Exploiting Redundancy for Flexible Behavior: Unsupervised Learning in a Modular Sensorimotor Control Architecture

Autor/inn/en	Butz, Martin V.; Herbort, Oliver; Hoffmann, Joachim
Titel	Exploiting Redundancy for Flexible Behavior: Unsupervised Learning in a Modular Sensorimotor Control Architecture
Quelle	In: Psychological Review, 114 (2007) 4, S.1015-1046 (32 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-295X
Schlagwörter	Memory; Redundancy; Motor Development; Psychomotor Skills; Perceptual Motor Learning; Computer Simulation; Computer System Design; Computer Software; Programming; Spatial Ability; Learning Processes; Human Posture; Mathematical Models + Suchen Sie Ihr Suchwort? Gedächtnis; Redundanz; Motorische Entwicklung; Psychomotorische Aktivität; Perceptual-motor learning; Sensumotorisches Lernen; Wahrnehmungsschulung; Computergrafik; Computersimulation; Programmierung; Räumliches Vorstellungsvermögen; Learning process; Lernprozess; Posture; Körperhaltung; Mathematical model; Mathematisches Modell
Abstract	Autonomously developing organisms face several challenges when learning reaching movements. First, motor control is learned unsupervised or self-supervised. Second, knowledge of sensorimotor contingencies is acquired in contexts in which action consequences unfold in time. Third, motor redundancies must be resolved. To solve all 3 of these problems, the authors propose a sensorimotor, unsupervised, redundancy-resolving control architecture (SURE_REACH), based on the ideomotor principle. Given a 3-degrees-of-freedom arm in a 2-dimensional environment, SURE_REACH encodes 2 spatial arm representations with neural population codes: a hand end-point coordinate space and an angular arm posture space. A posture memory solves the inverse kinematics problem by associating hand end-point neurons with neurons in posture space. An inverse sensorimotor model associates posture neurons with each other action-dependently. Together, population encoding, redundant posture memory, and the inverse sensorimotor model enable SURE_REACH to learn and represent sensorimotor grounded distance measures and to use dynamic programming to reach goals efficiently. The architecture not only solves the redundancy problem but also increases goal reaching flexibility, accounting for additional task constraints or realizing obstacle avoidance. While the spatial population codes resemble neurophysiological structures, the simulations confirm the flexibility and plausibility of the model by mimicking previously published data in arm-reaching tasks. (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10