Miyauchi, G., Gross, R. and Chen, C. orcid.org/0000-0001-7289-1242 (2026) Warmth and competence in the swarm: designing effective human-robot teams. In: Groß, R., Becker, A.T., Di Caro, G.A., Haghighat, B., Hsieh, M.A., Abu-Aisheh, R., Talamali, M.S. and Dorigo, M., (eds.) Swarm Intelligence: 15th International Conference, ANTS 2026, Darmstadt, Germany, June 8–10, 2026, Proceedings. 15th International Conference, ANTS 2026, 08-10 Jun 2026, Darmstadt, Germany. Lecture Notes in Computer Science (LNCS 16515). Springer Cham, pp. 272-286. ISBN: 9783032261229. ISSN: 0302-9743. EISSN: 1611-3349.
Abstract
As groups of robots increasingly collaborate with humans, understanding how humans perceive them is critical for designing effective human-robot teams. While prior research examined how humans interpret and evaluate the abilities and intentions of individual agents, social perception of robot teams remains relatively underexplored. Drawing on the competence–warmth framework, we conducted two studies manipulating swarm behaviors in completing a collective search task and measured the social perception of swarm behaviors when human participants are either observers (Study 1) and operators (Study 2). Across both studies, our results show that variations in swarm behaviors consistently influenced participants’ perceptions of warmth and competence. Notably, longer broadcast durations increased perceived warmth; larger separation distances increased perceived competence. Interestingly, individual robot speed had no effect on either of the perceptions. Furthermore, our results show that these social perceptions predicted participants’ team preferences more strongly than task performance. Participants preferred robot teams that were both warm and competent, not those that completed tasks most quickly. These findings demonstrate that human-robot interaction dynamically shapes social perception, underscoring the importance of integrating both technical and social considerations when designing robot swarms for effective human-robot collaboration.
Metadata
| Item Type: | Proceedings Paper |
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| Copyright, Publisher and Additional Information: | © 2026 The Author(s). Except as otherwise noted, this author-accepted version of a journal article published in Swarm Intelligence: 15th International Conference, ANTS 2026, Darmstadt, Germany, June 8–10, 2026, Proceedings is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
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| Institution: | The University of Sheffield |
| Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
| Date Deposited: | 23 Apr 2026 14:20 |
| Last Modified: | 01 Jun 2026 11:30 |
| Status: | Published online |
| Publisher: | Springer Cham |
| Series Name: | Lecture Notes in Computer Science |
| Refereed: | Yes |
| Identification Number: | 10.1007/978-3-032-26123-6_21 |
| Related URLs: | |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:240395 |
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