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This work presents the dual benefit of integrating imitation learningtechniques, based on the dynamical systems formalism, with the visual servoingparadigm. On the one hand, dynamical systems allow to program additional skillswithout explicitly coding them in the visual servoing law, but leveraging fewdemonstrations of the full desired behavior. On the other, visual servoingallows to consider exteroception into the dynamical system architecture and beable to adapt to unexpected environment changes. The beneficial combination ofthe two concepts is proven by applying three existing dynamical systems methodsto the visual servoing case. Simulations validate and compare the methods;experiments with a robot manipulator show the validity of the approach in areal-world scenario.

Learning Stable Dynamical Systems for Visual Servoing