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Toumpa, A and Cohn, AG orcid.org/0000-0002-7652-8907 (2023) Object-agnostic Affordance Categorization via Unsupervised Learning of Graph Embeddings. Journal of Artificial Intelligence Research, 77. 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. Published by AI Access Foundation under 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. |
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 Alan Turing Institute No ref given EU - European Union 825619 |
Depositing User: | Symplectic Publications |
Date Deposited: | 21 Jun 2023 11:35 |
Last Modified: | 05 Jan 2024 16:09 |
Status: | Published |
Publisher: | AI Access Foundation |
Identification Number: | 10.1613/jair.1.13253 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:200574 |
Available Versions of this Item
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Object-agnostic Affordance Categorization via Unsupervised Learning of Graph Embeddings. (deposited 05 Jan 2024 16:10)
- Object-agnostic Affordance Categorization via Unsupervised Learning of Graph Embeddings. (deposited 21 Jun 2023 11:35) [Currently Displayed]