## 4 Discussion and Conclusions  
This paper proposes a method for controlling a robot arm in an MR environment. The method uses object recognition and localization to generate digital twins of objects in corresponding locations in the virtual scene. In this virtual scene, users can specify which objects to grasp and their placement positions. Then, the server autonomously controls the robot arm to complete the grasping and placing operations. It is, thus, a technique intended to teleoperate the robot arm by interacting with an object's digital twins. Our purpose is to develop an easy-to-use teleoperation method to enhance the teleoperation experience, allowing inexperienced users to execute a task that requires specific skills and experience. This paper also compares the proposed method to the traditional method and assesses the study results using qualitative metrics questionnaires. The results indicate that it is easy and feasible for inexperienced users to control a robot arm using the MR interface. The results also show that the proposed method reduces the workload of the teleoperation system compared to the traditional method. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Measure**} & \multicolumn{2}{c}{**Traditional Method**} & \multicolumn{2}{c}{**Our Method**} & \multirow{2}{*}{**t**} & \multirow{2}{*}{_p_} \\ \cline{2-2} \cline{5-6}  & **Mean** & **Std. Dev** & & & & \\ \hline Mental Demand & 45.50 & 13.50 & 18.67 & 12.91 & 7.737 & \textless{}0.001 \\ Physical Demand & 27.00 & 12.42 & 17.50 & 7.274 & 3.554 & \textless{}0.001 \\ Temporal Demand & 38.50 & 14.67 & 21.17 & 13.64 & 4.659 & \textless{}0.001 \\ Performance & 37.50 & 15.85 & 20.83 & 11.84 & 4.537 & \textless{}0.001 \\ Effort & 48.33 & 19.72 & 26.57 & 18.28 & 4.359 & \textless{}0.001 \\ Frustration & 27.83 & 13.89 & 21.10 & 20.82 & 1.449 & 0.154 \\ Learnability & 53.75 & 17.12 & 75.83 & 17.95 & \textless{}4.794 & \textless{}0.001 \\ Usability & 65.63 & 16.44 & 82.19 & 10.95 & \textless{}4.516 & \textless{}0.001 \\ Total score & 63.25 & 13.94 & 80.92 & 9.475 & \textless{}5.645 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Inferential statistics for all measures. Figure 9: Statistical graphs of SUS for the traditional method and our method during the task [15]. The whiskers indicate the mean +/ – standard deviation, and \({}^{*}\) denotes the data that are statistically significantly different, \(p\) < 0.001. In summary, our research findings substantiate the advantages of our method in the teleoperation of robot arms, including ease of use and a reduced workload. When using the method proposed in this paper for the teleoperation of robot arms, all participants agreed that the method was user-friendly, intuitive, and beneficial for the task of robot arm teleoperation. After experimenting with the equipment, they reported a sense of comfort with the method and did not perceive a significant workload, as evidenced by the NASA-TLX questionnaire data. In terms of the system's usability and learnability, all participants demonstrated high levels of proficiency, indicating that the requirements for users to effortlessly learn and utilize the method have been satisfactorily met. Compared to the other methods [30], the proposed method has a lower workload, and the comparative table is shown in Table 4. The other method recruited 15 participants to test and evaluate the performance of the system. As shown in Table 4, our method achieves better scores in the measures of NASA-TLX compared to other methods, especially in mental demand, physical demand, temporal demand, and performance; the workload scores were reduced by more than 50% compared to the other method. This indicates that the method proposed in this paper is superior at reducing the user's workload. The major limitation of the present study is motion planning. For motion planning, the method uses the default algorithm provided by ROS, which results in suboptimal motion trajectories.