Toumpa, A. and Cohn, A.G. orcid.org/0000-0002-7652-8907 (2023) Object-agnostic Affordance Categorization via Unsupervised Learning of Graph Embeddings. Journal of Artificial Intelligence Research, 77. pp. 1-38. ISSN 1076-9757
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.
Metadata
Authors/Creators: |
|
||||||
---|---|---|---|---|---|---|---|
Copyright, Publisher and Additional Information: | © 2023 The Authors. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. | ||||||
Keywords: | reasoning about actions and change, qualitative reasoning, vision, qualitative spatiotemporal relations | ||||||
Dates: |
|
||||||
Institution: | The University of Leeds | ||||||
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) | ||||||
Funding Information: |
|
||||||
Depositing User: | Symplectic Publications | ||||||
Date Deposited: | 08 Dec 2023 11:54 | ||||||
Last Modified: | 08 Dec 2023 11:54 | ||||||
Status: | Published | ||||||
Publisher: | AI Access Foundation | ||||||
Identification Number: | https://doi.org/10.1613/jair.1.13253 |