@inproceedings{song-etal-2025-hazards,
title = "Hazards in Daily Life? Enabling Robots to Proactively Detect and Resolve Anomalies",
author = "Song, Zirui and
Ouyang, Guangxian and
Fang, Meng and
Na, Hongbin and
Shi, Zijing and
Chen, Zhenhao and
Yujie, Fu and
Zhang, Zeyu and
Jiang, Shiyu and
Fang, Miao and
Chen, Ling and
Chen, Xiuying",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.379/",
doi = "10.18653/v1/2025.naacl-long.379",
pages = "7399--7415",
ISBN = "979-8-89176-189-6",
abstract = "Existing household robots have made significant progress in performing routine tasks, such as cleaning floors or delivering objects. However, a key limitation of these robots is their inability to recognize potential problems or dangers in home environments. For example, a child may pick up and ingest medication that has fallen on the floor, posing a serious risk. We argue that household robots should proactively detect such hazards or anomalies within the home, and propose the task of anomaly scenario generation. To accomplish this task, we leverage foundational models instead of relying on manually labeled data to build simulated environments. Specifically, we introduce a multi-agent brainstorming approach, where agents collaborate and generate diverse scenarios covering household hazards, hygiene management, and child safety. These textual task descriptions are then integrated with designed 3D assets to simulate realistic environments. Within these constructed environments, our LLM-based robotic agent learns the necessary skills to proactively discover and handle the proposed anomalies through task decomposition, optimal learning approach selection. We demonstrate that our generated environment outperforms others in terms of task description and scene diversity, ultimately enabling robotic agents to better address potential household hazards."
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<abstract>Existing household robots have made significant progress in performing routine tasks, such as cleaning floors or delivering objects. However, a key limitation of these robots is their inability to recognize potential problems or dangers in home environments. For example, a child may pick up and ingest medication that has fallen on the floor, posing a serious risk. We argue that household robots should proactively detect such hazards or anomalies within the home, and propose the task of anomaly scenario generation. To accomplish this task, we leverage foundational models instead of relying on manually labeled data to build simulated environments. Specifically, we introduce a multi-agent brainstorming approach, where agents collaborate and generate diverse scenarios covering household hazards, hygiene management, and child safety. These textual task descriptions are then integrated with designed 3D assets to simulate realistic environments. Within these constructed environments, our LLM-based robotic agent learns the necessary skills to proactively discover and handle the proposed anomalies through task decomposition, optimal learning approach selection. We demonstrate that our generated environment outperforms others in terms of task description and scene diversity, ultimately enabling robotic agents to better address potential household hazards.</abstract>
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%0 Conference Proceedings
%T Hazards in Daily Life? Enabling Robots to Proactively Detect and Resolve Anomalies
%A Song, Zirui
%A Ouyang, Guangxian
%A Fang, Meng
%A Na, Hongbin
%A Shi, Zijing
%A Chen, Zhenhao
%A Yujie, Fu
%A Zhang, Zeyu
%A Jiang, Shiyu
%A Fang, Miao
%A Chen, Ling
%A Chen, Xiuying
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F song-etal-2025-hazards
%X Existing household robots have made significant progress in performing routine tasks, such as cleaning floors or delivering objects. However, a key limitation of these robots is their inability to recognize potential problems or dangers in home environments. For example, a child may pick up and ingest medication that has fallen on the floor, posing a serious risk. We argue that household robots should proactively detect such hazards or anomalies within the home, and propose the task of anomaly scenario generation. To accomplish this task, we leverage foundational models instead of relying on manually labeled data to build simulated environments. Specifically, we introduce a multi-agent brainstorming approach, where agents collaborate and generate diverse scenarios covering household hazards, hygiene management, and child safety. These textual task descriptions are then integrated with designed 3D assets to simulate realistic environments. Within these constructed environments, our LLM-based robotic agent learns the necessary skills to proactively discover and handle the proposed anomalies through task decomposition, optimal learning approach selection. We demonstrate that our generated environment outperforms others in terms of task description and scene diversity, ultimately enabling robotic agents to better address potential household hazards.
%R 10.18653/v1/2025.naacl-long.379
%U https://aclanthology.org/2025.naacl-long.379/
%U https://doi.org/10.18653/v1/2025.naacl-long.379
%P 7399-7415
Markdown (Informal)
[Hazards in Daily Life? Enabling Robots to Proactively Detect and Resolve Anomalies](https://aclanthology.org/2025.naacl-long.379/) (Song et al., NAACL 2025)
ACL
- Zirui Song, Guangxian Ouyang, Meng Fang, Hongbin Na, Zijing Shi, Zhenhao Chen, Fu Yujie, Zeyu Zhang, Shiyu Jiang, Miao Fang, Ling Chen, and Xiuying Chen. 2025. Hazards in Daily Life? Enabling Robots to Proactively Detect and Resolve Anomalies. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 7399–7415, Albuquerque, New Mexico. Association for Computational Linguistics.