@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://preview.aclanthology.org/landing_page/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."
}