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arxiv: 2604.27385 · v1 · submitted 2026-04-30 · 💻 cs.RO · cs.HC· cs.SY· eess.SY

An Experimental Modular Instrument With a Haptic Feedback Framework for Robotic Surgery Training

Walid Shaker , Mustafa Suphi Erden This is my paper

Pith reviewed 2026-05-07 10:08 UTC · model grok-4.3

classification 💻 cs.RO cs.HCcs.SYeess.SY
keywords robotic surgeryhaptic feedbackforce sensinglaparoscopic instrumentsurgery traininguser studyforce regulation
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The pith

A wrist-mounted force sensor and haptic framework lets robotic surgery trainees regulate forces more accurately and efficiently than with visuals alone.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to show that a new modular laparoscopic instrument can deliver real-time haptic feedback during robotic surgery training, addressing the loss of touch that raises risks of excessive tissue forces. Current systems often lack affordable haptic options, so the design mounts a force/torque sensor at the wrist to estimate tip forces without the fragility problems of tip sensors. A dedicated framework then extracts those forces and renders stable feedback to a haptic device. In a user study on a force-regulation task, participants using the haptic version posted higher success rates, tighter force control, and quicker completion times compared to visual feedback only.

Core claim

The central claim is that the modular instrument with its wrist-mounted F/T sensor and integrated haptic framework produces stable, perceptually meaningful force feedback, and that this feedback yields significantly higher task success rates, better force regulation accuracy, and greater task efficiency than visual-only conditions in controlled robotic surgery training experiments.

What carries the argument

The wrist-mounted force/torque sensor paired with the real-time haptic feedback framework that extracts external contact forces and renders them to the user device.

If this is right

  • Trainees complete force-sensitive tasks at higher success rates when haptic feedback is available.
  • Force application becomes more accurate and consistent with the addition of haptic cues.
  • Overall task time decreases compared to relying on visual information alone.
  • The modular instrument can be integrated into existing training setups to provide high-fidelity interaction at lower cost than commercial haptic systems.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the force estimates hold across varied tissue types, the instrument could inform designs for clinical robotic platforms that reduce unintended tissue damage.
  • Training programs might test whether the observed gains persist when users move to more complex, multi-step procedures.
  • Wider adoption of such lower-cost haptic tools could shorten the time needed for surgeons to reach safe force-handling proficiency.

Load-bearing premise

The wrist-mounted sensor can accurately estimate tool-tissue forces at the tip while avoiding durability problems, and the haptic system can deliver stable and meaningful sensations.

What would settle it

A controlled replication in which users show no measurable difference in success rate or force error between haptic and visual-only conditions, or in which independent tip-force measurements diverge substantially from the wrist-sensor estimates.

Figures

Figures reproduced from arXiv: 2604.27385 by Mustafa Suphi Erden, Walid Shaker.

Figure 2
Figure 2. Figure 2: Components of the proposed haptic-enabled instrument and its view at source ↗
Figure 4
Figure 4. Figure 4: The complete mapping between coordinate frames required to view at source ↗
Figure 5
Figure 5. Figure 5: RoboScope system architecture showing the proposed instrument view at source ↗
Figure 7
Figure 7. Figure 7: The camera view available to the user during the force regulation view at source ↗
Figure 8
Figure 8. Figure 8: Success rate for each target force (GENTLE 0.6 N, FIRM 1.2 view at source ↗
Figure 9
Figure 9. Figure 9: Average performance metrics (Haptic OFF vs. ON) across all view at source ↗
read the original abstract

Robotic-assisted surgery offers significant clinical advantages but largely eliminates direct haptic feedback, increasing the risk of excessive tool-tissue interaction forces. Although recent commercial systems have begun to introduce force feedback, their high cost limits accessibility, particularly for surgical training. This paper presents a modular experimental robotic laparoscopic instrument integrated with a real-time haptic feedback framework. The proposed instrument employs a wrist-mounted force/torque (F/T) sensor to estimate tool-tissue interaction forces while avoiding the durability and integration challenges of tip-mounted sensors. 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. The instrument is integrated into the robotic surgery training system (RoboScope) and evaluated through a controlled user study involving a force regulation task. Experimental results demonstrate that haptic feedback significantly improves task success rate, force regulation accuracy, and task efficiency compared to visual-only feedback. The proposed instrument enables stable, high-fidelity haptic interaction, supporting effective robotic surgery training.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript presents a modular experimental robotic laparoscopic instrument that uses a wrist-mounted force/torque (F/T) sensor to estimate tool-tissue interaction forces, avoiding the durability issues of tip-mounted sensors. It describes a real-time haptic feedback framework that extracts external contact forces, renders them to a haptic device, and produces stable perceptually meaningful feedback. The instrument is integrated into the RoboScope robotic surgery training system and evaluated in a controlled user study on a force regulation task, with the central claim that haptic feedback significantly improves task success rate, force regulation accuracy, and task efficiency relative to visual-only feedback.

Significance. If the reported user-study gains can be substantiated with sensor validation and statistical detail, the work would offer a practical, lower-cost route to haptic-enabled training for robotic surgery. The wrist-mounted sensor placement and modular instrument design address real integration and durability constraints that have limited prior haptic systems, potentially increasing accessibility for training programs. The empirical focus on force-regulation metrics directly targets a clinically relevant skill gap created by the loss of direct haptic cues in robotic platforms.

major comments (2)
  1. [Methods (Instrument Design)] Methods section on instrument design and force estimation: the claim that the wrist-mounted F/T sensor reliably estimates tool-tissue interaction forces is load-bearing for the entire haptic framework and the user-study results, yet no calibration curves, dynamic compensation equations, comparative error metrics (wrist estimate vs. independent tip measurement or ground-truth load cell), or validation experiments are provided.
  2. [Results (User Study)] Results section on the user study: the abstract asserts that haptic feedback 'significantly improves' success rate, force regulation accuracy, and task efficiency, but the evaluation provides no sample size, statistical tests, p-values, error bars, or raw data summaries. This absence prevents assessment of whether the observed differences are reliable or could be explained by training effects or non-specific cues.
minor comments (2)
  1. [Abstract] Abstract: the terms 'force regulation accuracy' and 'task efficiency' are used without defining the exact quantitative metrics (e.g., RMS force error, completion time) that were measured.
  2. [Figures] Figure captions and text: ensure all hardware diagrams clearly label the wrist-mounted sensor location relative to the tool tip and the haptic rendering pipeline.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and positive assessment of the work's potential significance. We agree with both major comments and will revise the manuscript to incorporate the requested details on sensor validation and statistical reporting.

read point-by-point responses

  1. Referee: Methods section on instrument design and force estimation: the claim that the wrist-mounted F/T sensor reliably estimates tool-tissue interaction forces is load-bearing for the entire haptic framework and the user-study results, yet no calibration curves, dynamic compensation equations, comparative error metrics (wrist estimate vs. independent tip measurement or ground-truth load cell), or validation experiments are provided.

    Authors: We acknowledge that the current manuscript does not provide these supporting elements for the force estimation approach. In the revised version, we will add calibration curves, the dynamic compensation equations applied to the wrist-mounted sensor data, comparative error metrics (including comparisons to independent tip measurements or ground-truth load cells), and results from dedicated validation experiments to demonstrate the reliability of the estimation method. revision: yes

  2. Referee: Results section on the user study: the abstract asserts that haptic feedback 'significantly improves' success rate, force regulation accuracy, and task efficiency, but the evaluation provides no sample size, statistical tests, p-values, error bars, or raw data summaries. This absence prevents assessment of whether the observed differences are reliable or could be explained by training effects or non-specific cues.

    Authors: We agree that the statistical details and supporting data are essential for validating the reported improvements. In the revised manuscript, we will include the sample size, the statistical tests performed with associated p-values, error bars on all relevant figures, and summaries of raw data or descriptive statistics. We will also expand the discussion to address potential confounds such as training effects or non-specific cues. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical user-study results with no derivations or fitted predictions

full rationale

The paper reports development of a modular instrument and a haptic framework, then evaluates performance via a controlled user study on a force regulation task. Central claims (improved success rate, force accuracy, and efficiency with haptic feedback) rest directly on the observed experimental outcomes rather than any mathematical derivation, parameter fitting, or prediction that reduces to prior inputs. No equations, models, or self-citations are invoked to justify the results; the study design provides independent empirical benchmarks. This is the expected non-finding for an instrumentation-and-user-study paper.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The work is purely experimental with no mathematical derivations or fitted constants; it relies only on standard domain assumptions about sensor-based force estimation and haptic stability in robotics.

axioms (2)
  • domain assumption Wrist-mounted F/T sensors can estimate external contact forces with sufficient accuracy for haptic rendering in laparoscopic tasks.
    Invoked when stating that the sensor avoids tip-mounted challenges while still enabling force feedback.
  • domain assumption Real-time extraction and rendering of forces produces stable, perceptually meaningful haptic signals.
    Stated as the goal of the developed haptic feedback framework.

pith-pipeline@v0.9.0 · 5481 in / 1275 out tokens · 86820 ms · 2026-05-07T10:08:02.068305+00:00 · methodology

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Reference graph

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15 extracted references · 15 canonical work pages · 1 internal anchor

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