In this paper, we present a novel method for reliable frontier selection in Zero-Shot Object Goal Navigation (ZS-OGN), enhancing robotic navigation systems with foundation models to improve commonsense reasoning in indoor environments. Our approach introduces a multi-expert decision framework to address the nonsensical or irrelevant reasoning often seen in foundation model-based systems. The method comprises two key components: Diversified Expert Frontier Analysis (DEFA) and Consensus Decision Making (CDM). DEFA utilizes three expert models: furniture arrangement, room type analysis, and visual scene reasoning, while CDM aggregates their outputs, prioritizing unanimous or majority consensus for more reliable decisions. Demonstrating state-of-the-art performance on the RoboTHOR and HM3D datasets, our method excels at navigating towards untrained objects or goals and outperforms various baselines, showcasing its adaptability to dynamic real-world conditions and superior generalization capabilities.

翻译：本文提出了一种用于零样本目标导航（ZS-OGN）的可靠前沿选择新方法，通过将基础模型集成到机器人导航系统中，以增强室内环境中的常识推理能力。我们的方法引入了一个多专家决策框架，以解决基于基础模型的系统中常见的无意义或不相关推理问题。该方法包含两个关键组件：多样化专家前沿分析（DEFA）和共识决策（CDM）。DEFA利用三个专家模型：家具布局分析、房间类型分析和视觉场景推理，而CDM则聚合它们的输出，优先考虑一致或多数共识，以做出更可靠的决策。我们的方法在RoboTHOR和HM3D数据集上展示了最先进的性能，在导航至未经训练的对象或目标方面表现出色，并超越了多种基线方法，证明了其适应动态现实世界条件的卓越泛化能力。