Task-Independent Spiking Central Pattern Generator: A Learning-Based Approach
This work addresses legged locomotion for robotics and neuroscience, offering a novel framework but is incremental in building on existing spiking neural network models.
The paper tackles the challenge of legged locomotion in robotics by introducing a task-independent, biologically plausible central pattern generator based on spiking neural networks, with results showing stable walking at different speeds and speed changes within a gait cycle in robotic experiments.
Legged locomotion is a challenging task in the field of robotics but a rather simple one in nature. This motivates the use of biological methodologies as solutions to this problem. Central pattern generators are neural networks that are thought 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 applicable 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 are not only evaluated in simulation but also in a robotic experiment. The results are very promising as the used robot was able to perform stable walking at different speeds and to change speed within the same gait cycle.