用多专家共识提升大模型在零样本导航中的决策可靠性
Exploring the Reliability of Foundation Model-Based Frontier Selection in Zero-Shot Object Goal Navigation
- 引入三个专家模型协同分析场景布局、房间类型和视觉信息
- 通过多数一致或全票通过机制筛选前沿点，减少错误推理
- 在RoboTHOR和HM3D上表现领先，适用于未见过的目标导航
本文提出一种新型可靠前沿选择方法，用于零样本物体目标导航（ZS-OGN），通过引入基础模型增强机器人在室内环境中的常识推理能力。该方法构建多专家决策框架，包含两个核心组件：多样化专家前沿分析（DEFA）与共识决策机制（CDM）。DEFA集成家具布局、房间类型分析和视觉场景推理三个专家模型；CDM聚合其输出，优先采纳一致或多数共识结果，以提升决策可靠性。在RoboTHOR和HM3D数据集上达到当前最优性能，能有效导航至未训练过的物体或目标，在动态真实环境中展现出更强的适应性与泛化能力。
原文摘要 · Abstract (English)
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.
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