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Dynamical Systems (DS) for robot motion modeling are well-suited for efficient robot learning and control. Our focus in this extended abstract is on autonomous dynamical systems, which represent a motion plan completely without dependency on time. We develop a method that allows to locally reshape an existing, stable autonomous DS without risking introduction of additional equilibrium points or unstable behavior. This is achieved by locally applying rotations and scalings to the original dynamics. Gaussian Processes are then used to incrementally learn reshaped dynamical systems. We briefly report on pre- liminary results from applying the proposed methodology for learning 2d hand-writing motions.


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Last update: 25/08/06