  We propose a visual servoing method consisting of a detection network and a
velocity trajectory planner. First, the detection network estimates the objects
position and orientation in the image space. Furthermore, these are normalized
and filtered. The direction and orientation is then the input to the trajectory
planner, which considers the kinematic constrains of the used robotic system.
This allows safe and stable control, since the kinematic boundary values are
taken into account in planning. Also, by having direction estimation and
velocity planner separated, the learning part of the method does not directly
influence the control value. This also enables the transfer of the method to
different robotic systems without retraining, therefore being robot agnostic.
We evaluate our method on different visual servoing tasks with and without
clutter on two different robotic systems. Our method achieved mean absolute
position errors of <0.5 mm and orientation errors of <1{\deg}. Additionally, we
transferred the method to a new system which differs in robot and camera,
emphasizing robot agnostic capability of our method.
