Goal-directed imitation with self-adjusting adaptor based on a neural oscillator network

An innovative framework of imitation between dissimilar bodies is proposed to automatically achieve the goal of the perceived behavior. Biologically inspired control based on central pattern generators currently gains increasing attention to embody human-like rhythmic motions to humanoid robots. However, this control approach suffers from highly nonlinear dynamics of neural systems, difficulty of motion pattern generation, uncertainty of behavior between neural systems and biomechanics, and so on. To cope with these problems, the imitation technique is employed in this work. We first propose the self-adjusting adaptor to easily generate an appropriate motion pattern by modifying the perceived motion toward attaining the goal of the behavior. Secondly, we verify the property of entrapment of neural oscillator network in the proposed adaptor to duplicate the regenerated motion pattern. In the numerical simulations of biped locomotion, the perceived pattern data is regenerated to keep the direction of the foot contact force identical between the demonstrator and the imitator Also, the neural oscillator is entrained by external signals under stable conditions. To the best of the authors' knowledge, this paper is the first work to validate the advantages of neural oscillator networks as a tool of imitation