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On the evolution of behaviors through embodied imitation

Erbas, Mehmet D.; Bull, Larry; Winfield, Alan F.T.

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Mehmet D. Erbas

Lawrence Bull
School Director (Research & Enterprise) and Professor


© 2015 Massachusetts Institute of Technology. Abstract This article describes research in which embodied imitation and behavioral adaptation are investigated in collective robotics. We model social learning in artificial agents with real robots. The robots are able to observe and learn each others' movement patterns using their on-board sensors only, so that imitation is embodied. We show that the variations that arise from embodiment allow certain behaviors that are better adapted to the process of imitation to emerge and evolve during multiple cycles of imitation. As these behaviors are more robust to uncertainties in the real robots' sensors and actuators, they can be learned by other members of the collective with higher fidelity. Three different types of learned-behavior memory have been experimentally tested to investigate the effect of memory capacity on the evolution of movement patterns, and results show that as the movement patterns evolve through multiple cycles of imitation, selection, and variation, the robots are able to, in a sense, agree on the structure of the behaviors that are imitated.


Erbas, M. D., Bull, L., & Winfield, A. F. (2015). On the evolution of behaviors through embodied imitation. Artificial Life, 21(2), 141-165.

Journal Article Type Article
Publication Date May 26, 2015
Publicly Available Date Jun 6, 2019
Journal Artificial Life
Print ISSN 1064-5462
Electronic ISSN 1530-9185
Publisher Massachusetts Institute of Technology Press (MIT Press)
Peer Reviewed Peer Reviewed
Volume 21
Issue 2
Pages 141-165
Keywords social learning, behavioural adaptation, embodied imitation, multi-robot systems, swarm intelligence
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