Nav-$R^2$ Dual-Relation Reasoning for Generalizable Open-Vocabulary Object-Goal Navigation

• URL: http://arxiv.org/abs/2512.02400v1
• Date: Tue, 02 Dec 2025 04:21:02 GMT
• Title: Nav-$R^2$ Dual-Relation Reasoning for Generalizable Open-Vocabulary Object-Goal Navigation
• Authors: Wentao Xiang, Haokang Zhang, Tianhang Yang, Zedong Chu, Ruihang Chu, Shichao Xie, Yujian Yuan, Jian Sun, Zhining Gu, Junjie Wang, Xiaolong Wu, Mu Xu, Yujiu Yang,
• Abstract summary: Nav-$R2$ is a framework that explicitly models two types of relationships, target-environment modeling and environment-action planning.<n>Our SA-Mem preserves the most target-relevant and current observation-relevant features from both temporal and semantic perspectives.<n>Nav-R2 achieves state-of-the-art performance in localizing unseen objects through a streamlined and efficient pipeline.
• Score: 67.68165784193556
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Object-goal navigation in open-vocabulary settings requires agents to locate novel objects in unseen environments, yet existing approaches suffer from opaque decision-making processes and low success rate on locating unseen objects. To address these challenges, we propose Nav-$R^2$, a framework that explicitly models two critical types of relationships, target-environment modeling and environment-action planning, through structured Chain-of-Thought (CoT) reasoning coupled with a Similarity-Aware Memory. We construct a Nav$R^2$-CoT dataset that teaches the model to perceive the environment, focus on target-related objects in the surrounding context and finally make future action plans. Our SA-Mem preserves the most target-relevant and current observation-relevant features from both temporal and semantic perspectives by compressing video frames and fusing historical observations, while introducing no additional parameters. Compared to previous methods, Nav-R^2 achieves state-of-the-art performance in localizing unseen objects through a streamlined and efficient pipeline, avoiding overfitting to seen object categories while maintaining real-time inference at 2Hz. Resources will be made publicly available at \href{https://github.com/AMAP-EAI/Nav-R2}{github link}.

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