3D scene graphs have empowered robots with semantic understanding for navigation and planning, yet they often lack the functional information required for physical manipulation, particularly regarding articulated objects. Existing approaches for inferring articulation mechanisms from static observations are prone to visual ambiguity, while methods that estimate parameters from state changes typically rely on constrained settings such as fixed cameras and unobstructed views. Furthermore, fine-grained functional elements like small handles are frequently missed by general object detectors. To bridge this gap, we present ArtiSG, a framework that constructs functional 3D scene graphs by encoding human demonstrations into structured robotic memory. Our approach leverages a robust articulation data collection pipeline utilizing a portable setup to accurately estimate 6-DoF articulation trajectories and axes even under camera ego-motion. We integrate these kinematic priors into a hierarchical and open-vocabulary graph while utilizing interaction data to discover inconspicuous functional elements missed by visual perception. Extensive real-world experiments demonstrate that ArtiSG significantly outperforms baselines in functional element recall and articulation estimation precision. Moreover, we show that the constructed graph serves as a reliable functional memory that effectively guides robots to perform language-directed manipulation tasks in real-world environments containing diverse articulated objects.

翻译：三维场景图赋予机器人语义理解能力以支持导航与规划，但其通常缺乏物理操作所需的功能性信息，尤其在关节物体方面。现有基于静态观测推断关节机制的方法易受视觉模糊性影响，而通过状态变化估计参数的方法通常依赖于固定相机和无遮挡视图等受限设置。此外，通用物体检测器常遗漏细粒度功能元件（如小型把手）。为弥补这一差距，我们提出ArtiSG框架，通过将人类演示编码为结构化机器人记忆来构建功能性三维场景图。该方法采用便携式装置构建的鲁棒关节数据采集流程，即使在相机自身运动条件下也能精确估计六自由度关节轨迹与轴线。我们将这些运动学先验整合至分层开放词汇图结构中，并利用交互数据发现视觉感知遗漏的不显眼功能元件。大量真实环境实验表明，ArtiSG在功能元件召回率与关节估计精度方面显著优于基线方法。此外，我们证明所构建的图可作为可靠的功能记忆，有效指导机器人在包含多样化关节物体的真实环境中执行语言指令驱动的操作任务。