## 1 Introduction
In their experiment, they compared the performance of HMD with a 2D interface and no visualization interface. Users could only observe the real robot arm with no visualization interface. The 2D interface displayed a 3D model of the robot arm, the predicted sparse motion trajectory of the robot arm, and a 3D point cloud of the working scene. The HMD overlaid the predicted motion trajectory on the real robot arm. The results showed that the HMD significantly outperformed the 2D interface, with the collision prediction accuracy improving by 15% and the time reduced by 38%. There was no significant difference between the lack of a visualization interface and the 2D interface in collision prediction. [PERSON] et al. [30] compared the impact of three different visual schemes on the robot arm teleoperation, including mixed reality with multi-perspective 2D monocular RGB camera views (MR-2D), which used two 2D monocular cameras to provide the workspace's information, mixed reality with 3D stereoscopic vision (SV) and monocular RGB (baseline)-integrated vision (MR-3 DSV), which adds a stereoscopic camera in the MR-2D to provide an extra stereoscopic vision, and mixed reality with 3D point cloud (PC) and monocular RGB (baseline)-integrated vision (MR-3 DPC) which adds a Kinect V2 depth camera in the MR-2D to provide an extra 3D point cloud. The experiment concluded that MR-2D was the worst among the three visual schemes, and MR-3 DSV was inferior to MR-3 DPC in terms of task execution time, user workload, and system usability. Similarly, [PERSON] et al. [31] used a virtual reality (VR) controller to remotely operate a robot arm and compared three different interfaces: a pure virtual robot arm interface, a point cloud-only interface, and a point cloud combined with a virtual robot arm interface. The results indicate that the pure point cloud interface was less efficient than the rendered virtual robot arm interface. Recently, three-dimensional virtual scenes have been mainly presented in the form of point cloud models. The data volume of point cloud models is often enormous, which makes real-time scene modeling, data conversion, and rendering very challenging. To address the limitations of the point cloud interfaces, [PERSON] et al. [32] used prefabricated 3D models to replace the point clouds of fixed objects to reduce the data volume, but this method still uses the point clouds to display the unfixed objects. Currently, teleoperation methods based on mixed reality are implemented by controlling the position of the robot arm's end effector or the angles of its joints [15; 33; 34; 35], such as the method proposed by [PERSON] [22], which allows users to interact with the robot arm's digital twin to control its movement, but users can only manipulate the virtual end effector. However, these methods have requirements for the user's proficiency, and the unskilled operation is prone to collision between the robot arm and the surrounding objects. In order to reduce the difficulty of the teleoperation system, this paper presents a method to teleoperate the robot arm in a mixed reality environment by directly interacting with the digital twin of the target object. This method allows the user to move the digital twin of a real object in a virtual scene to indicate the object to be moved and the target position to be placed without directly controlling the robot arm. To achieve this goal, the objects in the working scene are identified and located, and their 3D models are created in advance. An operable virtual scene is then quickly generated in HoloLens2 through server communication. The target application picks and places objects in dangerous situations. Our teleoperation method approach enables seamless integration of the teleoperation of the robot arm with an immersive human-machine interface. This method provides a virtual scene that can be viewed from any angle and an easy-to-learn operation method.