Real-world Transfer of Evolved Artificial Immune System Behaviours between Small and Large Scale Robotic Platforms
This addresses the challenge of cross-platform adaptability in mobile robotics, though it is incremental as it builds on existing artificial immune system methods.
The paper tackled the problem of transferring evolved robotic behaviors from a small robot to a larger one, showing that an artificial immune system technique enabled successful real-world transfer with good navigation and target-finding results, while reinforcement learning alone performed poorly.
In mobile robotics, a solid test for adaptation is the ability of a control system to function not only in a diverse number of physical environments, but also on a number of different robotic platforms. This paper demonstrates that a set of behaviours evolved in simulation on a miniature robot (epuck) can be transferred to a much larger-scale platform (Pioneer), both in simulation and in the real world. The chosen architecture uses artificial evolution of epuck behaviours to obtain a genetic sequence, which is then employed to seed an idiotypic, artificial immune system (AIS) on the Pioneers. Despite numerous hardware and software differences between the platforms, navigation and target-finding experiments show that the evolved behaviours transfer very well to the larger robot when the idiotypic AIS technique is used. In contrast, transferability is poor when reinforcement learning alone is used, which validates the adaptability of the chosen architecture.