Object-agnostic Affordance Categorization via Unsupervised Learning of Graph Embeddings

• URL: http://arxiv.org/abs/2304.05989v1
• Date: Thu, 30 Mar 2023 15:04:04 GMT
• Title: Object-agnostic Affordance Categorization via Unsupervised Learning of Graph Embeddings
• Authors: Alexia Toumpa and Anthony G. Cohn
• Abstract summary: Acquiring knowledge about object interactions and affordances can facilitate scene understanding and human-robot collaboration tasks. We address the problem of affordance categorization for class-agnostic objects with an open set of interactions. A novel depth-informed qualitative spatial representation is proposed for the construction of Activity Graphs.
• Score: 6.371828910727037
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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