Responding to affordances: learning and projecting a sensorimotor mapping

An important aim of robotics is to design and build machines that can recognize and exploit opportunities afforded by new situations. Traditionally in artificial intelligence, this task has fallen on abstract representations, but that has left the problem of how to ground the representations in sensorimotor activity. The paper proposes a computational architecture whereby a mobile robot internalizes representations based on its experience. The paper examines a fast online learning algorithm that allows the robot to build up a mapping of how its motor signals transform sensory data. The author also proposes a way of categorizing object affordances according to their internal effects. Based on these effects, wavelet analysis is applied to sensory data to uncover invariance, thus abstracting a representation of goals. Finally, heuristics are explored for projecting a learned sensorimotor mapping into the future to attain these goals.