标题： RATE-Nav：基于区域感知的零样本物体导航终止增强视觉语言模型 显示英文标题

标题： RATE-Nav: Region-Aware Termination Enhancement for Zero-shot Object Navigation with Vision-Language Models

摘要： 物体导航（ObjectNav）是具身人工智能的一项基础任务。尽管当前研究在语义地图构建和目标方向预测方面取得了显著进展，但冗余探索和探索失败仍然不可避免。一个关键但尚未充分探索的方向是如何及时终止探索以克服这些挑战。我们观察到探索步数与探索率之间存在边际效应递减现象，并分析了探索的成本效益关系。受此启发，我们提出了区域感知的终止增强方法RATE-Nav。该方法包括几何预测区域分割算法和基于区域的探索估计算法来计算探索率。通过利用视觉语言模型（VLMs）的视觉问答能力和探索率，可以实现高效的探索终止。在HM3D数据集上，RATE-Nav的成功率为67.8%，SPL为31.3%。而在更具挑战性的MP3D数据集上，RATE-Nav相对于先前的零样本方法显示出大约10%的提升。 显示英文摘要

摘要： 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.