## 4 Mixed Reality for Human-Robot Interaction
### Mixed Reality-Enhanced Telemanipulation
#### Mixed Reality-Enhanced Intuitive Telemanipulation
\begin{table}
\begin{tabular}{p{28.5 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Reference** & **Year** & \multicolumn{2}{c}{**Topics Focused on Recent Papers**} & \multicolumn{2}{c}{**Differences with This Article**} \\ \hline
[98] & 2021 & \(\bullet\) & Review of the current state of VR/AR solutions in HRI and HRC solutions summarized in four main categories: operator support, instruction, simulation, and manipulation & \(\bullet\) & Review of novel XR-enhanced robotic approaches for intuitive remote teleoperation applications \\
[98] & 2021 & \(\bullet\) & Review of existing AR-enhanced HRI and robotic interfaces Approaches presented in two main categories: augmenting robots and augmenting surroundings & \(\bullet\) & This article focuses on XR technologies in robotic telemanipulation and telemanipulation tasks in hazardous conditions \\ \hline
[99] & 2022 & \(\bullet\) & Review of existing VR/AR/MR tools and frameworks in HRC using game engines & \(\bullet\) & This article presents XR systems that extend the user’s reality and provide a more immersive and intuitive interface \\
[100] & 2023 & \(\bullet\) & Review of existing VR/AR/MR tools and frameworks in HRC using game engines & \(\bullet\) & This article focuses on both software and hardware aspects \\
[100] & 2023 & \(\bullet\) & Recent literature on software tools categorized into communication tools and interaction tools & \(\bullet\) & This article focuses on both software and hardware aspects \\
[100] & 2023 & \(\bullet\) & Applications presented in four main categories: social robotics, programming of industrial robots, teleoperation, and human–robot collaboration & \(\bullet\) & This work classifies the industrial application context of the reviewed articles into two groups: XR-enhanced telemanipulation and XR-enhanced tele-welding \\ \hline \end{tabular}
\end{table} 
Table 1: Differences between this article and comparable studies recently introduced in the literature on MR-enhanced robotics. and addressing issues related to poor virtual transparency [111; 112; 113]. However, these telerobotic systems do not fully exploit the potential performance enhancements provided by using an MR subspace as an intermediary for the integration of imitation-based motion mapping and 3D visualization mapping. Figure 4 shows the typical diagram and communication structure of the XR-enhanced intuitive and immersive teleoperation systems. The XR-enhanced robotic teleoperation system consists of four parts: (1) the XR-enhanced human-robot interaction unit; (2) the XR scene for vision and motion mapping; (3) the remotely controlled robot working unit; and (4) the bi-directional communication links. [PERSON] et al. [114] developed an MR-enhanced heterogeneous robot telemanipulation system that presents the real robot working space and the corresponding virtual scene presented to the operator in the robot telemanipulation architecture for immersive and intuitive remote grasping. The pose of the robot replica in MR is synchronized with the current pose of the physical robot via messages. The communication between the robot and MR scene is maintained via the ros-bridge. A virtual screen on the left wall augments the virtual scene by displaying an image stream from the camera mounted above the physical robot. However, intuitive teleoperation is only applied to the robotic hand instead of the arm-hand system in its entirety, limiting the workspace and overall application significantly. In [111], researchers from the University of California, Berkeley, built an MR-based teleoperation system on a PR2 robot through imitation learning. The system allows the operator to teleoperate robots to perform complicated tasks naturally and intuitively. In the proposed teleoperation system, a robot is controlled at a distance by the human operator through an MR-based telerobotic interface with overlaid information, which is an effective approach to collect high-quality demonstrations for training the robot.