Multi-Scale Spatial Cognition Models and Bio-Inspired Robot Navigation

The rodent navigation system has been the focus of study for over a century. Discoveries made lately have provided insight on the inner workings of this system. Since then, computational approaches have been used to test hypothesis, as well as to improve robotics navigation and learning by taking inspiration on the rodent navigation system. This dissertation focuses on the study of the multi-scale representation of the rat’s current location found in the rat hippocampus. It first introduces a model that uses these different scales in the Morris maze task to show their advantages. The generalization power of larger scales of representation are shown to allow for the learning of more coherent and complete policies faster. Based on this model, a robotics navigation learning system is presented and compared to an existing algorithm on the taxi driver problem. The algorithm outperforms a canonical Q-Learning algorithm, learning the task faster. It is also shown to work in a continuous environment, making it suitable for a real robotics application. A novel task is also introduced and modeled, with the aim of providing further insight to an ongoing discussion over the involvement of the temporal portion of the hippocampus in navigation. The model is able to reproduce the results obtained with real rats and generates a set of empirically verifiable predictions. Finally, a novel multi-query path planning system is introduced, inspired in the way rodents represent location, their way of storing a topological model of the environment and how they use it to plan future routes. The algorithm is able to improve the routes in the second run, without disrupting the robustness of the underlying navigation system