  Efficient skill acquisition, representation, and on-line adaptation to
different scenarios has become of fundamental importance for assistive robotic
applications. In the past decade, dynamical systems (DS) have arisen as a
flexible and robust tool to represent learned skills and to generate motion
trajectories. This work presents a novel approach to incrementally modify the
dynamics of a generic autonomous DS when new demonstrations of a task are
provided. A control input is learned from demonstrations to modify the
trajectory of the system while preserving the stability properties of the
reshaped DS. Learning is performed incrementally through Gaussian process
regression, increasing the robot's knowledge of the skill every time a new
demonstration is provided. The effectiveness of the proposed approach is
demonstrated with experiments on a publicly available dataset of complex
motions.
