@inproceedings{li-etal-2025-rate,
    title = "{RATE}-Nav: Region-Aware Termination Enhancement for Zero-shot Object Navigation with Vision-Language Models",
    author = "Li, Junjie  and
      Zhang, Nan  and
      Qu, Xiaoyang  and
      Lu, Kai  and
      Li, Guokuan  and
      Wan, Jiguang  and
      Wang, Jianzong",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-acl.341/",
    pages = "6564--6574",
    ISBN = "979-8-89176-256-5",
    abstract = "Object Navigation (ObjectNav) is a fundamental task in embodied artificial intelligence. Although significant progress has been made in semantic map construction and target direction prediction in current research, redundant exploration and exploration failures remain inevitable. A critical but underexplored direction is the timely termination of exploration to overcome these challenges. We observe a diminishing marginal effect between exploration steps and exploration rates and analyze the cost-benefit relationship of exploration. Inspired by this, we propose RATE-Nav, a Region-Aware Termination-Enhanced method. It includes a geometric predictive region segmentation algorithm and region-Based exploration estimation algorithm for exploration rate calculation. By leveraging the visual question answering capabilities of visual language models (VLMs) and exploration rates enables efficient termination.RATE-Nav achieves a success rate of 67.8{\%} and an SPL of 31.3{\%} on the HM3D dataset. And on the more challenging MP3D dataset, RATE-Nav shows approximately 10{\%} improvement over previous zero-shot methods."
}