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Three-fold Adaptivity in Groups of Robots: The Effect of Social Learning by Jacqueline Heinerman; Dexter Drupsteen; A.E. Eiben is a scholarly article available to read on EtoBox.

What is Three-fold Adaptivity in Groups of Robots: The Effect of Social Learning about?

Adapting the control systems of robots on the fly is important in robotic systems of the future. In this paper we present and investigate a three-fold adaptive system based on evolution, individual and social learning in a group of robots and report on a proof-of-concept study based on epucks. We distinguish inheritable and learnable components in the robots' makeup, specify and implement operators for evolution, learning and social learning, and test the system in an arena where the task is to learn to avoid obstacles. In particular, we make the sensory layout evolvable, the locomotion control system learnable and investigate the effects of including social learning in the 'adaptation engine'. Our simulation experiments demonstrate that the full mix of three adaptive mechanisms is practicable and that adding social learning leads to better controllers faster.

Author

Jacqueline Heinerman; Dexter Drupsteen; A.E. Eiben

Publisher

ACM

Published

2015

Language

EN