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
Item Type: | Article |
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Authors/Creators: |
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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: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number EU - European Union 825619 Alan Turing Institute No ref given |
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: | 10.1613/jair.1.13253 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:206333 |