Addressing robotic navigation in big indoors and outdoors is a way to challenge our systems. The idea is that scaling up is not only a matter of more computational power. Instead, an autonomous robot should be able to self-evaluate, self-regulate and adapt its own behavior in order to cope with a dynamic unstructured environment. For that purpose, modeling internal and external signals — such as motivations, prediction errors, emotions, social interactions, contexts, etc. — is essential. This also allows for using robots, as artificial forms of life, to investigate what and how different kinds of information affect the way living beings can perceive and act in their physical and social environment.

The main topic of the thesis is to develop bio-inspired actions selection models based on the emulation of the cortico-basal loops involving the hippocampus,the prefrontal cortex and the basal ganglia. The goal is to develop more robust robotic control architecture within the frame of computational neuroscience. By analyzing and reproducing the results obtained by neurobiologists, we hope to obtain a better comprehension of the brain functions allowing us to deduce models more suitable for real-world dynamic environments. The models describe functions involved in low-level action selections, like choosing to turn left or right; as well as in selection between high level strategies used in various navigational paradigms, like the competition/cooperation between sensory-motro habitual and high level cognitive strategie.

The aim of this thesis is to ensure the scalability and the robustness of existing navigation algorithms in large and unknown environments (no prior knowledge).