## 1 Introduction  
Robots are used in various hazardous or challenging tasks, such as disaster search and rescue [1], industrial automation [2, 3], space exploration [4], surgical applications [5], military applications [6], and underwater exploration [7]. In industrial automation, most production lines achieve automation with the help of robots, such as product picking and placing, welding, and painting. However, when facing an open work environment, dynamic and changing workflows, and hard-to-define human-robot cooperation requirements [8, 9], it is difficult to implement robotic automation. In such environments, the teleoperation of robots can integrate the advantages of robots and users to complete complex tasks. Users leverage their perception, judgment, and adaptability to make the remote control of robots more effective, and the robots keep users away from danger. But traditional robot teleoperation provides only a limited field of view of the workspace, which makes it difficult for users to develop effective spatial understanding, and traditional teleoperation also depends on physical devices to control robots, which may lead to incorrect operation [10]. The traditional remote interfaces mainly include joysticks [11, 12], haptic controllers [13], a keyboard, and a mouse [14], which have been extensively researched in robot arm teleoperation. The interface of the Fanuc industrial robot is based on a small touchscreen with a complex panel filled with buttons and allows users to operate the robot remotely. In this case, only experienced users can manage the complex interface. Some manufacturers have developed more user-friendly interfaces. KUKA and ABB provide interfaces that can carry out most of the interaction by touching the screen panels. However, users need to program the industrial robot's motion to control the industrial robot by their interfaces. Before programming robots, users need to learn previous knowledge in areas such as robotics, automation, and computer science. To address the issue of the complex interface, [PERSON] et al. [15] used the Augmented Reality (AR) interface and game controller to control the industrial robot. In the interface, several holograms of task messages are visualized, such as the position and orientation of the end effector, the direction of the command movements, the camera view, the robot velocity, the target position on the object, and the end effector trajectory. The holograms are registered in the surrounding environment of the robot through the AR headset. Users use a game controller to control the robot remotely and interact with the holograms. The results show that this method is more intuitive and easier to use and significantly improves the speed of remote operations. Mixed reality (MR) offers more user-friendly remote interfaces and is increasingly important in many industries. [PERSON] et al. [16] conducted an empirical study to measure the errors performed by two groups of people performing a working task through a virtual reality (VR) device to investigate the impact of robots on human operators. One group worked with a robot, and the other worked without the presence of a robot. Although statistical results showed that there were no significant differences between the two groups, qualitative analysis proved that the presence of the robot led to people paying more attention during the execution of the task but also resulted in a worse learning experience. [PERSON] et al. [17] compared Augmented Reality (AR), virtual reality (VR), and video-based tasks in safety training. The results showed that AR outperformed traditional video training in terms of knowledge retention, long-term self-efficacy, and quality of instruction. The AR experience was not as effective as the VR experience in all these areas, but the AR group experienced a smaller decrease in knowledge over time. [PERSON] et al. [18] proposed a fiducial-based hand-eye calibration method.