%0 Journal Article %1 Butz:2007g %A Butz, M. Volker %A Herbort, O. %A Hoffmann, J. %D 2007 %J Psychological Review %K MVButz MVButzJ avoidance computational control, encoding, learning, model, motor movements, obstacle population reaching redundancy resolution, sensorimotor unsupervised %P 1015-1046 %T Exploiting Redundancy for Flexible Behavior: Unsupervised Learning in a Modular Sensorimotor Control Architecture %V 114 %X Autonomously developing organisms face several challenges when learning reaching movements, such as reaching for a glass. First, motor control has to be learned unsupervised, or selfsupervised. Second, knowledge of sensorimotor contingencies has to be acquired in contexts in which action consequences unfold in time. Third, motor redundancies need to be resolved. To solve all three of these problems, we propose a sensorimotor, unsupervised, redundancyresolving control architecture (SURE REACH), which is based on the ideomotor principle. Given a three degree of freedom arm in a 2-D environment, SURE REACH encodes two 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 inverse sensorimotor model enable SURE REACH to learn and represent sensorimotor grounded distance measures and to use dynamic programming to reach goals flexibly and efficiently. The architecture does not only solve the redundancy problem, but significantly increases goal reaching flexibility, accounting for additional task constraints or realizing obstacle avoidance. While the spatial population codes resemble neurophysiological structures, simulations confirm the plausibility and flexibility of the model, mimicking various previously published behavior data in arm reaching tasks.

@article{Butz:2007g, abstract = {Autonomously developing organisms face several challenges when learning reaching movements, such as reaching for a glass. First, motor control has to be learned unsupervised, or selfsupervised. Second, knowledge of sensorimotor contingencies has to be acquired in contexts in which action consequences unfold in time. Third, motor redundancies need to be resolved. To solve all three of these problems, we propose a sensorimotor, unsupervised, redundancyresolving control architecture (SURE REACH), which is based on the ideomotor principle. Given a three degree of freedom arm in a 2-D environment, SURE REACH encodes two 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 inverse sensorimotor model enable SURE REACH to learn and represent sensorimotor grounded distance measures and to use dynamic programming to reach goals flexibly and efficiently. The architecture does not only solve the redundancy problem, but significantly increases goal reaching flexibility, accounting for additional task constraints or realizing obstacle avoidance. While the spatial population codes resemble neurophysiological structures, simulations confirm the plausibility and flexibility of the model, mimicking various previously published behavior data in arm reaching tasks.}, added-at = {2009-10-05T22:17:39.000+0200}, author = {Butz, M. Volker and Herbort, O. and Hoffmann, J.}, biburl = {https://www.bibsonomy.org/bibtex/2b6f40026831c0758ed315a2ce6bc34d1/butz}, description = {own papers}, interhash = {85456327a6d60713468cce5e2daf85d1}, intrahash = {b6f40026831c0758ed315a2ce6bc34d1}, journal = {Psychological Review}, keywords = {MVButz MVButzJ avoidance computational control, encoding, learning, model, motor movements, obstacle population reaching redundancy resolution, sensorimotor unsupervised}, owner = {butz}, pages = {1015-1046}, timestamp = {2009-10-05T22:44:40.000+0200}, title = {Exploiting Redundancy for Flexible Behavior: {U}nsupervised Learning in a Modular Sensorimotor Control Architecture}, volume = 114, year = 2007 }