Object navigation requires an agent to locate a target in an unknown environment through visual observations. Existing methods typically rely on open-vocabulary detectors or vision-language models (VLMs) to answer where to search, but often overlook what not to trust - which semantic cues are unreliable. Open-vocabulary perception is prone to systematic misleading evidence: false positives, outdated static priors, and repeated failed exploration due to lack of embodied verification, which contaminates mapping and decision-making. Such errors are rooted in structured object relations in real-world scenes. To address this, we propose DB-Nav, a framework that reshapes the search space via dual relational biases. It factorizes target-centric relations into an Activation Bias (propagates contextual evidence) and an Inhibition Bias (suppresses unreliable regions via perceptual confusion and action-level falsification). These biases are unified into a Relational Activation-Inhibition Exploration Graph that modulates frontier exploration values using online observations and failed accesses. Experiments on ObjectNav benchmarks show that DB-Nav significantly outperforms existing methods in success rate (SR) and Success weighted by Path Length (SPL), offering a lightweight, interpretable, and robust navigation framework without costly online VLM reasoning.

翻译：物体导航要求智能体通过视觉观测在未知环境中定位目标。现有方法通常依赖开放词汇检测器或视觉语言模型（VLM）来回答“在哪里搜索”，但往往忽略了“什么不可信”——哪些语义线索是不可靠的。开放词汇感知容易产生系统性误导证据：假阳性、过时的静态先验，以及因缺乏具身验证导致的重复探索失败，这些都会污染地图构建与决策过程。此类错误源于真实世界场景中结构化的物体关系。针对这一问题，我们提出DB-Nav框架，通过双重关系偏置重塑搜索空间。该框架将目标中心关系分解为激活偏置（传播上下文证据）与抑制偏置（通过感知混淆与行动层面证伪抑制不可靠区域）。两种偏置统一为关系激活-抑制探索图，利用在线观测与失败访问记录调整前沿探索值。在ObjectNav基准上的实验表明，DB-Nav在成功率（SR）和路径长度加权成功率（SPL）上显著优于现有方法，提供了轻量级、可解释且鲁棒的导航框架，无需昂贵的在线VLM推理。