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Hazards in Daily Life? Enabling Robots to Proactively Detect and Resolve Anomalies
Song, Zirui Ouyang, Guangxian Fang, Meng Na, Hongbin Shi, Zijing Chen, Zhenhao Fu, Yujie Zhang, Zeyu Jiang, Shiyu Fang, Miao
Song, Zirui
Ouyang, Guangxian
Fang, Meng
Na, Hongbin
Shi, Zijing
Chen, Zhenhao
Fu, Yujie
Zhang, Zeyu
Jiang, Shiyu
Fang, Miao
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2025.naacl-long.379.pdf
Adobe PDF, 7.37 MB
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Natural Language Processing
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Conference proceeding
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http://creativecommons.org/licenses/by/4.0/
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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. 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, the robotic agent learns the necessary skills to proactively discover and handle the proposed anomalies through task decomposition, and 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.
Citation
Source
Proceedings of the 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics Human Language Technologies Long Papers Naacl Hlt 2025
Conference
2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2025
Keywords
46 Information and Computing Sciences, 49 Mathematical Sciences, 4602 Artificial Intelligence
Subjects
Source
2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2025
Publisher
Association for Computational Linguistics