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Performing task-space tracking control on redundant robot manipulators is a difficult problem. When the physical model of the robot is too complex or not available, standard methods fail and machine learning algorithms can have advantages. We propose an adaptive learning algorithm for tracking control of underactuated or non-rigid robots where the physical model of the robot is unavailable. The control method is based on the fact that forward models are relatively straightforward to learn anddoi:10.1109/icra.2012.6224831 dblp:conf/icra/BocsiHCP12 fatcat:wuzem7owwfdgja7xmtjpadiy4a