Pith. sign in

REVIEW 4 major objections 5 minor 5 references

Non-experts interpret a robot's task success rate the way experts do, and they also want failure-case descriptions and robot self-estimates before trusting it with novel tasks.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · deepseek-v4-flash

2026-08-03 04:48 UTC pith:C6L2YRXK

load-bearing objection First novice-focused study of RFM performance reporting, with useful findings on failure cases and related-task data, but the claim that TSR 'works as intended' is weaker than the abstract suggests. the 4 major comments →

arxiv 2602.03920 v2 pith:C6L2YRXK submitted 2026-02-03 cs.RO cs.HC

How Users Understand Robot Foundation Model Performance through Task Success Rates and Beyond

Isaac Sheidlower , Jindan Huang , James Staley , Bingyu Wu , Qicong Chen , Reuben Aronson , Elaine Short This is my paper
classification cs.RO cs.HC
keywords robot foundation modelstask success ratehuman-robot interactionuser studyfailure casesperformance evaluationtrust in automationinterpretability
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper asks whether ordinary people who would use a general-purpose home robot understand its most common performance metric—task success rate, the ratio of successful attempts to total attempts—the way roboticists intend. Using real evaluation data from published robot foundation models, an online study (112 participants) found a clear positive correlation between reported success rates and users' trust and confidence in the robot, confirming that non-experts read TSR as experts expect. Users also reported that natural-language failure-case descriptions are valuable, and many asked for both real data from similar tasks and robot-generated estimates of success on never-before-seen tasks. The authors conclude that robot foundation model evaluations and deployment interfaces should report failure cases, provide access to related-task data, and supply calibrated self-estimates, so users can make informed choices about novel requests. An in-person study with a physical robot (14 participants) mirrored these preferences, adding concerns about speed, workspace, and physical damage.

Core claim

On its own terms, the paper's central discovery is that non-expert users treat task success rate as a trustworthy signal of robot capability: higher TSR values are strongly correlated with higher user-reported trust and comfort, and only the estimated success rate significantly improved users' ability to predict whether the robot would succeed. Users did not stop at TSR, however. They rated failure-case descriptions as useful, showed a strong appetite for real evaluation data from similar tasks, and wanted the robot itself to estimate its performance on novel tasks. The authors take this as evidence that robot foundation model evaluations and deployment interfaces should standardize failure-

What carries the argument

The study's central machinery is a set of four information types: estimated task success rate (a robot's internal estimate), estimated failure case (a natural-language description of the most likely failure), related-task success rate (real data from a similar task), and related-task failure case (a real failure description from a similar task). These were shown to 112 online participants across 16 real robot foundation model evaluation tasks, paired in all combinations, and to 14 in-person participants watching a physical robot attempt a shelving task. The load-bearing result is the correlation between reported success rates and users' trust and confidence, extracted from Likert-scale respo

Load-bearing premise

The study presents true evaluation results to participants as if they were the robot's own estimates, so the observed trust in estimates rests on the untested assumption that genuine robot-generated estimates—which can be wrong or uncertain—would be received the same way.

What would settle it

Give users a robot whose self-reported success estimate is deliberately miscalibrated (say, 90% claimed but 50% actual) and measure whether their trust and willingness to let it operate follow the number or the robot's observed behavior; if trust tracks the inaccurate number rather than the robot's true reliability, the recommendation that robot foundation models provide self-estimates is not supported. Alternatively, a calibration study comparing user confidence to actual success rates across many tasks would check whether 'used as intended' means users' expectations match the true success ra

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Robot foundation model evaluation reports should routinely publish natural-language failure-case descriptions alongside success rates, not just success percentages.
  • Deployment interfaces should give users access to a history of evaluations on similar tasks, with some measure of task similarity made explicit.
  • Robot foundation models should be designed to provide calibrated self-estimates for novel tasks, since users explicitly ask for them and use them to decide whether to supervise or intervene.
  • Because only estimated success rate improved binary outcome prediction, interfaces that rely on success rate alone may leave users under-informed about the chance of failure.
  • Users' reported information needs extend to speed, physical capability, and robustness to environmental changes, suggesting evaluation benchmarks should track these dimensions.

Where Pith is reading between the lines

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

  • If real robot-generated estimates are less accurate than the data-as-estimates used here, user trust could diverge sharply; a field test with actual estimators is the direct extension of this work.
  • Users' threshold language (e.g., 'below 70% felt iffy') suggests interfaces could offer interactive success-rate thresholds or alerts rather than raw numbers.
  • Users' desire to know 'how the robot learns from past mistakes' hints that they treat robots as improvable agents, so evaluations may eventually need learning or adaptation metrics to sustain trust.
  • Embodied presence shifted user concerns toward physical damage and workspace footprint, so information needs likely depend on the deployment context and task stakes.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper reports two user studies on how non-expert users interpret robot foundation model (RFM) evaluation information. The online study (n=112) presented 16 real evaluation tasks from three published RFM projects and varied four information types: estimated task success rate (ETSR), estimated failure case (EFC), related-task TSR (RT-TSR), and related-task failure case (RT-FC). The in-person study (n=14) used a physical robot to check whether embodiment changes information preferences. The paper claims that non-experts interpret TSR consistently with expert expectations, that natural-language failure-case descriptions are highly valued, and that users want both real evaluation data from similar tasks and robot-provided performance estimates for novel tasks. Based on these findings it recommends that RFM evaluations report failure cases and that deployments provide related-task data and reliable performance estimates.

Significance. If the central claims hold, this is a useful and timely empirical contribution to HRI and RFM evaluation. The paper uses real evaluation data, includes manipulation checks, uses Bayesian analyses and qualitative coding with published codebooks, and provides appendices with task lists, questions, and instructions. The qualitative findings about users wanting failure-case descriptions and related-task data are plausible and well supported by the coded open responses. However, the quantitative evidence for the headline claim that users 'use TSR as intended' is weaker than the text suggests. The TSR-trust correlation is potentially confounded by task-specific stimulus properties, the only value-sensitivity analysis reports prediction accuracy below the mean TSR base rate, and the 'estimates' shown to participants are actually true evaluation outcomes relabeled as estimates. These gaps are load-bearing for the paper's main recommendation that RFMs should provide performance estimates, and they need to be addressed before the central claim can be accepted.

major comments (4)
  1. [§4.2, Quantitative (TSR–comfort correlation)] The claim that users interpret TSR as intended rests on the correlation between each task's actual TSR and reported comfort/trust (R²=.26). TSR is a fixed attribute of each of the 16 tasks, so this correlation may be driven by the task request, the still image, the visible robot arm, or perceived task difficulty, not by the displayed TSR value. The study included a no-information condition, but no analysis is reported for that subset. Please report the TSR–trust/comfort correlation separately for no-information trials. If a substantial correlation persists without any TSR display, the headline validation is an artifact of task stimulus properties. If it does not persist, the analysis should still be conditional on information presence to show that the displayed number, not task identity, drives the effect.
  2. [§4.2, Quantitative (binary prediction accuracy)] The only analysis attempting to test value sensitivity reports that ETSR significantly improves binary success prediction, with users correct 61.9% of the time. Since the mean TSR is approximately 66%, always predicting success would yield ~66% accuracy; the reported accuracy is therefore below the base rate. To support the claim that users are calibrated to TSR, report the base rate in the ETSR-present condition and the prediction accuracy in the no-information condition. Also report a confusion matrix or calibration curve. The current numbers are hard to reconcile with the conclusion that non-experts use TSR as experts intend.
  3. [§4.1, Procedure (ETSR/EFC construction)] The paper states that 'the data used for ETSR and EFC was real data, but was framed as estimates to participants.' This means participants saw perfect, noise-free estimates. Real robot-generated estimates would include error and uncertainty, which can substantially change user trust and decision-making. The recommendation that RFMs should provide self-estimates therefore goes beyond what the data can support. This should be explicitly acknowledged as a limitation, and the deployment recommendation should be qualified or tested with estimates that include realistic error.
  4. [§4.2, Quantitative (RM-ANOVA high/low definition)] The RM-ANOVA is described as showing that 'if the robot has a high ETSR and a high RT-TSR, user comfort increases.' The manuscript does not define how 'high' versus 'low' ETSR/RT-TSR was determined, nor does it report the thresholds, cell means, or interaction effects. Without this information the analysis is not reproducible, and the reader cannot tell whether the effect is driven by the numerical value or merely by the presence of any success-rate information. Please specify the coding and provide the full ANOVA table or cell means.
minor comments (5)
  1. [General] Typos and formatting: 'Wilcoxon singed-rank test' should be 'signed-rank'; 'computate limitations' should be 'computational limitations'; 'wholistically' should be 'holistically'; 'start-of-the-art' should be 'state-of-the-art'; and 'RM-ANOV A' appears with an errant space.
  2. [References] Some reference author initials are corrupted (e.g., 'Y' instead of 'V' or 'Y'), and the reference list would benefit from a careful proofread.
  3. [Appendix A] The example task 'Put away ball' uses TinyVLA, which is not listed in Table 1. Clarify that this was a practice example and that the 16 analyzed tasks exclude it.
  4. [§4.2, Quantitative] Multiple RM-ANOVAs and t-tests are reported without correction for multiple comparisons. While the main effects are large (BF > 1000), the paper should state whether any correction was applied or justify not applying one.
  5. [§4.2, Qualitative] The qualitative coding is described as inductive with two independent labelers, but inter-rater reliability statistics (e.g., Cohen's kappa) are not reported. This would strengthen confidence in the code counts.

Circularity Check

0 steps flagged

No significant circularity; empirical user study with an acknowledged estimate-grounding limitation but no derived-prediction loop.

full rationale

This is an empirical user study rather than a derivation, and the central claims (TSR correlates with comfort/trust; failure cases are valued; users want real related-task data and robot estimates) are direct measurements from participant responses. There is no fitted parameter later renamed as a prediction, no uniqueness theorem imported from the authors' prior work, and no equation by which an output equals an input by construction. The one place where inputs and presented stimuli coincide is the use of real evaluation data as the ETSR/EFC 'estimates' (§4.1: 'The data used for ETSR and EFC was real data, but was framed as estimates to participants. This ensured that ETSR and EFC were accurate and could be presented without developing an estimator.'). This is an explicitly acknowledged construct-validity limitation — the study cannot speak to how users would treat inaccurate or uncertain robot-generated estimates — but it is not circular reasoning, because the paper does not claim to have derived or predicted those estimates from the responses; it only measures reactions to them. The only self-citation (Huang et al., 2024, on robot-error effects on teaching dynamics) is background related work and is not load-bearing for the empirical claims. Potential confounds such as task-specific stimulus properties are correctness/validity risks, not circularity. Score 1 reflects the minor non-load-bearing self-citation and the estimate-grounding limitation without treating either as a circular step.

Axiom & Free-Parameter Ledger

0 free parameters · 5 axioms · 0 invented entities

No free parameters in the mathematical sense; the inputs are real TSR values and qualitative coding decisions. The four information types are experimental conditions, not fitted quantities. No new physical or algorithmic entities are postulated.

axioms (5)
  • domain assumption TSR is the primary metric that RFM evaluations report and that users should interpret as success probability.
    The study is premised on TSR being the central reported metric; motivated in §1 and §2.
  • domain assumption The four information types (ETSR, EFC, RT-TSR, RT-FC) are the relevant information dimensions for users.
    Authors select these from common RFM evaluation practice in §3; other dimensions (speed, robustness) are only captured in open-ended responses, not in the main experimental manipulation.
  • domain assumption Likert responses can be analyzed with parametric RM-ANOVA.
    §4.2: 'Despite likert-item data being non-parametric, we used an RM-ANOVA specifically to account for potential interaction effects.' This is a contested assumption in HRI statistics.
  • ad hoc to paper Presenting true TSR/failure outcomes as robot estimates measures how users treat estimates.
    §4.1: 'The data used for ETSR and EFC was real data, but was framed as estimates to participants.' This equates estimates with ground truth, which is load-bearing for the estimate-recommendation claims.
  • domain assumption Qualitative coding by three lab members captures task similarity as perceived by users.
    §4.1: similarity was judged by 'robot skills' by lab members, not by participants; users later reported that similarity mattered, but the study does not validate the coding against user perceptions.

pith-pipeline@v1.3.0-alltime-deepseek · 17046 in / 10322 out tokens · 102420 ms · 2026-08-03T04:48:21.008848+00:00 · methodology

0 comments
read the original abstract

Robot Foundation Models (RFMs) represent a promising approach to developing general-purpose home robots. Given the broad capabilities of RFMs, users will inevitably ask an RFM-based robot to perform tasks that the RFM was not trained or evaluated on. In these cases, it is crucial that users understand the risks associated with attempting novel tasks due to the relatively high cost of failure. Furthermore, an informed user who understands an RFM's capabilities will know what situations and tasks the robot can handle. In this paper, we study how non-roboticists interpret performance information from RFM evaluations. These evaluations typically report task success rate (TSR) as the primary performance metric. While TSR is intuitive to experts, it is necessary to validate whether novices also use this information as intended. Toward this end, we conducted a study in which users saw real evaluation data, including TSR, failure case descriptions, and videos from multiple published RFM research projects. The results highlight that non-experts not only use TSR in a manner consistent with expert expectations but also highly value other information types, such as failure cases that are not often reported in RFM evaluations. Furthermore, we find that users want access to both real data from previous evaluations of the RFM and estimates from the robot about how well it will do on a novel task.

Figures

Figures reproduced from arXiv: 2602.03920 by Bingyu Wu, Elaine Short, Isaac Sheidlower, James Staley, Jindan Huang, Qicong Chen, Reuben Aronson.

Figure 1
Figure 1. Figure 1: This work investigates how people interpret commonly [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the study procedure. Users saw a successful or failed trajectory based on a probabilistic sample from the real evaluation [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Responses to the pre-task and post-task Likert questions of information sufficiency under different conditions. p-values and [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The in-person study was conducted in a University building [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Like in the online study, a large majority of users [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Information usage code counts from online study. [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Other information code counts from online study. [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Other information code counts from in-person study. [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Instruction page (1/3) [PITH_FULL_IMAGE:figures/full_fig_p018_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Instruction page (2/3) [PITH_FULL_IMAGE:figures/full_fig_p019_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Instruction page (3/3) [PITH_FULL_IMAGE:figures/full_fig_p020_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Pre-task execution questionnaire [PITH_FULL_IMAGE:figures/full_fig_p021_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Post-task execution questionnaire [PITH_FULL_IMAGE:figures/full_fig_p022_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Post-study questionnaire [PITH_FULL_IMAGE:figures/full_fig_p023_15.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

5 extracted references · 2 canonical work pages

  1. [3]

    doi: 10.1145/3549532. B. Liu, Y . Zhu, C. Gao, Y . Feng, Q. Liu, Y . Zhu, and P. Stone. LIBERO: benchmarking knowledge transfer for lifelong robot learning. In Proceedings of the 37th International Conference on Neural Information Processing Systems , NIPS ’23, pages 44776–44791, Red Hook, NY , USA, May

    work page doi:10.1145/3549532

  2. [5]

    You want the robot to

    doi: 10.1109/ICRA57147.2024.10610720. M. Reuss, O. E. Yagmurlu, F. Wenzel, and R. Lioutikov. Mul- timodal Diffusion Transformer: Learning Versatile Behav- ior from Multimodal Goals. In Proceedings of Robotics: Science and Systems , Delft, Netherlands, July 2024. doi: 10.15607/RSS.2024.XX.121. M. Riveiro and S. Thill. The challenges of providing ex- planat...

    arXiv 2024

  3. [2003]

    ISBN 978-0-8147-0695-4. S. Belkhale and D. Sadigh. MiniVLA: A Better VLA with a Smaller Footprint, 2024. A. Brohan, N. Brown, J. Carbajal, Y . Chebotar, X. Chen, K. Choromanski, T. Ding, D. Driess, A. Dubey, C. Finn, P. Florence, C. Fu, M. G. Arenas, K. Gopalakrishnan, K. Han, K. Hausman, A. Herzog, J. Hsu, B. Ichter, A. Ir- pan, N. Joshi, R. Julian, D. K...

    2024

  4. [2023]

    arXiv:2307.15818 [cs]

    URL http://arxiv.org/abs/2307.15818. arXiv:2307.15818 [cs]. R. Cadene, S. Alibert, A. Soare, Q. Gallouedec, A. Zouitine, and T. Wolf. LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch, 2024. N. Chater and M. Oaksford. The Probabilistic Mind:Prospects for Bayesian cognitive science . Oxford University Press, Mar. 2008. ISBN 978-...

    Pith/arXiv arXiv 2024

  5. [2024]

    Curran Associates Inc. Z. Liu, A. Bahety, and S. Song. REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction. Aug. 2023. N. M. Moorman, N. Gopalan, A. Singh, E. Botti, M. Schrum, C. Yang, L. Seelam, and M. Gombolay. Investigating the Impact of Experience on a User’s Ability to Perform Hier- archical Abstraction. In Proceedings of R...

    work page doi:10.15607/rss.2023.xix.004 2023