Demonstration learning of robotic skills using repeated suggestions learning algorithm

In this paper a new model of nonlinear dynamical system based on adaptive frequency oscillators for learning rhythmic signals is implemented by demonstration. This model uses coupled Hopf oscillators to encode and learn any periodic input signal. Learning process is completely implemented in the dynamics of adaptive oscillators. One of the issues in learning in such systems is constant number of oscillators in the feedback loop. In other words, the number of adaptive frequency oscillators is one of the design factors. In this contribution, it is shown that using enough number of oscillators can help the learning process. In this paper, we address this challenge and try to solve it in order to learn the rhythmic movements with greater accuracy, lower error and avoid missing fundamental frequency. To reach this aim, a method for generating drumming patterns is proposed which is able to generate rhythmic and periodic trajectories for a NAO humanoid robot. To do so, a programmable central pattern generator is used which is inspired from animal's neural systems and these programmable central pattern generators are extended to learn patterns with more accuracy for NAO humanoid robots. Successful experiments of demonstration learning are done using simulation and a NAO Real robot.