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REVIEW 4 major objections 5 minor 39 references

Preserving Sense of Agency: User Preferences for Robot Autonomy and User Control across Household Tasks

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A survey-based study finds that third-party involvement in programming or operating a household robot lowers a user's sense of agency more sharply than the robot acting autonomously, and that letting users program the robot themselves…

desk verdict A useful small study with a genuinely new comparison, but one headline claim is overstated because the authors compare main effects in a model with a significant interaction. read the letter →

arxiv 2506.19202 v1 pith:PHKUXCAB submitted 2025-06-24 cs.RO cs.HC

Claire Yang , Heer Patel , Max Kleiman-Weiner , Maya Cakmak This is my paper
classification cs.ROcs.HC
keywords senseofagencyrobotautonomyend-userprogrammingthird-partyteleoperationhouseholdrobotsuserpreferencestaskriskhuman-robotinteraction
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

Assistive household robots are often designed on the assumption that full autonomy is what users want, but this study argues that users care at least as much about who is behind the robot. Using a vignette survey of four robot types, it finds that third-party involvement in programming or operating the robot lowers perceived sense of agency more sharply than the robot's acting autonomously. The main design claim is that an end-user programmed autonomous robot preserves a user's sense of agency nearly as well as direct control, because the user authors the robot's behavior. Task risk also matters: people want more of their own control in high-risk chores like preparing food for an allergic child. The study offers a quantitative model of agency as a function of autonomy, third-party involvement, and trust.

What carries the argument

The study's load-bearing instrument is a four-level autonomy taxonomy (fully autonomous, end-user programmed, third-party teleoperated, fully user-controlled), crossed with two binary factors: whether the robot acts autonomously and whether a third party is involved. The quantitative work is done by a mixed linear model estimated by restricted maximum likelihood, with participant as a random effect and fixed effects for autonomy, third-party involvement, their interaction, and user trust in the third party. A six-item questionnaire adapted from the Sense of Agency Scale measures the outcome. The mechanism the model exposes is that user authorship over the robot's program substitutes for moment-to-moment control, which is why an autonomous robot the user programmed scores close to a robot the user directly drives.

What would settle it

Run a between-subjects experiment with a physical assistive robot: one group programs the robot themselves, another uses it with a manufacturer-written program, and a third group has it teleoperated by a remote third party, with agency measured both by the six-item questionnaire and by an implicit measure such as intentional binding. If the end-user programmed robot stops scoring near the directly controlled robot in real interaction, the design claim would be weakened.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is a dissociation: loss of agency tracks the locus of authorship more than the locus of execution. In a mixed-effects model fit to 21 participants' ratings, having the robot act autonomously reduces predicted agency by $\beta = -0.865$, while having a third party involved in programming or operation reduces it by $\beta = -1.996$, with a positive interaction term ($\beta = 0.980$). The model predicts an end-user programmed robot yields agency of 2.5 versus 1.5 for a fully autonomous robot programmed by the manufacturer, so users can keep a feeling of control while still letting the robot run on its own. Preferences shift with perceived risk: for low-risk tasks such as watering plants, autonomous options win; for high-risk health-related tasks, users overwhelmingly put the fully user-controlled robot first. Trust in a third-party operator moderates the trade-off, making a teleoperated robot about as acceptable as end-user programming when trust is full.

Load-bearing premise

The load-bearing premise is that people's ratings of imagined robots in written vignettes track how much agency they would actually feel while using or being assisted by a real robot.

Editorial extensions

If this is right

  • Robot designers can preserve a sense of agency without sacrificing autonomy by letting users program the robot themselves, rather than shipping fixed autonomous behavior.
  • In high-risk tasks, such as preparing food for a child with allergies or fetching medication, users should be given direct control; autonomy becomes acceptable mainly in low-risk chores.
  • Third-party teleoperation carries an agency cost larger than the autonomy itself, so deploying remotely operated household robots should be weighed against user-authoring alternatives.
  • Trust in a third-party operator can substantially offset the agency loss of teleoperation, so trust-building measures could change which robot people prefer.

Reading between the lines

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

  • A testable extension of this result is that shared-autonomy and variable-autonomy systems should be judged by whether they preserve the user's authorship over the robot's decisions, not only by task performance or safety.
  • The model suggests that any form of robot assistance that inserts an invisible third party between the user and the robot's behavior, such as cloud-based decision policies or AI training on others' data, may reduce agency even when the robot does not look autonomous.
  • The vignette limitation could be probed by comparing these ratings with actual hands-on experience; if real exposure shifts ratings, the design principle might hold most strongly for first-time or imagined use.
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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 / 5 minor

Summary. This paper investigates how robot autonomy and third-party involvement affect users' sense of agency and how preferences change across household tasks with varying risk. Participants (N=21) rated their sense of agency for four robot configurations—fully autonomous, end-user programmed, third-party teleoperated, and fully user-controlled—and ranked these configurations across eight vignette scenarios. A linear mixed model with participant random effects found significant negative effects of robot autonomy (β=-0.865) and third-party involvement (β=-1.996), a significant interaction (β=0.980), and a positive effect of user trust in a third party (β=1.250). The authors conclude that third-party involvement reduces sense of agency more than robot autonomy, that end-user programming can preserve agency while retaining robot autonomy, and that users prefer more control in high-risk tasks. Study materials, anonymized responses, and codings are shared on OSF.

Significance. If the main results hold, the paper makes a useful contribution to HRI design by decoupling robot autonomy from the question of who is in control, and by pointing to end-user programming as a compromise that preserves agency without giving up autonomous operation. The study also connects perceived task risk and trust in third parties to autonomy preferences, and it uses a validated sense-of-agency questionnaire. The open data and codings are a clear strength: the analysis is checkable and the model produces concrete, falsifiable estimates for future work. However, the headline comparative claim is not statistically established as reported, and the small, homogeneous convenience sample plus the vignette methodology limit the strength of the design principles. The limitations are acknowledged in Section VI, but the abstract and results currently present the findings in more general terms than the evidence supports.

major comments (4)
  1. [Section IV-A, Table II] The claim that third-party involvement decreases sense of agency "more so than" the robot acting autonomously is not supported by the model as reported. Because the interaction β=0.980 is significant, the simple effect of third-party involvement when the robot acts autonomously is -1.996 + 0.980 = -1.016, while the simple effect of autonomy when no third party is involved is -0.865. These are the coefficients most relevant to the paper's end-user-programming design principle, and the difference between them is -0.151, which is not reported and is unlikely to be statistically significant. The large -1.996 coefficient is the simple effect of third-party involvement only in the non-autonomous condition, not a general effect across all conditions. Please report all four simple effects with confidence intervals and a formal test of the difference that the "more so" claim requires, and revise the conclusion accordingly.
  2. [Section IV-A, Table II] The predicted ratings in Section IV-A (2.5 for the end-user programmed robot, 2.6 for the third-party teleoperated robot when the user fully trusts the third party) can only be reproduced if the "User Trusts Third Party" covariate is applied exclusively to third-party-involved conditions. As reported in Table II, however, the term appears to be a main effect, which would also add 1.250 to the end-user programmed condition for a trusting user, yielding a predicted value of 3.75. Please clarify exactly how the trust variable was coded (per participant, per robot type, or per open-ended mention), and present predicted values obtained directly from the fitted model with the coding rule stated.
  3. [Section III-C] The "User Trusts Third Party" predictor is coded from the same open-ended responses in which participants explained their sense-of-agency ratings, and it is then included as a covariate in the model for those ratings. This creates a risk of circularity and overfitting, because the theme is inferred from the outcome explanation and then used to explain the outcome. Please describe the exact coding rule and the inter-rater reliability for this specific code, and report whether the autonomy and third-party coefficients and their significance are robust when the trust term is removed from the model.
  4. [Section III-B] The two tasks omitted from the analysis are never identified, and the criteria for "participant confusion and differing implicit assumptions" are not operationalized. Because the risk-preference analysis in Section IV-C is based on the remaining six tasks, the reader cannot determine whether the exclusion was post hoc in a way that could bias the preference results. Please identify the excluded tasks and state the rule used to decide that a task was confused or assumption-laden.
minor comments (5)
  1. [Section IV-A] The word "predicted" is used for ratings that are in-sample fitted values from the mixed model, not out-of-sample predictions; please use "model-estimated" or "fitted values" and add confidence intervals for the reported point estimates.
  2. [Section II-A] The phrase "human agency as a casual relationship" appears to be a typo; it should be "causal relationship."
  3. [Section III-C, Table II] The six Likert items are averaged into a single 1–5 score and analyzed with a linear mixed model; please clarify whether the scale was treated as continuous and provide confidence intervals for the Table II coefficients.
  4. [Section III-B, Figure 3] Please clarify whether the low/medium/high risk grouping of tasks was defined a priori or created after inspecting the data, since the grouping appears to be introduced after the scatter plot is shown.
  5. [Section IV-C] The statement that "over 90% of participants who emphasized that these tasks were low risk" ranked the autonomous robots first uses a subset of participants; please report the denominator for this percentage and for the other subset-based percentages in the section.

Circularity Check

1 steps flagged · score 4.0 of 10

In-sample fitted values are labeled as 'predictions,' but the central comparative claims are direct empirical estimates, so the paper is only partially circular.

  1. fitted input called prediction [Section IV-A, paragraph 3 (under 'Factors Influencing Robot User’s Sense of Agency')]
    "Notably, third-party involvement in the programming or control of the robot most significantly reduced the user’s sense of agency, more so than whether the robot acted autonomously. This finding suggests an important design principle: users’ sense of agency can be preserved even when interacting with autonomous robots as long as users themselves program the robot. Our model predicts that an end-user programmed robot would yield a sense of agency rating of 2.5, which is significantly higher than a fully autonomous robot programmed by a third party, with a predicted rating of 1.5."

    The 'predicted' ratings are not out-of-sample forecasts; they are the fitted cell means of the same mixed model used to estimate the coefficients. From Table II: end-user programmed = Intercept (3.365) + Robot Acts Autonomously (-0.865) = 2.500; fully autonomous = 3.365 - 0.865 - 1.996 + 0.980 = 1.484 ≈ 1.5. The trust-adjusted equivalence (2.6 vs 2.5) is likewise the same fitted coefficients: 3.365 - 1.996 + 1.250 = 2.619 for third-party teleoperated. Thus the design-principle claim is supported by re-stating the model's in-sample fitted values as 'predictions' rather than by an independent validation. This is partial circularity; the coefficients themselves remain direct empirical estimates from the survey data.

full rationale

No load-bearing self-citation, imported uniqueness theorem, or ansatz-smuggling is present; the related-work citations to the authors' prior papers are contextual and non-essential. The central comparison (third-party involvement beta = -1.996 vs autonomy beta = -0.865) is a direct coefficient estimate from the participants' ratings, not a reduction to the paper's inputs. The circular element is confined to Section IV-A, where fitted cell means computed from Table II's coefficients are presented as 'predictions' (2.5 vs 1.5, and 2.6 vs 2.5). These values are by construction linear combinations of the same fitted parameters and therefore add no independent support to the design principle. The trust covariate is also coded from the same participants' open-ended responses, which limits its evidentiary independence, though it is not definitionally the same as the outcome. The significant interaction between autonomy and third-party involvement raises a statistical-interpretation concern about comparing main effects, but that is a correctness issue rather than circularity. Overall, the empirical finding has independent content, so the circularity score is modest rather than high.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The model uses five fitted coefficients estimated from participant ratings. The study relies on several domain assumptions about the validity of vignette-based agency measurement, the interval nature of Likert scales, and the coding of qualitative responses. No new entities are invented.

free parameters (5)
  • Intercept (baseline sense of agency) = 3.365
    Fitted to participant ratings for the fully user-controlled robot in the mixed linear model, used to derive all predicted agency ratings.
  • Robot Acts Autonomously coefficient = -0.865
    Fitted fixed effect; quantifies the decrease in sense of agency when the robot acts autonomously.
  • Third Party Is Involved coefficient = -1.996
    Fitted fixed effect; quantifies the larger decrease in sense of agency when a third party programs or operates the robot.
  • User Trusts Third Party coefficient = 1.250
    Fitted coefficient for a covariate coded from participants' open-ended responses; increases predicted agency for third-party conditions.
  • Autonomy x Third Party interaction coefficient = 0.980
    Fitted interaction term; partially offsets the combined negative effects for fully autonomous robots involving a third party.
assumptions (5)
  • domain assumption Participants' self-reported six-item Likert ratings measure sense of agency as defined across the four hypothetical robot interactions.
    The study adapts Tapal et al.'s Sense of Agency Scale to hypothetical vignettes (Section III-A); no validation that these ratings predict real-world agency.
  • domain assumption The binary factors 'robot acts autonomously' and 'third party is involved' fully characterize the differences among the four robot types with respect to agency.
    The model in Section IV-A treats only these factors plus trust; other differences (e.g., learning from demonstration, remote control) are collapsed into these binaries.
  • domain assumption Averaged Likert scores across six items behave as interval data suitable for linear mixed modeling.
    Section III-C averages ratings and fits a REML model; treating ordinal Likert responses as interval is a common but contested assumption.
  • domain assumption Task risk ratings and desired involvement ratings reflect real perceived risk in household tasks.
    Section III-B obtains self-reported risk and involvement per vignette; no behavioral validation.
  • domain assumption Open-ended response coding into themes such as trust in third party is reliable.
    Inter-rater reliability 0.99 reported, but the trust covariate is measured from the same responses used for agency ratings.

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Cite this review

Pith. "Pith review of Preserving Sense of Agency: User Preferences for Robot Autonomy and User Control across Household Tasks." pith.science (2026). https://pith.science/paper/PHKUXCAB

@misc{pith2026250619202,
  author       = {Pith},
  title        = {Pith review of: Preserving Sense of Agency: User Preferences for Robot Autonomy and User Control across Household Tasks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PHKUXCAB}},
  note         = {Machine review of arXiv:2506.19202}
}
read the original abstract

Roboticists often design with the assumption that assistive robots should be fully autonomous. However, it remains unclear whether users prefer highly autonomous robots, as prior work in assistive robotics suggests otherwise. High robot autonomy can reduce the user's sense of agency, which represents feeling in control of one's environment. How much control do users, in fact, want over the actions of robots used for in-home assistance? We investigate how robot autonomy levels affect users' sense of agency and the autonomy level they prefer in contexts with varying risks. Our study asked participants to rate their sense of agency as robot users across four distinct autonomy levels and ranked their robot preferences with respect to various household tasks. Our findings revealed that participants' sense of agency was primarily influenced by two factors: (1) whether the robot acts autonomously, and (2) whether a third party is involved in the robot's programming or operation. Notably, an end-user programmed robot highly preserved users' sense of agency, even though it acts autonomously. However, in high-risk settings, e.g., preparing a snack for a child with allergies, they preferred robots that prioritized their control significantly more. Additional contextual factors, such as trust in a third party operator, also shaped their preferences.

Figures

Figures reproduced from arXiv: 2506.19202 by the authors.

Figure 1
Figure 1. Robot types presented in the survey. (A) Fully autonomous, (B) end [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Average Perceived Sense of Agency by Robot Type. Participants [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Participants’ Average Ratings of Risk vs. Desired Involvement for [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Histogram of ranked robot types for the medium risk tasks, where [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

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Pith tools

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