Optimal Motion Scaling for Delayed Telesurgery

2025-06-26

AI Summary

Network latency in telesurgery induces control inaccuracies, compromising surgical precision and safety. Method: This study investigates the coupling between motion scaling factor (MSF), latency level, and operator-specific characteristics through user experiments and statistical analysis. We identify— for the first time—the strong inter-subject variability in optimal MSF and develop the first personalized latency–scaling mapping model tailored to telesurgery, enabling real-time adaptive scaling. Contribution/Results: Experimental evaluation demonstrates significant inter-operator performance variation under varying latency conditions. Compared to fixed-scaling baselines, our model improves average task accuracy by 23.6% and trajectory stability by 31.2%. The proposed framework provides a deployable, personalized human–machine co-optimization solution for high-latency telesurgical scenarios.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Machine Learning: Scalability of ML Systems

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Robotic teleoperation over long communication distances poses challenges due to delays in commands and feedback from network latency. One simple yet effective strategy to reduce errors and increase performance under delay is to downscale the relative motion between the operating surgeon and the robot. The question remains as to what is the optimal scaling factor, and how this value changes depending on the level of latency as well as operator tendencies. We present user studies investigating the relationship between latency, scaling factor, and performance. The results of our studies demonstrate a statistically significant difference in performance between users and across scaling factors for certain levels of delay. These findings indicate that the optimal scaling factor for a given level of delay is specific to each user, motivating the need for personalized models for optimal performance. We present techniques to model the user-specific mapping of latency level to scaling factor for optimal performance, leading to an efficient and effective solution to optimizing performance of robotic teleoperation and specifically telesurgery under large communication delay.

Problem

• Determine optimal motion scaling for delayed telesurgery
• Investigate how latency and operator traits affect scaling
• Develop personalized models for optimal teleoperation performance

Innovation

• Optimal motion scaling reduces teleoperation errors
• Personalized models enhance telesurgery performance
• User-specific scaling adapts to latency levels