## 1 Introduction  
With the rapid development of space exploration, deep-sea discovery, nuclear rescue, radiation detection, and robot-assisted medical equipment in recent years, humans urgently need interactive control of slave robots to complete remote operations [1, 2]. More recently, medical robotic applications during the coronavirus pandemic have proven valuable [3, 4]. Due to the highly contagious nature of the novel coronavirus, surgeons are at high risk of infection when examining and sampling patients face-to-face [5]. However, oropharyngeal swabbing is a commonly used technique for COVID-19 sampling and diagnosis in the pandemic worldwide [6, 7]. One application scenario of medical telerobotic systems is to teleoperate robots to conduct COVID-19 swab testing and provide other healthcare services, such as (1) robotics-assisted telesurgery, (2) tele-examination of patients before and after treatment, and (3) tele-training for surgical procedures [8, 9]. On the user side of biomedical telerobotic systems, surgeons can operate a Human-Robot Interaction (HRI) system with an MR Head-Mounted Display (HMD) and control robots from a distance to perform surgery. Additionally, healthcare workers can telemanipulate robots for the care of infected patients or the collection of biological samples, which greatly reduces the risk of infection. Human-robot interactive teleoperation technology remains the main means to realize remote operations in a complex and dynamic environment. Robotic teleoperation satisfies the demands of scenarios in which human access is dangerous but human intelligence is required [10]. Human-in-the-loop tele-control of robotic systems enables operators to remotely implement complex tasks in order to reduce risk without losing quality [11; 12]. An interactive teleoperation system consists of five main components, including the human operator, master control loop, communication channel, slave control loop, and robotic agent. The operator passes commands to the slave loop via the communication channels [13], which also return information on the robot's interaction with its environment [14], using visual and haptic feedback [15; 16]. An effective teleoperation system not only enables intuitive HRI, but ensures that the robot can also be operated in a way allowing the operator to experience the \"feel\" of the robot working on the remote side [17], gaining a \"sense of presence\" [18; 19]. Typically, in conventional systems, the user controls the remote robotic system with a joystick, gamepad, keyboard and mouse, or 3D mouse, and simultaneously receives visual feedback from 2D displays [20; 21]. Robot control is not intuitive and natural to the user [22; 23]. The mismatch between the range of user control space and the limits of the input device workspace can increase the difficulty of telemanipulation and lead to poor operation [24; 25]. Another disadvantage of typical telerobotic systems is the lack of depth perception due to monocular, 2D visualization of the remote site [26; 27; 28], limiting operator performance [29; 30] and any feelings of immersion and telepresence in the remote workspace [31]. MR technology has great potential in co-located and remote collaboration. Researchers have been exploring MR-enhanced collaboration to assist in interactive activities in co-located or remote scenarios [32]. MR-based remote collaboration holds great promise in situations where human/robotic contributors are physically separated and need to find a shared space to work on a common project. By leveraging the seamless integration of virtual and physical content, MR-based collaboration provides a common understanding or shared environment for collaborators. To date, the majority of research efforts have been focused on developing human-computer interaction (HCI), evaluating collaborative processes, and establishing conceptual models and taxonomies of collaborative MR [33].