Evolutionary robotics aim at building machines constantly able to learn new knowledges in a continuous, uncontroled and changing world. This method allowed to successfully build real robots showing complex reactive behaviors. Further research include the design of control architectures with more cognitive abilities. Memory is a central component of cognition and finding methods allowing robots to acquire memory could be a first step necessary to develop higher cognitive behavior. On this basis, the goal of this thesis is to study the synthesis of control architecture for robots, able to achieve tasks requiring the development of internal memory. We hypothesize that building an internal form of memory in a control architecture is a deceptive problem. In other words evolutionary robotics tend to generate agents only taking into account their current perceptions. We propose an approach based on the use of different selective pressures in order to avoid premature convergence to individuals having reactive behavior. We show that in order to promote the emergence of internal memory, it is necessary : (1) to use discrete fitness which doesn't introduce gradients which may tend to local optima; (2) to develop behavioral diversity mechanisms in order to explore search space more efficiently; (3) to develop different helper objectives that ensure robust memory.
