Despite efforts to design precise motor controllers, robot joints do not always move exactly as desired. This paper introduces a general model-based method for improving the accuracy of joint control. First, a model that predicts the effects of joint requests is built based on empirical data. Then this model is approximately inverted to determine the control requests that will most closely lead to the desired movements. We implement and validate this approach on a popular, commercially available robot, the Sony Aibo ERS-210A.