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

• URL: http://arxiv.org/abs/2604.23696v1
• Date: Sun, 26 Apr 2026 13:21:45 GMT
• Title: Real-Time Non-Contact Force Compensation for Wrist-Mounted Force/Torque Sensors in Haptic-Enabled Robotic Surgery Training
• Abstract summary: 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.
• Score: 1.6206092886889898
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Overcoming Imperfect Kinematics in Surgical Robotics Through Sim-to-Real Visuomotor Learning [3.24203536935265]
  This work uses a learning-policy to actively compensate for hardware inaccuracies using closed-loop visual feedback that was trained from a teacher-student learning framework.<n>The policy can fuse unreliable internal readings with precise external visual data, allowing it to correct for kinematic errors in real time without needing a perfect physical model.<n>This research provides a foundational and reliable control methodology, paving the way for more advanced and robust surgical automation.
  arXiv  Detail & Related papers  (2026-06-19T13:02:35Z)
• AetheRock: An Arm-Worn Robot Teaching System for Force-Guided Vision-Tactile Learning [56.20593626288444]
  AetheRock is an arm-worn device featuring a modular and easily manufactured visuo-tactile sensor, GelSlim-MiniFab, at the fingertip.<n>We propose ForceVT, a representation learning framework that uses force and vision to guide fidelity-agnostic tactile learning.
  arXiv  Detail & Related papers  (2026-06-08T17:38:57Z)
• Shaft-integrated Force Sensing with Transformer-based Dynamics Compensation for Telesurgery [9.235166181120494]
  Robot-Assisted Minimally Invasive Surgery (RAMIS) enhances surgeon dexterity, with newer platforms leveraging haptic feedback to further improve performance.<n>Force information has broader potential to inform performance assessment, tactile localization, and surgical autonomy.<n>This work presents a method for integrating a six-axis commercial force sensor into the distal end of a standard cable-driven surgical instrument.
  arXiv  Detail & Related papers  (2026-05-29T15:28:39Z)
• EIT-Pneumatic Hybrid Robotic Skin for Practical and Accurate Force Map Reconstruction [6.554973825521011]
  The developed robotic skin is fabricated entirely by 3D printing and spray coating, making it affordable and easy to build.<n>A Tikhonov-regularized inverse reconstruction, paired with per-pad pneumatic calibration, enables accurate large-area tactile sensing.
  arXiv  Detail & Related papers  (2026-05-27T13:34:23Z)
• An Experimental Modular Instrument With a Haptic Feedback Framework for Robotic Surgery Training [1.6206092886889898]
  The paper presents a modular experimental robotic laparoscopic instrument integrated with a real-time haptic feedback framework.<n>The proposed instrument employs a wrist-mounted force/torque (F/T) sensor to estimate tool-tissue interaction forces.<n>A haptic feedback framework is developed to extract the external contact forces, render them to the haptic device, and generate stable and perceptually meaningful feedback.
  arXiv  Detail & Related papers  (2026-04-30T03:55:05Z)
• A Rapid Instrument Exchange System for Humanoid Robots in Minimally Invasive Surgery [9.137620295313711]
  This paper proposes an immersive teleoperated rapid instrument exchange system.<n>The system utilizes a low-latency mechanism based on single-axis compliant docking and environmental constraint release.
  arXiv  Detail & Related papers  (2026-04-03T04:05:00Z)
• Feel Robot Feels: Tactile Feedback Array Glove for Dexterous Manipulation [29.061513339025026]
  We present TAG, a low-cost glove system that integrates precise hand motion capture with high-resolution tactile feedback.<n>For motion capture, TAG employs a non-contact magnetic sensing design that provides drift-free, electromagnetically robust 21-DoF joint tracking.<n>We show that TAG enables reliable real-time perception of contact geometry and dynamic force, improves success rates in contact-rich teleoperation tasks, and increases the reliability of demonstration data collection for learning-based manipulation.
  arXiv  Detail & Related papers  (2026-03-30T15:04:13Z)
• A Robotic Testing Platform for Pipelined Discovery of Resilient Soft Actuators [63.07587517846448]
  We propose an optimization pipeline enabled by a novel testing robot capable of scanning DEA lifetime.<n>The robot integrates electro-mechanical property measurement, programmable voltage input, and multi-channel testing capacity.<n>Using it, we scanned the lifetime of Elastosil-based linear actuators across parameters including input voltage magnitude, frequency, electrode material concentration, and electrical connection filler.
  arXiv  Detail & Related papers  (2026-02-24T14:41:28Z)
• UniForce: A Unified Latent Force Model for Robot Manipulation with Diverse Tactile Sensors [51.88112610411651]
  We propose a novel unified tactile representation learning framework that learns a shared latent force space across diverse tactile sensors.<n>UniForce reduces cross-sensor domain shift by jointly modeling inverse dynamics (image-to-force) and forward dynamics (force-to-image)<n>To avoid reliance on expensive external force/torque (F/T) sensors, we exploit static equilibrium and collect force-paired data via direct sensor--object--sensor interactions.
  arXiv  Detail & Related papers  (2026-02-01T11:03:01Z)
• Learning-based Force Sensing and Impedance Matching for Safe Haptic Feedback in Robot-assisted Laparoscopic Surgery [0.0]
  Impedance Matching Approach (NIMA) incorporates nonlinear dynamics to accurately model and render complex tool-tissue interactions in real-time.<n>NIMA achieves a mean absolute error of 0.01 (std 0.02 N), representing a 95% reduction compared to previous approaches.<n>NIMA significantly improves force fidelity, responsiveness, and precision across various surgical conditions.
  arXiv  Detail & Related papers  (2026-01-20T19:57:50Z)
• Proprioceptive External Torque Learning for Floating Base Robot and its Applications to Humanoid Locomotion [17.384713355349476]
  This paper introduces a method for learning external joint torque solely using proprioceptive sensors (encoders and IMUs) for a floating base robot. Real robot experiments demonstrate that the network can estimate the external torque and contact wrench with significantly smaller errors. The study also validates that the estimated contact wrench can be utilized for zero moment point (ZMP) feedback control.
  arXiv  Detail & Related papers  (2023-09-08T05:33:56Z)
• Automatic diagnosis of knee osteoarthritis severity using Swin transformer [55.01037422579516]
  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. We propose an automated approach that employs the Swin Transformer to predict the severity of KOA.
  arXiv  Detail & Related papers  (2023-07-10T09:49:30Z)
• NeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesis [50.93065653283523]
  SPARTN (Synthetic Perturbations for Augmenting Robot Trajectories via NeRF) is a fully-offline data augmentation scheme for improving robot policies. Our approach leverages neural radiance fields (NeRFs) to synthetically inject corrective noise into visual demonstrations. In a simulated 6-DoF visual grasping benchmark, SPARTN improves success rates by 2.8$times$ over imitation learning without the corrective augmentations.
  arXiv  Detail & Related papers  (2023-01-18T23:25:27Z)
• Online Body Schema Adaptation through Cost-Sensitive Active Learning [63.84207660737483]
  The work was implemented in a simulation environment, using the 7DoF arm of the iCub robot simulator. A cost-sensitive active learning approach is used to select optimal joint configurations. The results show cost-sensitive active learning has similar accuracy to the standard active learning approach, while reducing in about half the executed movement.
  arXiv  Detail & Related papers  (2021-01-26T16:01:02Z)
• Learning Compliance Adaptation in Contact-Rich Manipulation [81.40695846555955]
  We propose a novel approach for learning predictive models of force profiles required for contact-rich tasks. The approach combines an anomaly detection based on Bidirectional Gated Recurrent Units (Bi-GRU) and an adaptive force/impedance controller.
  arXiv  Detail & Related papers  (2020-05-01T05:23:34Z)
• Efficiently Calibrating Cable-Driven Surgical Robots with RGBD Fiducial Sensing and Recurrent Neural Networks [26.250886014613762]
  We propose a novel approach to efficiently calibrate such robots by placing a 3D printed fiducial coordinate frames on the arm and end-effector that is tracked using RGBD sensing. With the proposed method, data collection of 1800 samples takes 31 minutes and model training takes under 1 minute. Results on a test set of reference trajectories suggest that the trained model can reduce the mean tracking error of the physical robot from 2.96 mm to 0.65 mm.
  arXiv  Detail & Related papers  (2020-03-19T00:24:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.