Object-agnostic Affordance Categorization via Unsupervised Learning of Graph Embeddings
This addresses scene understanding and human-robot collaboration by enabling affordance learning in everyday scenarios without object or scene constraints, though it is incremental as it builds on existing graph-based methods.
The paper tackles the problem of categorizing object affordances for class-agnostic objects with an open set of interactions by learning similarities between object interactions unsupervisedly, achieving a high V-measure in real-world cluttered scenes.
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.