Progressive Design through Staged Evolution

Evolutionary robotics is a good method for the generation of controllers for autonomous robots. However, up to date, evolutionary methods do not achieve the generation of behaviors for complex robots with a fixed body structure composed of lots of sensors and actuators. For such cases, no satisfactory results exist due to the large search space that the evolutionary algorithm has to face. Furthermore, the bootstrap problem does not allow convergence on the first generations, preventing the generation of simple solutions with a minimum fitness value that could guide the evolutionary path towards the final solution. Solutions like incremental evolution try to overcome the problem, but they do not scale well in complex robots with lots of devices. The question is then, why natural evolution succeeded evolving complex animals, but evolutionary robotics does not. One answer to that question may be that, while natural evolution gradually evolved at the same time the animals body plan, their sensors and actuators, their nervous system, and even their environment, artificial evolution tries to evolve the nervous system for a robot with a fixed given body, sensors and actuators, within a fixed complex environment. Our proposal states that when, as it happens in most cases, none of the evolutionary constraints can be relaxed (those are, the robot body, the sensors and actuators, the behavior to implement or the environment), then the use of external knowledge to guide the evolutionary process should be mandatory. Evolutionary approaches try to avoid the use of such knowledge, also called bias, because it directs the evolutionary search towards specific places of the space, not allowing the algorithm to find its own solutions. In this paper, we advocate instead for the use of bias as an inevitable situation when the robot body, task and environment are complex and fixed. Based on this idea, we develop a modular architecture for evolutionary controllers based on neural networks, which allows the selective introduction of bias knowledge in the neural controller during the evolutionary process. The architecture allows the introduction of external knowledge on selected stages of the evolutionary process, affecting only selected parts of the controller that need to accommodate that information. The evolutionary controller is progressively designed in a series of stages, almost in a surgical way, independently of the complexity of the robot (in terms of number of sensors and actuators). This approach allows to avoid the bootstrap problem completely, and to obtain a completely distributed neural controller for the robot using only artificial evolution.