SIMS: Surgeon-Intention-driven Motion Scaling for Efficient and Precise Teleoperation

Abstract

Telerobotic surgery often relies on a fixed motion scaling factor (MSF) to map the surgeon's hand motions to robotic instruments, but this introduces a trade-off between precision and efficiency: small MSF enables delicate manipulation but slows large movements, while large MSF accelerates transfer at the cost of accuracy. We propose a Surgeon-Intention driven Motion Scaling (SIMS) system, which dynamically adjusts MSF in real time based solely on kinematic cues. SIMS extracts linear speed, tool motion alignment, and dual-arm coordination features to classify motion intent via fuzzy C-means clustering and applies confidence-based updates independently for both arms. In a user study (n=10, three surgical training tasks) conducted on the da Vinci Research Kit, SIMS significantly reduced collisions (mean reduction of 83%), lowered mental and physical workload, and maintained task completion efficiency compared to fixed MSF. These findings demonstrate that SIMS is a practical and lightweight approach for safer, more efficient, and user-adaptive telesurgical control.