用视觉语言模型提升零样本物体导航的探索终止效率
RATE-Nav: Region-Aware Termination Enhancement for Zero-shot Object Navigation with Vision-Language Models
- 基于区域感知设计探索终止机制，动态判断何时停止探索
- 在HM3D上达67.8%成功率，MP3D上比前人方法提升约10%
- 适合研究智能体探索策略与视觉语言模型应用的学者
物体导航（ObjectNav）是具身人工智能中的基础任务。尽管当前研究在语义地图构建和目标方向预测方面取得进展，冗余探索与探索失败仍不可避免。一个关键但未被充分探索的方向是及时终止探索以克服这些问题。我们观察到探索步数与探索率之间的边际效益递减，并分析了探索的成本收益关系。受此启发，提出RATE-Nav：一种区域感知的探索终止增强方法。包含几何预测区域分割算法与基于区域的探索估计算法，用于计算探索率。通过利用视觉语言模型（VLMs）的视觉问答能力，结合探索率实现高效终止。在HM3D数据集上取得67.8%的成功率和31.3%的SPL；在更具挑战性的MP3D数据集上，相比之前零样本方法提升约10%。
原文摘要 · Abstract (English)
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.
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