Cat Royale: A case study of artist-led AI research
Cat Royale: A case study of artist-led AI research
We explore artist-led research as a method to complement technical AI methodologies. We present a case study called Cat Royale in which artists created a robot to play with cats. We show how the artist-led development of this system involved extensive improvisation to create a socio-technical AI system that ultimately delivered a corpus of video data of cats interacting with robots. We introduce a machine learning tool that enables diverse stakeholders to explore this corpus. We reflect on the distinctive characteristics of artist-led AI research, the potential benefits to AI, and the tensions involved.
Cat Royale
16-25
Cao, Dingbang
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Tandavanitj, Nick
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Adams, Matt
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Farr, Ju Row
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Schneiders, Eike
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Salimbeni, Guido
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Pound, Mike
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Mancini, Clara
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Benford, Steve
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Cao, Dingbang
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Tandavanitj, Nick
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Adams, Matt
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Farr, Ju Row
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Schneiders, Eike
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Salimbeni, Guido
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Pound, Mike
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Mancini, Clara
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Benford, Steve
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Cao, Dingbang, Tandavanitj, Nick, Adams, Matt, Farr, Ju Row, Schneiders, Eike, Salimbeni, Guido, Pound, Mike, Mancini, Clara and Benford, Steve
(2025)
Cat Royale: A case study of artist-led AI research.
Proceedings of Machine Learning Research, 295, .
Abstract
We explore artist-led research as a method to complement technical AI methodologies. We present a case study called Cat Royale in which artists created a robot to play with cats. We show how the artist-led development of this system involved extensive improvisation to create a socio-technical AI system that ultimately delivered a corpus of video data of cats interacting with robots. We introduce a machine learning tool that enables diverse stakeholders to explore this corpus. We reflect on the distinctive characteristics of artist-led AI research, the potential benefits to AI, and the tensions involved.
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Cat Royale: A Case Study of Artist-led AI Research
- Version of Record
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UKAI24_paper_3
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More information
e-pub ahead of print date: 5 August 2025
Keywords:
Cat Royale
Identifiers
Local EPrints ID: 503722
URI: http://eprints.soton.ac.uk/id/eprint/503722
ISSN: 2640-3498
PURE UUID: b8d2a81f-20d0-49ac-be4f-aa114148d565
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Date deposited: 11 Aug 2025 16:55
Last modified: 22 Aug 2025 02:46
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Contributors
Author:
Dingbang Cao
Author:
Nick Tandavanitj
Author:
Matt Adams
Author:
Ju Row Farr
Author:
Eike Schneiders
Author:
Guido Salimbeni
Author:
Mike Pound
Author:
Clara Mancini
Author:
Steve Benford
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