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REVIEW 2 major objections 7 minor 1 cited by

Effects of Robot Competency and Motion Legibility on Human Correction Feedback

T0 review · 2 major / 7 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A robot's apparent competence systematically biases human correction feedback, violating assumptions of learning-from-corrections algorithms.

desk verdict Useful first test of how competency and legibility bias correction feedback, but the headline RQ1 p-values overstate confidence by treating repeated corrections as independent. read the letter →

arxiv 2501.03515 v1 pith:QPNYWP2H submitted 2025-01-07 cs.RO

Shuangge Wang , Anjiabei Wang , Sofiya Goncharova , Brian Scassellati , Tesca Fitzgerald This is my paper · ORCID
classification cs.RO
keywords learningfromcorrectionsrobotcompetencymotionlegibilityhuman-robotinteractionkinestheticteachingcorrectionfeedbackuserstudytaskobjectivedivergence
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

The paper tries to establish that two features of a robot's behavior—its apparent competency and the legibility of its motions—systematically change how people supervise and correct it, violating the assumptions implicit in most learning-from-corrections (LfC) algorithms. In a 60-participant study of people correcting a robot arm during pick-and-place tasks, the authors find that people correct a highly competent robot earlier and at smaller task-objective divergences than an incompetent robot when motions are legible or predictable, and that they miss more necessary corrections for incompetent robots while giving more unnecessary corrections to competent ones. They also confirm a positive correlation between physical effort and correction precision overall, but show the correlation weakens significantly for an incompetent robot with legible motions. If correct, these results mean correction data cannot be read as clean, objective labels: the robot's own track record shapes the signal the human produces.

What carries the argument

The engine of the study is a between-subject 2×3 design: 60 participants each supervised a Kinova Gen3 robot arm through 64 pick-and-place trials, with competency set by the robot's intended success rate (25% vs 75%) and legibility by the style of the executed trajectories (predictable, legible, or illegible, the first generated as efficient RRT* paths and the others by optimizing a legibility score). The load-bearing measures are task-objective divergence (the Kullback-Leibler divergence between the goal distribution inferred from the robot's partial motion and the true goal), time until the first correction, the fraction of the intended trajectory left untraveled at correction, missed and unnecessary correction rates framed as a confusion matrix, and the Spearman correlation between correction precision and physical effort. These measures translate raw physical corrections into quantities that can be directly compared against the three LfC assumptions the paper targets.

What would settle it

Re-analyze the RQ1 data with mixed-effects models including participant as a random intercept and random slopes for the conditions; if the competency-by-legibility interactions on divergence, time-to-correction, or trajectory-untraveled cease to be significant, the claim that apparent competence shifts correction timing fails.

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Extended reading notes

Core claim

The central claim is that the robot's displayed competence shifts both when people intervene and how accurate their intervention labels are, in the opposite direction of what a simple trust story predicts. People supervising a highly competent robot corrected it earlier in its trajectory and at significantly smaller task-objective divergence than people supervising an incompetent robot, for both legible (p=0.0015 for divergence) and predictable (p=0.0055) motions; the same pattern appeared for time until correction and proportion of trajectory untraveled. Missed necessary corrections were far more common in low-competency conditions (11.3% vs 2.8%, p<0.0001), while unnecessary corrections were more common in high-competency conditions (9.8% vs 2.0%, p=0.0171). The authors interpret this as people holding competent robots to a higher standard and giving incompetent robots the benefit of the doubt. The precision–effort tradeoff held, but the correlation was significantly weaker for an incompetent robot with legible motions than for the same robot with predictable motions (p=0.0075).

Load-bearing premise

The timing and accuracy results treat each of the roughly 1,944 corrections as an independent observation, even though they come from just 60 participants, so unmodeled within-person correlations could drive the significant interaction effects.

Editorial extensions

If this is right

  • Algorithms that learn from corrections should stop treating the absence of a correction as an endorsement, especially for low-competency robots, where people systematically miss necessary corrections.
  • For high-competency robots, a higher rate of unnecessary corrections means algorithms should down-weight corrections as evidence of task-constraint violations.
  • The precision–effort tradeoff cannot be assumed uniformly; in the incompetent-plus-legible condition, effort is a much weaker guide to correction quality.
  • Interaction designers can steer feedback quality: competent robots should minimize deviating behavior, while incompetent robots can explore with less risk of triggering misleading corrections.
  • Robot learning evaluations should record or control the robot's apparent competency and motion legibility, since both change the meaning of the feedback signal.

Reading between the lines

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

  • If this effect generalizes, a robot learning from corrections will need an estimate of how competent the human believes it is, because identical corrections carry different task information depending on that belief.
  • The trust-based hypotheses predict tolerant correction of competent robots; the data show the opposite, pointing to expectation-based strictness. A modeling consequence is that correction thresholds should be functions of expected competence, not just task divergence.
  • The design confounds the robot's reputation with the robot's actual error distribution, so a cleaner test of the reputation effect would hold the robot's behavior fixed while merely varying the competence label or the participant's prior information.
  • Because the effort–precision correlation collapsed only for the incompetent-plus-legible condition, a testable extension is to vary legibility continuously and measure whether the correlation drop tracks perceived goal clarity.
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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

2 major / 7 minor

Summary. The paper reports a between-subject user study (N=60; 2 competency levels × 3 legibility levels, 10 participants per condition) in which participants supervised and kinesthetically corrected a robot performing 64 pick-and-place trials. The authors measure three families of outcomes: timing of corrections (task-objective divergence, time until correction, proportion of trajectory untraveled), prediction accuracy (missed and unnecessary correction rates, false omission and false correction rates), and the precision–effort tradeoff in corrections (Spearman correlations between a precision metric and integrated torque). They report that people correct a highly competent robot at smaller task-objective divergence and earlier in the trajectory than an incompetent robot when motions are legible or predictable; that people withhold necessary corrections from incompetent robots and give unnecessary corrections to competent robots; and that physical effort is positively correlated with correction precision overall, but significantly less so for an incompetent robot with legible motions than for an incompetent robot with predictable motions. The paper interprets these findings as evidence against three common assumptions in Learning from Corrections and offers design and learning-algorithm recommendations.

Significance. If the headline effects are real, the paper makes a valuable contribution: it provides one of the first empirical demonstrations that a robot's apparent competency biases the timing and necessity of human correction feedback, which has direct implications for LfC algorithms that treat corrections as noisy labels generated from a stable threshold. The RQ2 per-participant confusion-matrix analyses are methodologically sound (F(1,54), with one summary rate per participant), and the empirical test of the precision–effort tradeoff addresses an assumption that is widely used but rarely validated. The concrete recommendations for interaction designers and learning researchers are appropriately grounded in the results. The main limitation, acknowledged in Sec. VI.D, is that the RQ1 and RQ3 analyses treat trial-level data as independent despite nesting within participants, which undermines the headline p-values until reanalysis is performed. The strength of the RQ2 evidence, together with the fixability of the statistical issue, makes the paper suitable for major revision rather than rejection.

major comments (2)
  1. [V-A (Figs. 5–7)] The RQ1 two-way ANOVAs treat each correction as an independent observation, with error degrees of freedom around 1944, but the corrections are nested within only 60 participants (up to 64 trials per participant). Within-participant responses are likely correlated due to individual divergence thresholds, attention levels, and calibration to the robot's error rate, so the effective sample size is far smaller than the analysis assumes. This makes the reported p-values (e.g., p=0.0015 for legible high vs. low competency in task-objective divergence; p<0.0001 for time until correction and proportion of trajectory untraveled) anti-conservative. The manuscript itself acknowledges this in Sec. VI.D. Because the abstract presents these p-values without qualification, the central RQ1 claims are load-bearing. Please reanalyze RQ1 using linear mixed-effects models with participant as a random intercept (or participant-level aggregated means) and report whether the direction and significance of the high- vs. low-competency comparisons within the legible and predictable conditions survive.
  2. [V-C and Table I/II] The RQ3 Spearman correlations are computed on corrections pooled across participants within each condition (n ranges from 153 to 482 per cell), and the pairwise Fisher z-tests in Table II treat these observations as independent. Precision and effort values from the same participant are likely correlated, which would affect both the correlation estimates and their standard errors. The only significant pairwise difference (legible low vs. predictable low, p=0.0075) is based on n=468 and n=461 corrections from just 10 participants per condition, so this result is particularly vulnerable to clustering. Please reanalyze using cluster-robust methods (e.g., bootstrap resampling by participant) or by computing per-participant correlations and testing the difference at the participant level, and report the updated pairwise comparisons.
minor comments (7)
  1. [Abstract and Sec. I] The phrase "we present an between-subject user study" should be "a between-subject user study."
  2. [Sec. V-A] In the time-until-correction results, the sentence "people correct a competent robot earlier in high-competency conditions" is unclear; it should read "people correct a highly competent robot earlier in high-competency conditions than in low-competency conditions."
  3. [Sec. V-A] In the proportion-of-trajectory results, the text contains "in in low-competency conditions" (duplicate preposition) in the legible comparison; please fix this typo.
  4. [Sec. VI.A] The sentence beginning "This which strongly supports the inverse of H1B" is missing a word or should be rephrased, e.g., "This result strongly supports the inverse of H1B."
  5. [Appendix VIII-A] The precision metric normalizes EEF position error and rotation error "by their mean and standard deviation," but it is not stated whether the mean and SD are computed across all corrections, per participant, or per condition; please specify the normalization sample for reproducibility.
  6. [Sec. V-A and V-C] The paper reports raw p-values throughout but states that the Benjamini–Hochberg procedure was applied; please clarify whether the reported p-values are adjusted, and if they are raw, state which comparisons remained significant after adjustment.
  7. [Sec. V-C and Table II] For the significant correlation difference (legible low vs. predictable low, p=0.0075), please also report an effect size or confidence interval for the difference between correlations, not only the p-value.

Circularity Check

0 steps flagged · score 1.0 of 10

No circular derivation: the results are empirical comparisons between manipulated conditions and measured behaviors, not outputs forced by the measures or by self-citations; the disclosed non-independence caveat is a statistical-validity concern, not circularity.

full rationale

This paper is an empirical user study, so its derivation chain is the mapping from manipulated independent variables (robot competency, motion legibility) to measured dependent variables (correction timing, correction rates, precision-effort correlation). I checked each load-bearing link. The task-objective-divergence, time-until-correction, and untraveled-proportion measures are defined independently of the experimental conditions and are not algebraically equal to the hypotheses they support; for example, KLD at correction time is computed from the robot's motion distribution and the correct goal, not from the participant's condition label. The RQ2 confusion-matrix rates are conditional proportions, and the competency main effects are not forced by the 25%/75% success-rate manipulation: missed-correction rate divides by intended failures and unnecessary-correction rate divides by intended successes, so the base rates do not by themselves determine the direction of the reported differences. RQ3 is a measured Spearman correlation, not a fitted parameter renamed as a finding. The only self-citations ([23], [30]) appear in related-work descriptions of LfC assumptions and legibility-aware LfC; the present statistical results do not rest on them. The paper's own limitation in Sec. VI.D, noting that trial-level ANOVAs may inflate degrees of freedom due to within-participant correlation, is a real statistical caveat about p-values, but it is not circularity: it concerns independence of observations, not equivalence between inputs and outputs. No step reduces by construction to its own input, so no circularity steps are listed.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

This is an empirical user study with no new theoretical entities. The central claims rest on participant behavior measures and statistical assumptions. The main free parameter is the researcher-chosen precision weighting. The key questionable axiom is trial-level independence, disclosed by the authors.

free parameters (1)
  • Precision metric feature weights = 0.5 position, 0.5 rotation
    Precision is computed as an equally weighted linear combination of normalized EEF position and rotation errors to the correct goal (Appendix VIII-A). The weights are chosen by the authors, not estimated from data or theory.
assumptions (4)
  • domain assumption Each trial from each participant is treated as independent in RQ1 and RQ3 analyses.
    The paper assumes trial-level independence to use 1,944 corrections in RQ1 analyses. This is explicitly flagged in Sec VI.D as potentially inflating degrees of freedom due to within-participant correlation.
  • domain assumption Participant corrections reflect the measured constructs (task objective divergence, precision, physical effort) as operationalized.
    The study assumes the chosen metrics (KLD, precision linear combination, torque L2 norm) capture the intended psychological constructs.
  • standard math ANOVA is robust to non-normal data.
    Invoked in Sec V to justify ANOVA on non-normal measures, citing Blanca et al. 2017.
  • domain assumption Competency is operationalized as a 25% vs 75% intended success rate.
    The manipulation of competency is defined by the proportion of intended failures, which may not match a user's subjective sense of competence.

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Pith. "Pith review of Effects of Robot Competency and Motion Legibility on Human Correction Feedback." pith.science (2026). https://pith.science/paper/QPNYWP2H

@misc{pith2026250103515,
  author       = {Pith},
  title        = {Pith review of: Effects of Robot Competency and Motion Legibility on Human Correction Feedback},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QPNYWP2H}},
  note         = {Machine review of arXiv:2501.03515}
}
abstract

As robot deployments become more commonplace, people are likely to take on the role of supervising robots (i.e., correcting their mistakes) rather than directly teaching them. Prior works on Learning from Corrections (LfC) have relied on three key assumptions to interpret human feedback: (1) people correct the robot only when there is significant task objective divergence; (2) people can accurately predict if a correction is necessary; and (3) people trade off precision and physical effort when giving corrections. In this work, we study how two key factors (robot competency and motion legibility) affect how people provide correction feedback and their implications on these existing assumptions. We conduct a user study ($N=60$) under an LfC setting where participants supervise and correct a robot performing pick-and-place tasks. We find that people are more sensitive to suboptimal behavior by a highly competent robot compared to an incompetent robot when the motions are legible ($p=0.0015$) and predictable ($p=0.0055$). In addition, people also tend to withhold necessary corrections ($p < 0.0001$) when supervising an incompetent robot and are more prone to offering unnecessary ones ($p = 0.0171$) when supervising a highly competent robot. We also find that physical effort positively correlates with correction precision, providing empirical evidence to support this common assumption. We also find that this correlation is significantly weaker for an incompetent robot with legible motions than an incompetent robot with predictable motions ($p = 0.0075$). Our findings offer insights for accounting for competency and legibility when designing robot interaction behaviors and learning task objectives from corrections.

Figures

Figures reproduced from arXiv: 2501.03515 by the authors.

Figure 1
Figure 1. The robot begins moving along the yellow trajectory [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. A comparison between predictable (solid lines) and [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. This figure depicts the series of pick-and-place tasks performed by the robot. The tasks involved manipulating various [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: In an intended-success trial, the robot will attempt to [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Task Objective Divergence: The KLD between the estimated robot’s task belief distribution and the actual, correct motion goal [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 10
Figure 10. Figure 10: False Omission Rate: The pro￾portion of uncorrected trials that were intended failures. between these two variables. Overall, the trend in each con￾dition exhibited a weak positive correlation (Table I in the Appendix). After adopting a Fisher z-transformation on the …
Figure 11
Figure 11. Figure 11: False Correction Rate: The proportion of corrected [PITH_FULL_IMAGE:figures/full_fig_p012_11.png]

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.