Abstract
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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Bell, C., Storck, T., Sandamirskaya, Y. (2014). Learning to Look: A Dynamic Neural Fields Architecture for Gaze Shift Generation. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_88
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DOI: https://doi.org/10.1007/978-3-319-11179-7_88
Publisher Name: Springer, Cham
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