Abstract
Evolutionary robotics is a research area that makes use of evolutionary computation (EC) to provide a means of learning in robots. In this paper, we discuss a new way of integrating the actual robot and its model during EC. This method, which involves the co-evolution of model parameters, is applied to the problem of learning gaits for hexapod robots. The form of EC used is the cyclic genetic algorithm (CGA). Tests done in simulation and on the robot show that the CGA operating on the co-evolving model of the robot can adapt to changes in the robot’s capabilities to provide a system of anytime learning.
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