Learning for the Control of Dynamical Motion Systems Export

@inproceedings{citeulike:2860702, abstract = {This paper addresses the dynamic control of multi- joint systems based on learning of sensory-motor transformations. To avoid the dependency of the controllers to the analytical knowledge of the multi- joint system, a non parametric learning approach is developed which identifies non linear mappings between sensory signals and motor commands involved in control motor systems. The learning phase is handled through a General Regression Neural Network (GRNN) that implements a non parametric Nadarayan-Watson regression scheme and a set of local PIDs. The resulting dynamic sensory-motor controller (DSMC) is intensively tested within the scope of hand-arm reaching and tracking movements in a dynamical simulation environment. (DSMC) proves to be very effective and robust. Moreover, it reproduces kinematics behaviors close to captured hand-arm movements.}, author = {Marteau, Pierre-Francois and Gibet, Sylvie}, booktitle = {Intelligent Systems Design and Applications, 2007. ISDA 2007. Seventh International Conference on}, citeulike-article-id = {2860702}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/ISDA.2007.27}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4389650}, doi = {10.1109/ISDA.2007.27}, journal = {Intelligent Systems Design and Applications, 2007. ISDA 2007. Seventh International Conference on}, keywords = {control, function\_approximation, inverse\_dynamics}, pages = {454--459}, posted-at = {2008-06-04 11:21:05}, priority = {0}, title = {Learning for the Control of Dynamical Motion Systems}, url = {http://dx.doi.org/10.1109/ISDA.2007.27}, year = {2007} }

TY - CONF ID - citeulike:2860702 L3 - citeulike-article-id:2860702 N2 - This paper addresses the dynamic control of multi- joint systems based on learning of sensory-motor transformations. To avoid the dependency of the controllers to the analytical knowledge of the multi- joint system, a non parametric learning approach is developed which identifies non linear mappings between sensory signals and motor commands involved in control motor systems. The learning phase is handled through a General Regression Neural Network (GRNN) that implements a non parametric Nadarayan-Watson regression scheme and a set of local PIDs. The resulting dynamic sensory-motor controller (DSMC) is intensively tested within the scope of hand-arm reaching and tracking movements in a dynamical simulation environment. (DSMC) proves to be very effective and robust. Moreover, it reproduces kinematics behaviors close to captured hand-arm movements. JF - Intelligent Systems Design and Applications, 2007. ISDA 2007. Seventh International Conference on EP - 459 TI - Learning for the Control of Dynamical Motion Systems T2 - Intelligent Systems Design and Applications, 2007. ISDA 2007. Seventh International Conference on SP - 454 KW - control KW - function_approximation KW - inverse_dynamics AU - Marteau, Pierre-Francois AU - Gibet, Sylvie PY - 2007/// UR - http://dx.doi.org/10.1109/ISDA.2007.27 ER -