  This work presents the dual benefit of integrating imitation learning
techniques, based on the dynamical systems formalism, with the visual servoing
paradigm. On the one hand, dynamical systems allow to program additional skills
without explicitly coding them in the visual servoing law, but leveraging few
demonstrations of the full desired behavior. On the other, visual servoing
allows to consider exteroception into the dynamical system architecture and be
able to adapt to unexpected environment changes. The beneficial combination of
the two concepts is proven by applying three existing dynamical systems methods
to the visual servoing case. Simulations validate and compare the methods;
experiments with a robot manipulator show the validity of the approach in a
real-world scenario.
