Acquiring knowledge about object interactions and affordances can facilitate scene understanding and human-robot collaboration tasks. As humans tend to use objects in many different ways depending on the scene and the objects' availability, learning object affordances in everyday-life scenarios is a challenging task, particularly in the presence of an open set of interactions and objects. We address the problem of affordance categorization for class-agnostic objects with an open set of interactions; we achieve this by learning similarities between object interactions in an unsupervised way and thus inducing clusters of object affordances. A novel depth-informed qualitative spatial representation is proposed for the construction of Activity Graphs (AGs), which abstract from the continuous representation of spatio-temporal interactions in RGB-D videos. These AGs are clustered to obtain groups of objects with similar affordances. Our experiments in a real-world scenario demonstrate that our method learns to create object affordance clusters with a high V-measure even in cluttered scenes. The proposed approach handles object occlusions by capturing effectively possible interactions and without imposing any object or scene constraints.

本文介绍了一种针对开放式交互集的类不可知对象的功能类别识别方法，该方法通过无监督学习认知对象互动之间的相似性，从而诱导对象功能群簇，为构建活动图提出了一种新的深度感知定性空间表示方法，通过这种方法可以获得相似功能的物体组。实验证明，该方法即使在杂乱无章的场景中也能学习到高度 V-measure 的对象功能群簇，并且能够有效地处理对象遮挡，并且不强制施加任何对象或场景约束。

通过无监督学习图嵌入进行非特定物体的可用性分类