Robot-Assisted Minimally Invasive Surgery (RAMIS) enhances surgeon dexterity and precision, with newer platforms leveraging haptic feedback to further improve performance. Such force information has broader potential to inform performance assessment, tactile localization, and surgical autonomy. This motivates the need for accessible approaches to integrating force sensing into RAMIS tools more broadly, to facilitate research and development of these next-generation capabilities.
This work presents a method for integrating a six-axis commercial force sensor into the distal end of a standard cable-driven surgical instrument, enabling end-effector force measurement while preserving the original mechanical functionality of the device. The proposed design emphasizes reproducibility and accessibility for research applications, requiring no specialized manufacturing tools.
In addition, a transformer neural network integrates force sensor measurements with robot state information to aid estimation of applied forces at the end-effector, compensating for internal cable forces arising from actuation. On average, our shaft-integrated approach achieves <4% error, surpassing similar distal approaches, and is comparable to existing proximal approaches. This balance of system integrability and performance thus enables applications and research into timely topics of haptic feedback, skill assessment, and force-informed autonomy in RAMIS, offering a versatile platform for advancing the development and evaluation of intelligent surgical systems.
Overview of the shaft-integrated force sensor modification. The design integrates a commercial six-axis force sensor into the distal end of a standard cable-driven surgical instrument, enabling end-effector force measurement while preserving the original mechanical functionality.
@article{boone2026shaft,
author = {Boone, Grant and Yang, Shuyuan and Markert, Timo and Matich, Sebastian and Theissler, Andreas and Atzmueller, Martin and Chua, Zonghe},
title = {Shaft-integrated Force Sensing with Transformer-based Dynamics Compensation for Telesurgery},
journal = {TBD},
year = {2026},
}