Learning to Look: A Dynamic Neural Fields Architecture for Gaze Shift Generation

Abstract. Looking is one of the most basic and fundamental goal-directed behaviors. The neural circuitry that generates gaze shifts to-wards target objects is adaptive and compensates for changes in the sen-sorimotor plant. Here, we present a neural-dynamic architecture, which enables an embodied agent to direct its gaze towards salient objects in its environment. The sensorimotor mapping, which is needed to accu-rately plan the gaze shifts, is initially learned and is constantly updated by a gain adaptation mechanism. We implemented the architecture in a simulated robotic agent and demonstrated autonomous map learning and adaptation in an embodied setting