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

Looking is one of the most basic and fundamental goal-directed behaviors. The neural circuitry that generates gaze shifts towards target objects is adaptive and compensates for changes in the sensorimotor 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 accurately 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.

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