Recently, there has been a growing attention to develop a humanoid robot controller that hopes to move robots closer to real world applications. Several approaches have been proposed to support the learning phase at such a controller, where the robot can gain new knowledge via observation and\or a direct guidance from a human or even another robot. These approaches, however, desire dynamic learning and memorizing techniques, in which, the robot can keep reforming its internal system overtime. Along this line of research, this work therefore, investigates an idea inspired from some assumptions in neuroscience to develop an incremental learning and memory model, we named, a Hierarchical Constructive BackPropagation with Memory (HCBPM). The validity of the model was tested in teaching a humanoid robot a group of names through a natural interaction with human. The experimental results indicate that the robot, with a kind of social learning environment, could reform its own memory, learn different color names, and retrieve these data to teach another user what it had learned.