Object handover is a basic task that is found in many human-robot cooperation scenarios. If we are to build socially acceptable robots, we need to enable robots to perform handovers properly. In this paper, we discuss some of the social implications of proper robot-human handovers, and we focus on the challenge of determining a proper grasp configuration when handing over an object. We propose a framework that enables a robot to learn proper grasp configurations for handovers through observations. Our aim is to eliminate the need for manually specifying grasp configurations information to the robot, and allow generalization of handover grasp configurations for known objects to unknown objects. We are currently implementing our proposed framework onto an HRP4R robot, and we discuss about our plans for conducting user studies to evaluate our system upon its completion.