Task-independent Spiking Central Pattern Generator: a Learning-based Approach

Elie Aljalbout, Florian Walter, Florian Röhrbein, Alois Knoll

Abstract: Legged locomotion is a challenging task in the field of robotics but a rather simple one in nature. This motivates the adoption of biological methodologies as solutions to this problem. Central pattern generators are neural networks that were discovered in biology to be responsible for locomotion in humans and some animal species. As for robotics, many attempts were made to reproduce such systems and use them for a similar goal. One interesting design model is based on spiking neural networks. This model is the main focus of this work, as its contribution is not limited to engineering but also to neuroscience. This paper introduces a new general framework for building central pattern generators that are task-independent, biologically plausible, and rely on learning methods. The abilities and properties of the presented approach were not only evaluated in simulation but also in a robotic experiment. The results were very promising as the used robot was able to perform stable walking at different speeds and to change speed within the same gait cycle.

Supplementary Video with Results