## 5 Conclusions  
This work proposes a low cost autonomous manipulator realizing simple tasks, such as pressing a button lift, and implementing a low effort HMIs. The latter were empowered thanks to the use of an AI algorithm based on _YOLOv2_ convolutional neural network, which recognizes and extracts coordinates of a possible target objects. A custom closed feedback loop algorithm exploits the information on the object coordinates to automatize the execution of the task and to control the robot. Such algorithm uses the _KCF_ as additional tracking algorithm in combination with the _YOLOv2_ network. This approach minimizes the probability of losing the objects tracks and increases the immunity of the robot system \begin{table}
\begin{tabular}{c c c} \hline \hline
**Tracking Algorithms** & **RViz sim. Accuracy** & **Prototype Accuracy** \\ \hline Only _YOLOv2_ & 94\% & 43\% \\ _YOLOv2 + KCF_ & 100\% & 78\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Percentage accuracy of the system over 50 experiment iterations. to the non-idealities of the low cost camera and of the mechanics. A _FSM_ establishes a procedure composed by fixed steps of movements, each of them is realized through the _IBVS_ algorithm that allows exploiting 2D algorithm to drive a real manipulator. The usage of a _Kalman filter_ further make the system more robust against the mechanical vibrations. Such approach, formalized through a conceptual architecture in Section 2, was used to implement a low cost prototype featuring three versions of HMIs for people with different grade of disability. The prototype hardware and software architectures are described in Sections 3.1 and 3.2. Finally, a test procedure was realized aiming to characterize the prototype ability in performing the required tasks. The evaluation was performed by measuring the success rate in touching the correct panel lift button. The system was also simulated by excluding the mechanical non-idealities inside the RViz environment. The accuracy on 50 iterations was 78% with the prototype and 100% in simulation. The HMIs evaluation was performed through preliminary tests with the end-users, who tried to control the robot by using the HMI dedicated to them. All interfaces were appreciated by the users, who were able to execute the task in the given time, regardless their manual-skills. By limiting the number of tasks executable, the proposed solution offers an example of automatic manipulator, which succeeds in reducing the user effort through the complete automatism in the object interaction. Thanks to the use of an AI algorithm, which tracks the objects in the image frames and limits the user task to the only selection of the target. The system results to be less prone to the user error, increasing the safety of the HMI itself. In addition, it also improves the modularity of the system, allowing the use of the best HMI for the given user ability. Conceptualization, methodology, resources, data curation and software, G.G. ; Validation, writing--original draft preparation, writing--review and editing, G.G. and G.M. ; Formal analysis, G.M. ; Visualization, G.M., G.G. and L.F.; Supervision, L.F. and G.M. ; Project administration, G.G., G.M. and L.F. This work was realized in the contest of the RIMEDIO (Il braccio Robotico Intelligente per Migliorare I'autonomia delle pErsone con DIsabilita mOtoria\") project, supported by Fondazione Cassa di Risparmio di Lucca. We also want to thank all the students of the University of Pisa who helped us to test and to evaluate the manipulator. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: