In this paper, odometer parameters of a differential drive mobile robot are learned in an online fashion. EKF which is designed using nominal values of odometer, estimates pose of the mobile robot. A second open-loop model that tracks EKF filter is designed. This open-loop tracking system updates its parameter so as to track the states of the system estimated by EKF. As the parameters of the open-loop system is learned, nominal values of parameters of the EKF plant model are updated with these learned values. Hence a cascaded closed-loop system is proposed. In order to verify the results, a simulink model is developed and performance of the proposed adaptive learning system is investigated. It is seen that regular EKF filter diverges even under mild parameter uncertainty whereas the cascaded closed loop system is stable against severe parameter uncertainty.