## 1 Introduction  
Welding has been used extensively in the maintenance of nuclear plants, the construction of underwater structures, and the repair of spacecraft in outer space [1]. In these hazardous situations in which human welders have no access, the judgment and intervention of the human operators are required [2]. Customized production is also an application scenario for welding, where welders often work in environments with dust, strong light, radiation, and explosion hazards [3]. Human-in-the-loop (HITL) robotic tele-welding strategies have become a feasible approach for bringing humans out of these dangerous, harmful, and unpleasant environments while performing welding operations [4, 5]. Robotic tele-welding systems (RTWSs) combine the advantages of humans and robotics and coordinate the functions of all system components efficiently and safely [6, 7]. RTWSs can diminish geographical limitations for scarce welding professionals and bring a remote workforce into manufacturing [8, 9]. Welding training is a time-consuming and costly process. Intensive instruction and training are usually required to bring unskilled welders to an intermediate skill level [10, 11]. It is important to analyze the differences between the operating skills of professional and novice welders to facilitate the professional welding level of unskilled welders and further to improve the feasibility, efficiency, and welding quality of RTWSs for novice welders during remote welding operations. The expertise and skill extraction of professional weldersas well as the application of robot assistance in on-site welding operations have become popular research topics [12; 13; 14]. The implementation of interactive robots can stabilize the hand movements of novice welders for improved welding quality, but robot-assisted welding has not been studied in teleoperated welding scenarios. Welding motion capture systems were used in [15; 16] to differentiate between professional and unskilled welders in terms of operational behavior in the gas tungsten arc welding (GTAW) process, providing an experimental basis for the development of robot-assisted tele-welding schemes. The experiments in [17] revealed the differences between professional and unskilled welders in the trajectory of the GTAW hand movements and indicated that the main cause of the unsatisfactory welding results is that novice welders make abrupt movements in the direction perpendicular to the weld surface. However, the operational difference of gas metal arc welding (GMAW) between professional and novice users was not well researched [18]. Recent research on human-centered robotic welding has focused on the development of MR-based robot-assisted welding training platforms, intuitive programming for tele- robotic welding, interactive telerobotic welding design, and MR-enhanced tele-welding paradigms. A virtual reality (VR)-based haptic-guided welder training system was introduced in [19]. This system provides guidance force to welders, simulating a human welding trainer. Both novice and skilled welders can use this platform to improve their welding skills in a virtual environment. However, this system does not integrate real welding scenarios into the virtual environment to allow welders to adjust their movements in real-time according to the welding pool status, nor does it transfer human movements to the robot for actual tele-welding operations. [PERSON] et al. [20] proposed a vision-guided approach for programming automated welding robot paths in 2D, where the programmer draws the target weld pattern in the user presentation space, a low-cost camera in the system captures the image, and an algorithm detects and processes the geometry (contour lines) drawn by the human. This intuitive remote programming system for welding is limited by programming the contour lines in two-dimensional planes only and does not have the real-time capability of a telerobotic welding system.