Many basic locomotor patterns of living bodies are rhythmic, and oscillatory components of physical systems effectively contribute to the generation of the movement. The control signals for the basic locomotor patterns are generated by the central pattern generator (CPG), which is composed of collective neural oscillators, and the activity of the CPG is tightly synchronized with the movement of the physical systems. That is, appropriate locomotor patterns are realized by mutual synchronization between the physical system and the neural system. In this article a simple learning model is proposed to acquire an appropriate parameter set, the intrinsic fre quency of the CPG, and the interaction between the CPG and the physical system, in order to obtain a desired locomotor pattern. The performance of the proposed learning model is con firmed by computer simulations and an adaptive control experiment of a one-dimensional hop ping robot.
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