Recent advancements in VR technology facilitate tracking real-world objects and users’ movements in the virtual environment (VE) and inspire researchers to develop a physics-based haptic system (i.e., real object haptics) instead of computer-generated haptic feedback. However, there is limited research on the efficacy of such VR systems in enhancing operators’ sensorimotor learning for tasks that require high motor and physical demands. Therefore, this study aimed to design and evaluate the efficacy of a physics-based virtual reality (VR) system that provides users realistic cutaneous and kinesthetic haptic feedback. We designed a physics-based VR system, named PhyVirtual, and simulated human-robot collaborative (HRC) sequential pick-and-place lifting tasks in the VE. Participants performed the same tasks in the real environment (RE) with human-human collaboration instead of human-robot collaboration. We used a custom-designed questionnaire, the NASA-TLX, and electromyography activities from biceps, middle and anterior deltoid muscles to determine user experience, workload, and neuromuscular dynamics, respectively. Overall, the majority of responses (>65%) demonstrated that the system is easy-to-use, easy-to-learn, and effective in improving motor skill performance. While compared to tasks performed in the RE, the PhyVirtual system placed significantly lower physical demand (124.90%; p < 0.05) on the user. The electromyography data exhibited similar trends (p > 0.05; r > 0.89) for both environments. These results show that the PhyVirtual system is an effective tool to simulate safe human-robot collaboration commonly seen in many modern warehousing settings. Moreover, it can be used as a viable replacement for live sensorimotor training in a wide range of fields.

This study aimed to evaluate the visual-spatiotemporal co-registration of the real and virtual objects’ movement dynamics in a low-cost physics-based virtual reality (VR) system that provides real cutaneous and kinesthetic haptic feedback of the objects instead of computer-generated haptic feedback. Twelve healthy participants performed three human-robot collaborative (HRC) sequential pick-and-place lifting tasks while both motion capture and VR systems respectively traced the movement kinematics of the real and virtual objects. We used an iterative closest point algorithm to transform the 3D spatial point clouds of the VR system into the motion capture system. We employed a novel algorithm and principal component analysis to respectively calculate visual and spatiotemporal co-registration precisions between virtual and real objects. Results showed a high correlation (r > 0.96) between real and virtual objects’ movement dynamics and linear and angular co-registration errors of less than 5 cm and 8°, respectively. The trend also revealed a low temporal registration error of <12 ms and was only found along the vertical axis. The visual registration data indicated that the real cutaneous and kinesthetic haptics provided by the physical objects in the virtual environment enhanced proprioception and visuomotor functions of the users.