New automated learning CPG for rhythmic patterns

In this paper, we suggest a new supervised learning method called Fourier based automated learning central pattern generators (FAL-CPG), for learning rhythmic signals. The rhythmic signal is analyzed with Fourier analysis and fitted with a finite Fourier series. CPG parameters are selected by direct comparison with the Fourier series. It is shown that the desired rhythmic signal is learned and reproduced with high accuracy. The resulting CPG network offers several advantages such as, modulation and robustness against perturbation. The proposed learning method is simple, straightforward and efficient. Furthermore, it is suitable for on-line applications. The effectiveness of the proposed method is shown by comparison with four other supervised learning methods as well as an industrial robotic trajectory following application.