Social competence improves the performance of biomimetic robots leading live fish.

biomimetic robots robot learning social competence

Journal

Bioinspiration & biomimetics
ISSN: 1748-3190
Titre abrégé: Bioinspir Biomim
Pays: England
ID NLM: 101292902

Informations de publication

Date de publication:
04 05 2023
Historique:
received: 04 11 2022
accepted: 04 04 2023
medline: 2 5 2023
pubmed: 5 4 2023
entrez: 4 4 2023
Statut: epublish

Résumé

Collective motion is commonly modeled with static interaction rules between agents. Substantial empirical evidence indicates, however, that animals may adapt their interaction rules depending on a variety of factors and social contexts. Here, we hypothesized that leadership performance is linked to the leader's responsiveness to the follower's actions and we predicted that a leader is followed longer if it adapts to the follower's avoidance movements. We tested this prediction with live guppies that interacted with a biomimetic robotic fish programmed to act as a 'socially competent' leader. Fish that were avoiding the robot were approached more carefully in future approaches. In two separate experiments we then asked how the leadership performance of the socially competent robot leader differed to that of a robot leader that either approached all fish in the same, non-responsive, way or one that did change its approach behavior randomly, irrespective of the fish's actions. We found that (1) behavioral variability itself appears attractive and that socially competent robots are better leaders which (2) require fewer approach attempts to (3) elicit longer average following behavior than non-competent agents. This work provides evidence that social responsiveness to avoidance reactions plays a role in the social dynamics of guppies. We showcase how social responsiveness can be modeled and tested directly embedded in a living animal model using adaptive, interactive robots.

Identifiants

pubmed: 37015241
doi: 10.1088/1748-3190/acca59
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Creative Commons Attribution license.

Auteurs

Moritz Maxeiner (M)

Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany.

Mathis Hocke (M)

Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany.

Hauke J Moenck (HJ)

Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany.

Gregor H W Gebhardt (GHW)

Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany.
Computational Systems Neuroscience, Institute of Zoology, University of Cologne, Cologne, Germany.

Nils Weimar (N)

Institute of Zoology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.

Lea Musiolek (L)

Department of Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany.
Cluster of Excellence 'Science of Intelligence', Technical University of Berlin, Marchstrasse 23, 10587 Berlin, Germany.

Jens Krause (J)

Faculty of Life Sciences, Albrecht Daniel Thaer Institute of Agricultural and Horticultural Sciences, Humboldt Universität zu Berlin, Berlin, Germany.
Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany.
Cluster of Excellence 'Science of Intelligence', Technical University of Berlin, Marchstrasse 23, 10587 Berlin, Germany.

David Bierbach (D)

Faculty of Life Sciences, Albrecht Daniel Thaer Institute of Agricultural and Horticultural Sciences, Humboldt Universität zu Berlin, Berlin, Germany.
Cluster of Excellence 'Science of Intelligence', Technical University of Berlin, Marchstrasse 23, 10587 Berlin, Germany.

Tim Landgraf (T)

Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany.
Cluster of Excellence 'Science of Intelligence', Technical University of Berlin, Marchstrasse 23, 10587 Berlin, Germany.

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Classifications MeSH