<![CDATA[Conventionally, the trajectory programming procedure for industrial robots using physical teach pendants imposes a high cognitive load, is prone to triggering structural collision accidents for novice students, and limits training efficiency due to the minimal availability of equipment. This study aims to solve these issues by developing an autonomous direct teaching system for the UR5e manipulator utilizing Virtual Reality (VR) technology and a Digital Twin architecture at the SmartEdu-Station facility. As a solution, the system captures global spatial coordinate inputs from the user's natural hand movements via 6-DoF VR controller tracking, then translates them into real-time virtual robot posture visualization using Inverse Kinematics computation. The trajectory coordinate sets are validated for safety as autonomous waypoint data before being transmitted to the physical controller. Empirical testing results prove that data communication via TCP/IP and RTDE protocols records a trajectory packet transmission success rate of 100%, with an average execution response delay consistently under 1 second. Furthermore, interface ergonomics testing using the System Usability Scale (SUS) questionnaire on 10 respondents yielded an average score of 79.0 (Good Category). This research contributes significantly by providing an offline teaching simulation framework that successfully eliminates the risk of physical equipment damage while interactively and safely reducing the learning curve of spatial mapping.]]>
