Deep learning has become a powerful tool for dealing with various problems. In particular, in recent years, it has been an object of research even in the field of robotics, due to its ability to work easily with millions of parameters, exploiting the possibility of modeling complex functions. The aim of this thesis is to use one of the core architectures of DL, that is, Convolutional Neural Networks (CNNs), in order to build an open control loop to control a Meca500 robotic arm, which is intended to move towards a target. The target object chosen for the experiment is a simple six-faced die, which is placed in the workspace; a small endoscopic camera is mounted on the end-effector, aligned with its approach axis, in such a way that the target is always visible to the camera.