In this work we present a method that allows to learn a cost function for motion planning of human-robot collaborative manipulation tasks where the human and the robot manipulate objects simultaneously in close proximity. Our approach is based on inverse optimal control which enables, considering a set of demonstrations, to find a cost function balancing different features. The cost function that is recovered from the human demonstrations is composed of elementary features, which are designed to encode notions such as safely, legibility and efficiency of the manipulation motions. We demonstrate the approach on data gathered from motion capture of human-human manipulation in close proximity of blocks on a table. To demonstrate the feasibility and efficacy of our approach we provide initial test results consisting of learning a cost function and then planning for the human kinematic model used in the learning phase.