We investigate a novel adaptive system based on evolution, individual learning, and social learning in a swarm of physical Thymio II robots. The system is based on distinguishing inheritable and learnable features in the robots and defining appropriate operators for both categories. In this study we choose to make the sensory layout of the robots inheritable, thus evolvable, and the robot controllers learnable. We run tests with a basic system that employs only evolution and individual learning and compare this with an extended system where robots can disseminate their learned controllers. Results show that social learning increases the learning speed and leads to better controllers.

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.

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