  An increasing number of nonspecialist robotic users demand easy-to-use
machines. In the context of visual servoing, the removal of explicit image
processing is becoming a trend, allowing an easy application of this technique.
This work presents a deep learning approach for solving the perception problem
within the visual servoing scheme. An artificial neural network is trained
using the supervision coming from the knowledge of the controller and the
visual features motion model. In this way, it is possible to give a geometrical
interpretation to the estimated visual features, which can be used in the
analytical law of the visual servoing. The approach keeps perception and
control decoupled, conferring flexibility and interpretability on the whole
framework. Simulated and real experiments with a robotic manipulator validate
our approach.
