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REVIEW 3 major objections 5 minor 56 references

What Questions Should Robots Be Able to Answer? A Dataset of User Questions for Explainable Robotics

T0 review · 3 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read A new dataset of 1,893 user questions maps what people want to ask household robots.

desk verdict Valuable dataset with a solid design, but the reported numbers don't add up—fix the counts before it's treated as canonical. read the letter →

arxiv 2510.16435 v2 pith:LQSEZ52C submitted 2025-10-18 cs.RO cs.CLcs.HC

Lennart Wachowiak , Andrew Coles , Gerard Canal , Oya Celiktutan This is my paper
classification cs.ROcs.CLcs.HC
keywords userquestionsexplainableroboticshuman-robotinteractionquestion-answeringquestiontaxonomyhouseholdrobotsAIdataset
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

This paper introduces a dataset of 1,893 natural-language questions that people would ask a household robot, collected from 100 participants who watched videos or read text summaries of robots doing everyday chores. The questions are organized into 12 categories and 70 subcategories, revealing that most questions concern execution details, robot capabilities, and performance assessment. When users rate importance, questions about how the robot would handle potential issues and ensure correct behavior rank highest, while the why-questions that dominate explainable-robotics research rank near the bottom. The dataset is offered as an empirical foundation for deciding what a robot should log, what question-answering systems should be built, and how explanations should be matched to user expectations.

What carries the argument

The carry load of the argument is the dataset itself together with its two-level coding scheme: 1,893 user questions hierarchically organized into 12 main categories and 70 subcategories, each defined with examples. The collection protocol pairs video and text stimuli of robot household tasks with a structured prompt that asks participants to write questions the robot should be able to answer and to rate each question's importance on a 5-point scale. Statistical inference on importance rankings uses a linear mixed-effects model with participant and stimulus as random intercepts, allowing within-participant and within-stimulus correlation to be accounted for.

What would settle it

Run a longitudinal field study where participants live with an actual household robot for several weeks and their spontaneous questions are logged as they ask them, then compare the distribution and importance ratings to the 12-category taxonomy; if the category frequencies or the importance ordering diverge substantially for the same tasks, the dataset's transferability breaks down.

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Extended reading notes

Core claim

The paper's central claim is that the space of questions users want household robots to answer is much broader than the why-questions typically studied in explainable AI, and that this space can be systematically mapped. By eliciting questions from 100 participants across 15 video and 7 text stimuli of real robot task executions, the authors construct a hierarchical taxonomy with 12 main categories and 70 subcategories. The most frequently asked categories are execution-details (21.4%), what-abilities (12.6%), and self/task-assessment (10.7%). However, importance ratings from the same participants rank potential-issues questions (hypothetical difficulties and correctness assurance) as most i

Load-bearing premise

The questions people write while watching short video clips or reading text summaries are assumed to be essentially the same questions they would actually ask a real household robot during repeated, everyday interaction.

Editorial extensions

If this is right

  • Robot developers can use the taxonomy to decide which data a robot must log during task execution, since answering questions about environment state, execution details, or capabilities each requires different recorded information.
  • Question-answering and explanation modules should prioritize questions about potential issues and correctness assurance over the why-questions that have dominated XAI research, based on the importance rankings.
  • The dataset can serve as a benchmark for evaluating robot question-answering systems, offering a ground-truth set of user-generated questions with importance weights.
  • The difference in question types between novices and experienced users implies that adaptive question-answering systems may need to tailor their scope and defaults to user expertise.
  • The divergence between question frequency and importance within categories suggests that rare but highly important questions may deserve proactive explanation features rather than being overlooked.

Reading between the lines

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

  • The importance ranking could be converted into a weighted evaluation metric for generative question-answering systems, penalizing failure on high-importance categories more heavily.
  • The taxonomy may transfer beyond household robots to other service or collaborative robots, but validating that transfer would require similar elicitation in those domains.
  • The smaller gap between video and text elicitation results hints that users ask different questions about a robot's past activity versus live action; this could inform whether a robot should give periodic summaries or only respond on request.
  • A natural next step would be to collect paired user answers alongside questions to disambiguate intent, which the authors themselves note as a limitation and future direction.
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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

3 major / 5 minor

Summary. The paper introduces a dataset of 1,893 user questions for household robots, elicited from 100 Prolific participants in response to 15 video and 7 text stimuli depicting everyday robot tasks. The questions were coded inductively into a two-level hierarchy of 12 categories and 70 subcategories. The authors report importance ratings for each question, use a linear mixed-effects model to compare importance across categories, and examine how question types and importance scores relate to participants' robot experience and attitudes. They claim that potential-issue questions receive the highest importance ratings, that why-questions rank relatively low, and that robotics novices ask different question types than more experienced users.

Significance. If the dataset is internally consistent and the public artifacts match the manuscript, this would be a valuable empirical resource for XAI and HRI: it provides a structured, publicly available corpus with a codebook, inter-annotator agreement (Cohen's kappa = .77 on a 100-question subset), and a reproducible analysis pipeline. The finding that users prioritize hypothetical-scenario and correctness-assurance questions over the why-questions dominant in much XAI literature is practically useful for prioritizing logging and explanation functionality. However, the current manuscript contains multiple internal numeric inconsistencies in the central dataset-size claim and related descriptive statistics; these must be resolved before the dataset can be treated as a canonical resource.

major comments (3)
  1. [§1, §3.3, §4, Figure 2, Figure 3] The central claim that the dataset contains 1,893 questions is not supported by the manuscript's own numbers. Section 4 reports 2,052 collected and 143 excluded, which gives 1,909, not 1,893. The per-category sample sizes in Figure 3 sum to 1,948 (120+190+126+245+83+157+52+417+106+209+125+118), and these counts match the subcategory counts in Figure 2. If each question belongs to exactly one category, the categorized dataset should contain 1,948 rows. The abstract, Section 3.3, and Section 4 thus give three different totals. Please reconcile these numbers and verify against the released GitHub/HuggingFace dataset, since the reuse value of the dataset depends on knowing exactly which questions are included.
  2. [Abstract vs. §1; §3.2] The top-category percentages are inconsistent between the abstract and the introduction: the abstract reports execution-details 21.4%, capabilities 12.6%, performance 10.7%, while Section 1 reports 22.5%, 12.7%, and 11.3%. These do not correspond to the same denominator (e.g., the Figure 2/3 counts give 417/1948 = 21.4%, but 417/1893 = 22.0%). Additionally, the abstract says 7 text stimuli while Section 3.2 says 'we wrote six text reports'; Section 3.1 implies a pool of 22 stimuli (15 videos + 7 texts). Please correct these inconsistencies, as they affect both the summary statistics and the reproducibility of the stimulus set.
  3. [§4.3, Figure 5] The contribution statement claims that users with different robot experience 'ask different questions,' but this is supported only by descriptive percentage differences in Figure 5 with no inferential test. The text reports, for example, that low-experience participants asked execution-details questions 23.3% of the time versus 15.7% for high-experience participants, without a significance test or effect-size estimate. If this is a central finding of RQ3, please add an appropriate statistical analysis (e.g., a mixed-effects model or permutation test on category proportions) or explicitly label the observation as descriptive and temper the contribution claim.
minor comments (5)
  1. [§3.4, §4.3] The robot-experience variable is described in Section 4.3 as a 1–5 scale, but Figure 1 and Figure 5 describe it as a 1–7 Likert scale. Please make the scale consistent everywhere, including in the regression interpretation.
  2. [§3.4] The reliability subsection says the second annotator re-annotated '100 sentences ... balanced across categories, i.e., contained 8 samples per category.' With 12 categories, 8 samples per category gives 96, not 100. Please clarify how the 100-question sample was constructed and whether the balance statement is exact.
  3. [Figure 3] The x-axis label reads '0 (not at all important), 5 (extremely important),' but the text and Methods say the scale is 1 (Not at all important) to 5 (extremely important). Please correct the axis label.
  4. [§4.2] The list of pairwise contrasts reports corrected p-values only. For transparency, please also report the raw p-values or state explicitly that the reported values are Holm–Bonferroni-adjusted.
  5. [§5.1] The comparison of text- vs. video-elicited questions (36% vs. 16% execution-details) is informative, but it would be helpful to report whether this difference is statistically tested or is purely descriptive.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation; the dataset and importance rankings are empirically self-contained.

full rationale

This is a dataset-collection and descriptive-statistics paper; there is no formal derivation chain whose conclusions could be equivalent to its own inputs. The taxonomy was explicitly coded inductively from the newly collected questions: 'the questions were coded by the first author of this paper in an inductive, bottom-up manner. No pre-defined (top-down) codes were used.' The importance ranking comes from a linear mixed-effects model fit to the participants' own importance ratings, so it is a summary of those ratings rather than a prediction from fitted parameters. Earlier self-citations ([51], [53]) are used only to situate the study and explicitly do not constrain the coding: 'While these taxonomies provide us with insights into the HRI contexts in which questions are asked, in this work, we present a dataset of concrete natural language questions...' Therefore no step reduces by construction or by self-citation to the paper's inputs. Note: the internal count inconsistencies (2,052−143=1,909 vs. the stated 1,893, and Figure 3 category n's summing to 1,948) are a correctness/reproducibility concern, not circularity.

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

The central claims rest on domain assumptions about ecological validity, sample representativeness, annotation quality, and self-reported importance. These are not disguised or hidden; the authors explicitly acknowledge several of them in Section 5.4. No hand-fitted free parameters or newly invented entities are introduced.

assumptions (5)
  • domain assumption Questions elicited by prompted video/text stimuli approximate the questions users would ask a real household robot in natural interaction.
    Load-bearing for the dataset's claimed usefulness. Participants were prompted to provide questions after watching short clips or text summaries (Sections 3.1-3.2). The authors themselves list ecological validity as a limitation (Section 5.4).
  • domain assumption Prolific participants who are fluent in English are representative enough of household-robot users for the reported importance rankings and group differences.
    The sample is 100 Prolific participants, mostly Western/Anglophone and English-fluent (Section 3.3). The authors acknowledge participant-diversity limits in Section 5.4.
  • domain assumption The inductive coding by the first author, with a second annotator checking 100 questions, produces a valid and reproducible taxonomy.
    Coding was done bottom-up by one main annotator; inter-annotator agreement is substantial (kappa = .77) but only on a 100-question subset (Section 3.4).
  • domain assumption Self-reported 1-5 importance ratings measure how important it is for a robot to be able to answer a question.
    The importance scores are Likert self-reports collected immediately after each question (Section 3.1) and are treated as the outcome in the mixed-effects model.
  • standard math The linear mixed-effects model with random intercepts for participant and stimulus yields valid standard errors for category comparisons.
    The model formula and REML fitting are stated (Section 3.4). Distributional assumptions are not checked, but this is a standard statistical framework for repeated-measures data.

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Cite this review

Pith. "Pith review of What Questions Should Robots Be Able to Answer? A Dataset of User Questions for Explainable Robotics." pith.science (2026). https://pith.science/paper/LQSEZ52C

@misc{pith2026251016435,
  author       = {Pith},
  title        = {Pith review of: What Questions Should Robots Be Able to Answer? A Dataset of User Questions for Explainable Robotics},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LQSEZ52C}},
  note         = {Machine review of arXiv:2510.16435}
}
read the original abstract

With the growing use of large language models and conversational interfaces in human-robot interaction, robots' ability to answer user questions is more important than ever. We therefore introduce a dataset of 1,893 user questions for household robots, collected from 100 participants and organized into 12 categories and 70 subcategories. Most work in explainable robotics focuses on why-questions. In contrast, our dataset provides a wide variety of questions, from questions about simple execution details to questions about how the robot would act in hypothetical scenarios -- thus giving roboticists valuable insights into what questions their robot needs to be able to answer. To collect the dataset, we created 15 video stimuli and 7 text stimuli, depicting robots performing varied household tasks. We then asked participants on Prolific what questions they would want to ask the robot in each portrayed situation. In the final dataset, the most frequent categories are questions about task execution details (21.4%), the robot's capabilities (12.6%), and performance assessments (10.7%). Although questions about how robots would handle potentially difficult scenarios and ensure correct behavior are less frequent, users rank them as the most important for robots to be able to answer. Moreover, we find that users who identify as novices in robotics ask different questions than more experienced users. Novices are more likely to inquire about simple facts, such as what the robot did or the current state of the environment. As robots enter environments shared with humans and language becomes central to giving instructions and interaction, this dataset provides a valuable foundation for (i) identifying the information robots need to log and expose to conversational interfaces, (ii) benchmarking question-answering modules, and (iii) designing explanation strategies that align with user expectations.

Figures

Figures reproduced from arXiv: 2510.16435 by the authors.

Figure 1
Figure 1. Participant Statistics: their experience with robots (Likert scale 1–7), personal/societal-level attitudes towards robots (measured [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Hierarchical categorization of user questions for the robot (see Tables [PITH_FULL_IMAGE:figures/full_fig_p014_2.png] view at source ↗
Figure 3
Figure 3. Estimated marginal means (95% CIs) of the importance scores per question category — obtained via a linear mixed-effects [PITH_FULL_IMAGE:figures/full_fig_p015_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Users’ rating of how important they think it is that a robot can answer their proposed questions is related to their general [PITH_FULL_IMAGE:figures/full_fig_p016_4.png]
Figure 5
Figure 5. Figure 5: Difference in question type distribution based on robot experience. Robot experience is based on self-report on a Likert scale [PITH_FULL_IMAGE:figures/full_fig_p016_5.png]

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Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.