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REVIEW 4 major objections 6 minor 1 cited by

Optimal Motion Scaling for Delayed Telesurgery

T0 review · 4 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper argues that the optimal motion scaling factor for delayed telesurgery is different for every user, and that a Bayesian regression model trained with other users' data can predict each person's ideal scaling at any level of…

desk verdict A useful proof-of-concept for per-user adaptive motion scaling under delay, but the headline user-specificity claim is not backed by a statistical test and the optimal scaling factor is never validated out-of-sample. read the letter →

arxiv 2506.21689 v1 pith:B6L6SM4H submitted 2025-06-26 cs.RO

Jason Lim , Florian Richter , Zih-Yun Chiu , Jaeyon Lee , Ethan Quist , Nathan Fisher , Jonathan Chambers , Steven Hong
show 1 more author
Michael C. Yip
This is my paper · ORCID
classification cs.RO
keywords telesurgerymotionscalingcommunicationdelayBayesianpolynomialregressionpersonalizedmodelingteleoperationlatencyuserstudy
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Long-distance telesurgery suffers from network delay, and one cheap remedy is motion scaling: reduce the ratio between what the surgeon moves and what the robot moves. The open question this paper attacks is what scaling factor to use at any given delay. Drawing on user studies in a simulated pointing task and a physical robotic peg-transfer task, the paper claims that the best scaling factor is different for each user and tends to decrease as latency rises. It then proposes a personalized Bayesian regression model, seeded with a prior learned from other users' data, that predicts a given user's optimal scaling factor for any delay. If the claim holds, telesurgical systems could automatically tune scaling per surgeon and per latency level at almost no cost, improving both speed and safety.

What carries the argument

The load-bearing object is the personalized Bayesian Polynomial Regression surface $\hat{P}_o(s,d) = \varphi(s,d)^\top \beta$, a second-degree polynomial in scaling factor $s$ and delay $d$ with Gaussian observation noise $\varepsilon \sim N(0,\sigma^2)$ and a Normal-Inverse-Gamma prior on $(\beta,\sigma^2)$. The prior for a new user is obtained by maximising the likelihood of all other users' datasets, so the model's predictions for a new user are Student-$t$ distributions rather than point estimates; the recommended scale is the minimizer of the predictive mean found by discrete search over scale values. The mechanism that carries the argument is the combination of a response surface and a cross-user prior, which converts a few trials from a new operator into a delay-dependent scaling recommendation.

What would settle it

Run a validation study where a new user's model is fit on a few trials and then compared on a full grid of scale and delay values: if the fitted quadratic's recommended scaling factor is no better than the default scale of 1.0 or the best observed grid point in held-out trials, the claimed prediction is falsified. Additionally, fit the same data with a cubic or spline surface and check whether the predicted optimum moves substantially; if it does, the quadratic assumption is driving the result rather than the data.

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Extended reading notes

Core claim

The paper's central claim is that the optimal motion scaling factor is a per-user, delay-dependent quantity: for each operator there exists a value $s^\ast(d) = \operatorname{argmin}_s \hat{P}_o(s,d)$ that balances speed against safety better than a fixed scale, and this value shifts downward as latency grows, but at a user-specific rate. The claim is supported by two user studies: a 2D Fitts-style pointing task with ten participants and a peg-transfer task on the da Vinci Research Kit with simulated round-trip delays. Throughput served as the speed metric, and overshoot and force measures served as error, combined into a weighted performance score $WP = (1-w)\,TP - w\,(OSD+\Delta D)$. The paper then argues that a personalized Bayesian polynomial regression surface can capture this relationship, and that when the regression prior is learned by maximum likelihood from other users' data, the model predicts a new user's optimal scale with less data than an uninformative prior.

Load-bearing premise

The whole optimization depends on assuming that for each person, performance as a function of scaling factor and delay is a smooth bowl-shaped surface plus random scatter, and the paper does not test whether that assumed shape is actually right.

Editorial extensions

If this is right

  • A new user's optimal scaling schedule can be estimated from relatively few calibration trials, because the informed prior transfers information from previously observed users.
  • At higher delays, the model will generally recommend lower scaling factors, matching the observed trend that overshoot and error dominate throughput losses.
  • Telesurgical systems could adjust scaling continuously as measured round-trip delay changes, without hardware changes, since scaling is already implemented in software.
  • The recommended scaling depends on the speed/safety weight $w$; a setting that prioritizes safety will receive a different, lower scaling schedule than one prioritizing speed.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper: because the weighted performance metric encodes a clinical preference, the same fitted model can answer counterfactuals, such as what scaling schedule a safety-first department should use, without additional user studies.
  • Beyond the paper: a natural next experiment is a head-to-head out-of-sample comparison of the BPR-recommended scale versus the nominal default and versus a fixed low scale, measuring whether personalized recommendation actually improves composite performance.
  • Beyond the paper: the reported user-to-user variance suggests that a population-level model with user-specific random effects could estimate optima for an unseen user with even fewer trials than the MLE-prior approach.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper investigates how motion scaling affects teleoperation performance under time delay, using both a simulated 2D Fitts'-law-style task and a physical da Vinci Research Kit peg-transfer task. The authors fit user-specific Bayesian polynomial regression models of performance (throughput, error, and a weighted combination) as a function of scaling factor and latency, then define the optimal scaling factor for a user and delay as the argmin of that fitted surface (Eq. 11). They claim that the optimal scaling factor is user-specific and varies with delay, and that an informative prior learned from other users improves predictive accuracy in terms of mean squared error. The abstract and introduction further claim a statistically significant difference in performance between users, motivating personalized scaling.

Significance. If the central claim is correct, the paper provides a practical, low-cost approach to personalizing motion scaling for telesurgery under delay, with a clear path to deployment using only a small amount of per-user data. The Bayesian prior transfer idea is sensible and the reported reduction in prediction MSE with an informed prior is a useful empirical result. However, the main scientific claim—that a user-specific, delay-dependent optimal scaling factor exists and can be predicted—is not actually validated at the decision level. The optimization is performed on the fitted quadratic surface, but no experiment or held-out analysis shows that the recommended scaling factor improves real performance relative to a fixed or nominal scaling factor. The user-specificity claim also lacks a direct statistical test, and the physical study delays only the command stream, not visual feedback. These gaps are substantial but addressable within the manuscript's scope.

major comments (4)
  1. [§II-C and §III-D] The load-bearing claim is that s*(d) from Eq. (11), obtained by minimizing the fitted performance surface, is the optimal scaling factor for a user. Yet the model is evaluated only by predictive mean squared error (Fig. 6), not by whether the recommended s* actually improves measured performance on held-out trials. Without a decision-level validation (for example, holding out one latency level or one scale, fitting the model, computing s*, and comparing the achieved performance against the nominal scale and the empirical best scale on those held-out trials), the computed optima may be artifacts of the assumed quadratic polynomial shape rather than genuine user-specific optima.
  2. [Abstract and §III-C] The abstract claims 'a statistically significant difference in performance between users,' but no user-factor test is reported for the 2D study; Fig. 2 shows only two example users, and the statistical analysis in Fig. 4 compares scaling factors within the pooled data, not between users. The two-way ANOVA in Table I for the physical study includes latency and scale but not user as a factor. The user-specificity conclusion therefore currently rests on visual inspection of heatmaps rather than a formal statistical test, and the abstract overstates the evidence.
  3. [§IV-A] The physical experiment delays only the commands from the master to the patient-side manipulator; the manuscript explicitly states that 'the stereo camera information is not delayed.' The text then asserts that the buffer delay 'can be considered the round trip delay under the assumption delay is equal in both directions,' but this is an untested assumption. Because visual feedback is not delayed, the physical task does not reproduce the full sensorimotor delay loop of actual telesurgery, and the claim in §V that the physical experiments 'validate the findings from simulated environments' for round-trip-delayed telesurgery is overstated.
  4. [§II-A and §III-D] The performance model in Eq. (2) assumes P_o(s,d) is a second-degree polynomial surface in (s,d) with iid Gaussian noise, with the justification that 'the data seemed to follow a quadratic trend.' No goodness-of-fit diagnostics, residual analysis, or comparison against alternative functional forms (e.g., cubic terms, nonparametric regression) are provided. Since the optimum in Eq. (11) is determined by the curvature of this fitted surface, a misspecified mean function could produce an artificial minimum; this is a load-bearing modeling assumption that needs direct support.
minor comments (6)
  1. [§I-B] The word 'Additionaly' should be 'Additionally'.
  2. [§III-A] In the list of scaling factors, '04' should be '0.4'.
  3. [§III-B] The word 'adusted' should be 'adjusted' in the definition of the weighted performance metric.
  4. [§IV-A] The text has 'FIg. 1' and 'defualt'; these should be 'Fig. 1' and 'default'.
  5. [§II-A, Eq. (9)] Equation (9) appears to be missing a closing parenthesis and possibly an inverse; the predictive distribution for Bayesian polynomial regression is not written correctly, and this should be fixed for reproducibility.
  6. [§III-D] Figure 6 reports mean squared error without error bars or a description of how the mean and variance are computed across users; adding this information would clarify whether the improvement from the informed prior is consistent.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the optimal-scale recommendation is a functional of an explicitly fitted surrogate, and the user-specific claim rests on raw study data rather than on the model; the unvalidated optimum is a correctness risk, not a circular reduction.

full rationale

The derivation chain is self-contained and not circular. The paper fits a Bayesian polynomial regression to observed (performance, scale, delay, user) data (Eq. 2), evaluates prediction quality via MSE across training-set sizes (Fig. 6), and then defines a recommended scale as the argmin of the fitted surface (Eq. 11). This is a surrogate-optimization pipeline: s*(d) is a functional of the fitted model, not a re-statement of the data or of a fitted parameter. The empirical claim that users differ is based on raw heatmaps (Fig. 2) and paired t-tests against the nominal scale (Fig. 4), not on the model itself. The informative prior from other users is a genuine transfer-learning step and is tested by prediction MSE on the target user. The main limitations are evidentiary, not circular: the abstract's 'statistically significant difference between users' is not backed by a user-factor ANOVA (Table I only includes latency and scale), and the recommended s* is never validated by holding out trials and comparing against a fixed scale. These are missing-validation and correctness concerns, which do not make the derivation equivalent to its inputs by construction.

Assumptions & free parameters 3 free parameters · 4 assumptions · 1 invented entities

The modeling contribution rests on a per-user quadratic surface fit. The two fitted objects are the regression coefficients and the informed-prior hyperparameters; the hand-chosen weight w in the weighted performance metric is a third free parameter. The axioms are mostly domain assumptions about metrics and delay simulation, plus an ad hoc quadratic assumption that is not tested. No physical entities are introduced; the weighted performance metric is a constructed objective whose weight is arbitrary.

free parameters (3)
  • Per-user polynomial regression coefficients beta = not reported
    Six coefficients of the quadratic surface in (s,d) are fit per user via Eqs. 5-8; the predicted optimal scaling factor in Eq. 11 is the minimizer of this fitted surface.
  • Informed prior hyperparameters (m,V,a,b) = not reported
    Estimated by maximum likelihood over other users' data (Eq. 10) and used to inform the per-user model; these values are fit to data and not externally validated.
  • Weight w in weighted performance metric = 0.5 in main analysis; 0.1, 1, 2, 5 in Fig. 5
    WP = (1-w)*TP - w*(OSD + DeltaD); the optimal scaling factor changes with w, so the paper's optimality claim depends on a hand-chosen safety/speed tradeoff.
assumptions (4)
  • ad hoc to paper Performance P_o(s,d) is a second-degree polynomial surface in scaling factor and latency with iid Gaussian noise (Eq. 2).
    Adopted after observing that the data seemed to follow a quadratic trend; no goodness-of-fit test or model comparison is reported.
  • domain assumption Throughput, OSD, DeltaD, and force penalty are valid proxies for surgical speed and safety.
    Optimality is defined with respect to these metrics, not clinical outcomes; the physical error metric is force on the pegboard.
  • domain assumption Undelayed stereo video in the dVRK experiment is equivalent to a real round-trip delay.
    Section IV states that camera information is not delayed and the buffer delay is treated as round-trip delay, assuming equal delay in both directions.
  • domain assumption Repeated trials by the same user are independent observations.
    BPR treats residuals as iid; no random effects for users or trial order are modeled, which can inflate significance.
invented entities (1)
  • Weighted performance metric WP
    purpose: To combine throughput and error into a single objective whose minimizer defines the optimal scaling factor.
    No external validation of the weight w is given, and Fig. 5 shows the optimal scale changes with w, so the entity is imposed rather than measured.

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Cite this review

Pith. "Pith review of Optimal Motion Scaling for Delayed Telesurgery." pith.science (2026). https://pith.science/paper/B6L6SM4H

@misc{pith2026250621689,
  author       = {Pith},
  title        = {Pith review of: Optimal Motion Scaling for Delayed Telesurgery},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/B6L6SM4H}},
  note         = {Machine review of arXiv:2506.21689}
}
read the original 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.

Figures

Figures reproduced from arXiv: 2506.21689 by the authors.

Figure 1
Figure 1. Motion scaling scales the leaders motion, [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Heatmaps of the results from the 2D user study illustrate the two [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. A plot of the mean and one standard deviation shown for optimal [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Paired-sample t tests are conducted for each scaling factor against the nominal scaling factor of 1.0, demonstrating that reduced scaling factors lead [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Heatmaps of the weighted performance metric for one user with different values of [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: Comparing the mean squared error for informed vs. noninformed [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: Mean and standard deviation plots of the throughput and force metrics [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Spatiotemporal Context-dependent Personalized Movement Compensation in Delayed Telemanipulation

    cs.RO 2026-08 conditional novelty 5.0 of 10

    Personalized, context-dependent motion scaling improves delayed telemanipulation performance in simulation and transfers to a real surgical robot with reduced benefit.

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.