ArtiSG: 通过人类示范的可动物体操作构建功能3D场景图
ArtiSG: Functional 3D Scene Graph Construction via Human-demonstrated Articulated Objects Manipulation
AI总结：
ArtiSG通过编码人类示范到结构化机器人记忆中，构建功能3D场景图，解决现有方法在可动物体操作信息获取上的不足，提升机器人在真实环境中的任务执行能力。
摘要：
3D场景图已使机器人具备语义理解能力以用于导航和规划。然而，当前的功能场景图主要集中在静态元素检测，缺乏用于物理操作所需的动作动力学信息，特别是关于可动物体。现有方法从静态观察推断可动机制易受视觉歧义影响，而通过状态变化估计参数的方法通常依赖于受限设置，如固定相机和无遮挡视角。此外，隐蔽的功能元素如隐藏的把手常被纯视觉感知遗漏。为此，我们提出了ArtiSG，一个通过编码人类示范到结构化机器人记忆中构建功能3D场景图的框架。我们的方法利用稳健的数据采集管道，结合便携硬件设备，准确追踪6自由度操作轨迹并估计可动轴线，即使在相机自身运动下也能有效工作。通过将这些动力学先验整合到分层、开放词汇图中，系统不仅建模了可动物体的运动方式，还利用物理交互数据发现隐含元素。广泛的现实实验表明，ArtiSG在功能元素回忆和可动估计精度上显著优于基线方法。此外，我们证明所构建的图作为可靠的机器人记忆，能有效引导机器人在包含多种可动物体的真实环境中执行语言指导的操作任务。
英文摘要：
3D scene graphs have empowered robots with semantic understanding for navigation and planning. However, current functional scene graphs primarily focus on static element detection, lacking the actionable kinematic 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, inconspicuous functional elements like hidden handles are frequently missed by pure visual perception. 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 data collection pipeline utilizing a portable hardware setup to accurately track 6-DoF manipulation trajectories and estimate articulation axes, even under camera ego-motion. By integrating these kinematic priors into a hierarchical, open-vocabulary graph, our system not only models how articulated objects move but also utilizes physical interaction data to discover implicit elements. 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 robotic memory, effectively guiding robots to perform language-directed manipulation tasks in real-world environments containing diverse articulated objects.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
