Review on User Interaction for Robotic Arm in Digital Twin

The integration of human interaction techniques in digital twin (DT) systems has become increasingly important in manufacturing, industrial automation, and remote operations, particularly for robotic arm control. However, existing approaches joystick control, gesture-based input, and virtual reality (VR) are often disconnected across modalities, limiting effectiveness in real-time environments. The research contribution is a systematic literature review (SLR) that critically analyzes and synthesizes interaction techniques to identify performance trends, evaluation gaps, and design challenges in virtual robotic arm control within digital twin frameworks. The review covers studies published between 2020 and 2025, selected to reflect the rapid emergence of immersive technologies in real-time digital twin systems. Following PRISMA 2020 guidelines, 180 records were identified from IEEE Xplore, Scopus, and Web of Science, from which 77 peer-reviewed studies were selected. Interaction techniques were evaluated using task completion time, positional accuracy, NASA Task Load Index (NASA-TLX), and System Usability Scale (SUS). The findings reveal that VR-based techniques dominate due to their intuitiveness and immersive experience in human-in-the-loop control. However, evaluation remains inconsistent across studies, with significant variation in metrics and experimental setups. Latency and synchronization were identified as critical challenges in real-time control, where delays degrade precision and responsiveness. Traditional methods such as joysticks offer stability but lack the natural interaction of immersive techniques. These findings underscore the need for standardized evaluation frameworks and improved synchronization strategies, offering practical guidance for designing robust, human-centered digital twin interaction systems for robotic arms.