In recent years computational approaches have gained popularity in the area of motor learning and motor control. Theories from machine learning such as optimal control and Bayesian theory have been applied to questions pursued by traditional approaches. In the present talk, I will first review the concepts of motor learning and motor control, its relationship to other disciplines including psychology, and related scientific questions. I will then review some of my finished and planned projects. The questions explored in finished studies include: how is the psychological concept causal inference related to study of motor learning? What error size is optimal to facilitate motor learning? How does uncertainty in the sensory feedback and state estimation impact the rate of motor adaptation? Do human exploit the stability offered by the task itself? How do the rhythmic movement and the discrete movement, which are two movement types usually studied separately, interact with each other? Ongoing studies that will be briefly introduced include sensory integration in standing posture, utilizing customized video gaming system in rehabilitation of patient population such as stroke patients, short-term motor memory etc. After the presentation, I would like to communicate with colleagues about any possibilities of combining the experimentation and mathematical tools from motor behaviors with scientific questions from psychology. I hope my talk can be informative for current trends of computational approaches in motor learning and motor control.