Abstract
Objects of interest are represented in the brain simultaneously in different frames of reference. Knowing the positions of one’s head and eyes, for example, one can compute the body-centred position of an object from its perceived coordinates on the retinae. We propose a simple and fully trained attractor network which computes head-centred coordinates given eye position and a perceived retinal object position. We demonstrate this system on artificial data and then apply it within a fully neurally implemented control system which visually guides a simulated robot to a table for grasping an object. The integrated system has as input a primitive visual system with a what-where pathway which localises the target object in the visual field. The coordinate transform network considers the visually perceived object position and the camera pan-tilt angle and computes the target position in a body-centred frame of reference. This position is used by a reinforcement-trained network to dock a simulated PeopleBot robot at a table for reaching the object. Hence, neurally computing coordinate transformations by an attractor network has biological relevance and technical use for this important class of computations.
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© 2006 Springer-Verlag London Limited
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Weber, C., Muse, D., Elshaw, M., Wermter, S. (2006). A Camera-Direction Dependent Visual-Motor Coordinate Transformation for a Visually Guided Neural Robot. In: Macintosh, A., Ellis, R., Allen, T. (eds) Applications and Innovations in Intelligent Systems XIII. SGAI 2005. Springer, London. https://doi.org/10.1007/1-84628-224-1_12
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DOI: https://doi.org/10.1007/1-84628-224-1_12
Publisher Name: Springer, London
Print ISBN: 978-1-84628-223-2
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