Exploring the Kuramoto model of coupled oscillators in minimally cognitive evolutionary robotics tasks

This work is the first attempt to investigate the neural dynamics of a simulated robotic agent engaged in minimally cognitive tasks by employing evolved instances of the Kuramoto model of coupled oscillators as its nervous system. The main objectives are to shed new light into the role of neuronal synchronisation and phase towards the generation of cognitive behaviours and to initiate an investigation on the efficacy of such systems as practical robot controllers. The first experiment is an active categorical perception task in which the robot has to discriminate between moving circles and squares. In the second task, the robotic agent has to approach moving circles with both normal and inverted vision thus adapting to both scenarios. These tasks were chosen for being considered as benchmarks in the evolutionary robotics and adaptive behaviour communities. The results obtained indicate the feasibility of the framework in the analysis and generation of embodied cognitive behaviours.