Traditional learning from demonstration (LfD) generally demands a cumbersome collection of physical demonstrations, which can be time-consuming and challenging to scale. Recent advances show that robots can instead learn from human videos by extracting interaction cues without direct robot involvement. However, a fundamental challenge remains: how to generalize demonstrated interactions across different object instances that share similar functionality but vary significantly in geometry. In this work, we propose \emph{Semantic Anchored Functional Maps} (SemFM), a framework for transferring affordances across objects from a single visual demonstration. Starting from a coarse mesh reconstructed from an image, our method identifies semantically corresponding functional regions between objects, selects mutually exclusive semantic anchors, and propagates these constraints over the surface using a functional map to obtain a dense, semantically consistent correspondence. This enables demonstrated interaction regions to be transferred across geometrically diverse objects in a lightweight and interpretable manner. Experiments on synthetic object categories and real-world robotic manipulation tasks show that our approach enables accurate affordance transfer with modest computational cost, making it well-suited for practical robotic perception-to-action pipelines.

翻译：传统的示教学习通常需要收集大量物理演示，这一过程既耗时又难以扩展。最新研究表明，机器人可以通过分析人类视频提取交互线索，而无需直接参与物理交互。然而，一个根本性挑战依然存在：如何将演示的交互行为泛化到具有相似功能但几何结构差异显著的不同物体实例上。本研究提出语义锚定功能映射框架，该框架能够通过单次视觉演示实现物体间可供性的迁移。该方法从图像重建的粗糙网格出发，首先识别物体间语义对应的功能区域，选择互斥的语义锚点，继而通过功能映射将约束条件在物体表面传播，最终获得稠密且语义一致的对应关系。这使得已演示的交互区域能够以轻量化和可解释的方式迁移到几何结构各异的物体上。在合成物体类别和真实机器人操作任务上的实验表明，本方法能够以适中的计算成本实现精确的可供性迁移，非常适合实际机器人从感知到动作的完整流程。