Real-Time Non-Contact Force Compensation for Wrist-Mounted Force/Torque Sensors in Haptic-Enabled Robotic Surgery Training

2026-04-26

AI Summary

This study addresses the challenge of distorted haptic feedback in surgical robot training caused by non-contact disturbances—such as gravity, sensor bias, and mounting misalignment—affecting wrist-mounted force/torque sensors. To overcome this, the authors integrate a wrist sensor into the low-cost RoboScope platform and propose a real-time adaptive compensation method based on recursive least squares (RLS). This approach dynamically eliminates interference without requiring pre-collected data or repeated calibration, achieving fully online non-contact force compensation for the first time. Experimental results demonstrate that the method reduces force and torque errors by over 95% and 91%, respectively, significantly outperforming existing techniques. The proposed solution thus provides reliable, high-fidelity force perception essential for cost-effective, high-precision haptic surgical training.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Haptic feedback has been a long-missed feature in robotic-assisted surgery, one that would allow surgeons to perceive tissue properties and apply controlled forces during delicate procedures. Although commercial robotic systems have begun to integrate haptic technologies, their high costs limit accessibility for training and research purposes. To address this gap, we extend our previously developed low-cost robotic surgery training setup, RoboScope, by incorporating a wrist-mounted force/torque (F/T) sensor for haptic feedback training. Wrist-mounted sensing avoids many challenges associated with tip-mounted sensors but introduces additional non-contact forces, such as gravity, sensor bias, installation offsets, and associated torques, which compromise measurement accuracy. In this paper, we propose a robust real-time compensation method based on recursive least squares (RLS). This method eliminates the need for dataset collection and frequent recalibration while adapting to changing operating conditions. Experimental validation demonstrates that the proposed approach achieves over 95% error reduction in non-contact force compensation and more than 91% in non-contact torque compensation, significantly outperforming existing methods. These results highlight the potential of our approach for providing reliable haptic feedback in robotic surgery training and research.

Problem

• non-contact force
• force/torque sensor
• haptic feedback
• robotic surgery training
• measurement accuracy

Innovation

• real-time compensation
• wrist-mounted force/torque sensor
• recursive least squares
• haptic feedback
• robotic surgery training