In this paper, we proposed a control system with learning to investigate neuromechanical functions for generating adaptive bipedal locomotion on a splitbelt treadmill. Humans show two types of adaptations, called early adaptation and late adaptation, in splitbelt treadmill walking. In our previous work, we investigated the locomotor behavior of a biped robot driven by nonlinear oscillators with phase resetting and showed that it produced the early adaptation like humans. However, because the locomotion control system did not contain any learning mechanism, it did not show the late adaptation. In this paper, we newly develop learning systems, which modulate the interlimb and intralimb coordination patterns, and incorporate them to our locomotion control system. We investigated the locomotor behavior of a biped robot using computer simulations and robot experiments. The results showed the early and late adaptations during the splitbelt treadmill walking and the time evolution of locomotion parameters was similar to that of humans, which might contribute to understanding of adaptive mechanism in humans and to guiding principle for designing a control system of biped robots.