The autumn series of Nature Inspired Computing and Engineering Research Group seminars begins with our first seminar on Thursday 6th October. 

This talk will present a controller tuning algorithm inspired by the ‘Bayesian brain’ hypothesis; that is the brain models its environment in terms of probabilities, and uses approaches similar to those used in Bayesian statistics to make decisions. The tuning algorithm combines this theory with current understanding of neuromodulatory system, specifically the idea that neuromodulation is a mechanism for adjusting the hyperparameters of learning algorithms. It has been applied to three different components of a walking robot controller; the leg coordination component, which guides the robot towards a target while avoiding obstacles, the trajectory planning component which calculates the paths of each individual leg, and the tracking controller, which ensures the desired path is followed. The final controller demonstrates adaptability and robustness, as well as being reliable and improving efficiency by reducing power and torque requirements..