: replace-cross Abstract: Accurate process supervision remains a critical challenge for long-horizon robotic manipulation.
By Yibin Liu, Yaxing Lyu, Daqi Gao, Zhixuan Liang, Weiliang Tang, Shilong Mu, Xiaokang Yang, Yao Mu
arXiv:2606. 14778v1 Announce Type: cross Abstract: Long-term action anticipation (LTA) aims to predict an ordered sequence of future verb-noun actions from a partially observed video.
By Rui Cao, Jiannong Cao, Bo Yuan, Zhiyuan Wen, Mingjin Zhang
arXiv:2606. 01063v1 Announce Type: new Abstract: Theory of Mind (ToM) enables an agent to reason about another actor's beliefs, goals, and intentions, which is essential for human-centered embodied assistance.
By Ruoxuan Zhang, Qiaoqiao Wan, Zhengguang Wang, Chenghao Yu, Hongxia Xie, Jianlong Fu, Wen-Huang Cheng
arXiv:2606. 09547v1 Announce Type: cross Abstract: Learning everyday skills, like cooking a dish, relies increasingly on instructional media such as online videos.
By Apratim Bhattacharyya, Shweta Mahajan, Sanjay Haresh, Rajeev Yasarla, Reza Pourreza, Litian Liu, Risheek Garrepalli, Roland Memisevic
arXiv:2610.02715v1 Announce Type: new
Abstract: Egocentric videos capture how people carry out everyday activities, yet testing an agent requires evaluating the consequences of actions it chooses its...
By Qinchuan Cheng, Zhantao Gong, Pengzhan Sun, Angela Yao, Shijie Li