用几何部件与功能属性引导机器人导航，无需训练就能找没见过的物体
GAMap: Zero-Shot Object Goal Navigation with Multi-Scale Geometric-Affordance Guidance
- 融合物体部件和功能属性构建多尺度导航地图
- 在HM3D和Gibson数据集上成功率显著提升
- 适合无训练样本的未知物体导航场景
零样本物体目标导航（ZS-OGN）使机器人或智能体能够在未见物体类别的情况下完成导航任务，而无需针对特定物体进行训练。传统方法依赖类别语义信息进行引导，但在物体部分可见或环境缺乏详细功能表征时表现不佳。为此，我们提出几何部件与功能属性地图（GAMap），通过多尺度评分机制捕捉不同尺度下的物体几何部件和功能属性，作为导航依据。在HM3D和Gibson基准数据集上的全面实验表明，该方法在成功率（Success Rate）和路径加权成功率（Success weighted by Path Length）上均取得显著提升，验证了基于几何部件与功能属性的导航策略在不依赖任何额外物体特训或微调的前提下，有效增强了机器人的自主性与适应性。
原文摘要 · Abstract (English)
Zero-Shot Object Goal Navigation (ZS-OGN) enables robots or agents to navigate toward objects of unseen categories without object-specific training. Traditional approaches often leverage categorical semantic information for navigation guidance, which struggles when only objects are partially observed or detailed and functional representations of the environment are lacking. To resolve the above two issues, we propose \textit{Geometric-part and Affordance Maps} (GAMap), a novel method that integrates object parts and affordance attributes as navigation guidance. Our method includes a multi-scale scoring approach to capture geometric-part and affordance attributes of objects at different scales. Comprehensive experiments conducted on HM3D and Gibson benchmark datasets demonstrate improvements in Success Rate and Success weighted by Path Length, underscoring the efficacy of our geometric-part and affordance-guided navigation approach in enhancing robot autonomy and versatility, without any additional object-specific training or fine-tuning with the semantics of unseen objects and/or the locomotions of the robot.
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