It is shown that a neural network-based controller can perform rapidly and accurately, taking into account the non-linearities of various mapping functions. Such a controller is easy to train, tolerant of imprecise equipment configurations, and insensitive to camera perturbations following training. This method features real-time adaptivity to changes in mappings, and is simpler than traditional control techniques, which require the solution of the inverse perspective projection and inverse kinematics of the system.

Various operations including approaching, centering, paralleling, reaching and adjusting are performed by the robot as it navigates towards the target. The robot attempts to grasp targets that are sufficiently close, or approach them while avoiding collisions with obstacles.