Abstract
This paper addresses a principal problem of in vivo evolution of modular multi-cellular robots. To evolve robot morphologies and controllers in real-space and real-time we need a generic learning mechanism that enables arbitrary modular shapes to obtain a suitable gait quickly after ‘birth’. In this study we investigate a reinforcement learning method and conduct simulation experiments using robot morphologies with different size and complexity. The experiments give insights into the online dynamics of gait learning, the distribution of lucky / unlucky runs and their dependence on the size and complexity of the modular robotic organisms.
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