Nikolas Hemion, Frank Joublin, Katharina Rohlfing, "A competitive mechanism for self-organized learning of sensorimotor mappings", Int. Conf. on Development and Learning (ICDL), 2011.

How can a robot learn sensorimotor knowledge in a developmental way based on its own experiences solely? An important step is the acquisition of a body-schema—learning about the physical behavior of its own body, and how incoming sensory stimuli can be put in relation to the own body. In this work, we study how a competitive learning mechanism, which is related to the EM algorithm, can help to simplify the learning problem. We demonstrate how a robot can learn the way visual stimuli move as a consequence of the robots own actions of moving its camera or moving its hand in front of its camera. We show how the robot can discriminate stimuli originating from these two kinds of actions and learn the position of the hand in its visual input. Previous approaches have relied on a preprocessing step to “self-detect”, which we find is not necessary. The robot acquires a set of sensorimotor estimates, which could later be used, e.g. in visually guided reaching.