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In mobile robotics, a solid test for adaptation is the ability of a controlsystem to function not only in a diverse number of physical environments, butalso on a number of different robotic platforms. This paper demonstrates that aset of behaviours evolved in simulation on a miniature robot (epuck) can betransferred to a much larger-scale platform (Pioneer), both in simulation andin the real world. The chosen architecture uses artificial evolution of epuckbehaviours to obtain a genetic sequence, which is then employed to seed anidiotypic, artificial immune system (AIS) on the Pioneers. Despite numeroushardware and software differences between the platforms, navigation andtarget-finding experiments show that the evolved behaviours transfer very wellto the larger robot when the idiotypic AIS technique is used. In contrast,transferability is poor when reinforcement learning alone is used, whichvalidates the adaptability of the chosen architecture.

Real-world Transfer of Evolved Artificial Immune System Behaviours between Small and Large Scale Robotic Platforms