Neurobiological studies showed the important role of Centeral Pattern Generators for spinal cord in the control and sensory feedback of animals' locomotion. In this paper, this role is taken into account in modeling bipedal locomotion of a robot. Indeed, as a rhythm generator, a non-classical model of a neuron that can generate oscillatory as well as diverse motor patterns is presented. This allows different motion patterns on the joints to be generated easily. Complex tasks, like walking, running, and obstacle avoidance require more than just oscillatory movements. Our model provides the ability to switch between intrinsic behaviors, to enable the robot to react against environmental changes quickly, To achieve complex tasks while handling external perturbations, a new space for joints' patterns is introduced. Patterns are generated by our learning mechanism based on success and failure with the concept of vigilance. This allows the robot to be prudent at the beginning and adventurous at the end of the learning process, inducing a more efficient exploration for new patterns. Motion patterns of the joint are classified into classes according to a metric, which reflects the kinetic energy of the limb. Due to the classification metric, high-level control for action learning is introduced. For instance, an adaptive behavior of the rhythm generator neurons in the hip and the knee joints against external perturbation are shown to demonstrate the effectiveness of the proposed learning approach.
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