Evolving and Breeding Robots

Abstract Our experiences with a range of evolutionary robotic experiments have resulted in major changes to our set-up of arti�cial life experiments and our interpretation of observed phenomena. Initially, we investigated simulation-reality relationships in order to transfer our arti�cial life simulation work with evolution of neural network agents to real robots. This is a di�cult task, but can, in a lot of cases, be solved with a carefully built simulator. By being able to evolve control mechanisms for physical robots, we were able to study biological hypotheses about animal behaviours by using exactly the same experimental set-ups as were used in the animal behavioural experiments. Evolutionary robotic experiments with rats open �eld box experiments and chick detours show howevolutionary robotics can be a powerful biological tool, and they also suggest that incremental learning might be fruitful for achieving complex robot behaviour in an evolutionary context. However, it is not enough to evolve controllers alone, and we argue that robot body plans and controllers should co-evolve, which leads to an alternative form of evolvable hardware. By combining all these experiences, we reach breeding robotics. Here, children can, as breeders, evolve e.g. LEGO robots through an interactive genetic algorithm in order to achieve desired behaviours, and then download the evolved behaviours to the physical �LEGO � robots.