## 1 Introduction  
During the last years, modern electronic power wheelchairs have been equipped by manipulators to compensate the deficit in the manuals skill of users due to accidents or disabling diseases [1]. Such robotic arms are designed to perform simple operations, such as knocking on a door or pressing buttons in a lift panel, turning on the light in a room, etc. Owing to the benefits introduced by such manipulators in terms of increment of mobility and autonomy, the research has been focusing on the realization of robotic arms able to perform very complex tasks such as interaction with small objects [2]. However, for highly impaired users exploiting all the potentialities offered by such manipulators is often hard, especially if the the only device to control the robot is the power wheelchair joystick. Thus, various Human-Machine Interfaces (HMIs) have been developed for people with different disabilities. There are several typologies of HMIs, which are optimized for specific classes of disease. In the case of severe upper-link deficits, brain-computer interfaces can be used. This approach is very promising thanks to its usability by those users with no possibility of moving their arms. However, such HMI requires the presence of numerous electrodes placed on the body of the users [3] or dedicated helmets [4] resulting more invasive than other interfaces.Instead, for less severe disabilities different HMIs are convenient. [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [5] propose a semi-autonomous robotic arm with a HMI based on 3D vision to assist users to control the robot. Even if the handling is manual, users are supported in the objects interaction by some predetermined manipulations optionsthat can be addressed by voice commands. Although such interface is really promising, the voice interaction with the HMIs might be really arduous for users with severe dysarthria, which is typical in the case of degenerative diseases or strokes [6]. Indeed, in the presence of severe dysarthric, a speaker dependent voice commanded interface might be necessary in order to make the recognition performances acceptable [7]; it would require much effort by the user to train the interface to increase its usability. Previous versions of this work [8; 9; 10] implement an autonomous eye-in-hand manipulator which exploits the image features extracted by means of a camera and other sensors data to realize a close-feedback loop for the control of robot kinematics. The offered HMI is based on a touch screen in which the user can visualize the camera frames and press the target area where he wants to lead the manipulator. A Computer Vision algorithm is used to perform the image features extraction and their tracking over different images. The main drawback of this approach is the lack of robustness to the errors of the users. In fact, to lead the robotic arm to a predetermined area, it is necessary that the users press exactly the correspondent area on the HMI. Such task can be hard due to spasms or in the case of limited hands control. [PERSON], [PERSON], [PERSON] [11] used a machine learning approach to adapt the HMI to the manual skills of the users. Such method makes the system user dependent and requires a preliminary HMI training phase to be adapted on the user, which might be frustrating. In this work, we propose a user independent five-Degrees of Freedom (DOFs) manipulator equipped by a monocular camera proximity and force sensors. In [8; 9; 10], the robotic arm autonomy is reached by a closed-feedback loop that exploits image features extracted by the camera images. The main advantages of this work is due to an Artificial Intelligence (AI) algorithm that recognizes and locates the objects with respect to the camera frames. In fact, thanks to the AI, objects, such as the buttons of a lift panel, are shown to the users surrounded by bounding boxes.This increases the HMI robustness, since it accepts clicks only inside the bounding box shown, avoiding starting the approach in the case of user errors.