Advancing Human-Robot Collaboration in Teleoperation: Comparative Evaluation of Shared Control Modalities Across Tasks and Interfaces

Shared control enhances teleoperation by combining human decision-making with robotic autonomy, but its effectiveness across tasks and interfaces remains unclear. This study compares three shared control modalities: Semi-Autonomous Control (SAC), State-Fusion Shared Control (SFSC), and State-Guidance Shared Control (SGSC), against Direct Control (DC). A within-subject experiment evaluated performance in two teleoperation tasks (grasping and peg-in-hole) while varying interface type (VR controller vs. joypad) and task difficulty. Results show strong context-dependent effects. SFSC with a VR controller achieved the most efficient grasping by combining natural hand interaction with stabilized human-robot input fusion. SAC paired with a joypad performed best in precision-demanding peg-in-hole tasks, improving accuracy and user experience by reducing operator control burden. In contrast, SGSC provided limited benefits due to insufficient feedback richness. Based on these findings, we propose the Task–Interface–Shared Control Alignment Framework (TISCAF) to guide systematic selection and design of shared control strategies for teleoperation systems.