LEARNING SELECTION OF ACTION FOR CORTICALLY-INSPIRED ROBOT CONTROL

We aim to show in this paper that a cortically-inspired model applied to the control of an autonomous robot can provide it with strong capabilities. First, such an approach enables a robot to extract spatio-temporal regularities during its "life" in an unknown environment , these regularities being stored in a more expressive way than the Q-values. Second , a compromise between perception and drives emerges from the distributed mechanism of spreading activities through the cortical net. Endly, the multi-modal management of information flows endows the robot with capabilities to generalize sequences of perceptions and to use this knowledge to transfer learning from those learned sequences to further behavior. We insist in the paper on three learning mechanisms used in the model , and more precisely on the temporal causality learning rule, which is an original contribution of our work and whose robustness is tested.