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hri-experiments
GitHub指导HRI论文中人类受试者研究的设计与审计，涵盖因果/感知等声明匹配、组间/组内设计选择、Wizard-of-Oz伦理规范及统计报告要求。
触发场景
设计HRI人机交互实验
审计人类受试者研究方案
选择实验设计类型
处理Wizard-of-Oz方法
安装
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill hri-experiments -g -y
SKILL.md
Frontmatter
{
"name": "hri-experiments",
"description": "Use when designing or auditing the human-subjects study at the heart of an ACM\/IEEE HRI full paper — choosing between\/within-subjects designs, running Wizard-of-Oz honestly, powering the sample, reporting statistics with effect sizes and qualitative rigor, selecting validated scales, adding manipulation checks, pre-registering, and meeting HRI's human-participants ethics obligations."
}
HRI Experiments
The human-subjects study is HRI's currency. A brilliant robot behavior with a broken study fails;
a modest behavior with an airtight study can win a Best Paper. HRI reviewers — drawn from
psychology and HCI as well as robotics — hold study design to a standard closer to experimental
psychology than to a robotics benchmark. This skill covers the design decisions that decide the
paper, and HRI's specific ethics obligations. It pairs with the shared reporting kit in
../../resources/code/README.md.
Match the evidence to the claim
Decide what kind of claim you are making, then design to it:
| Claim shape | Evidence HRI expects |
|---|---|
| "Robot behavior X changes outcome Y" (causal) | A controlled experiment manipulating X, measuring Y, with a manipulation check and effect sizes |
| "People experience the robot as Z" (perception) | A validated instrument for Z, adequate power, and honest CIs |
| "This interaction/design works better" | A comparison with a fair baseline and a behavioral or task outcome, ideally shown on video |
| "Here is how people make sense of robots" (qualitative) | A rigorous qualitative method (thematic/grounded), reflexivity, transferable insight — not counts |
| "This method/measure is valid" (methods) | Psychometric or methodological evidence, not a single-use demo |
A mismatch — e.g., claiming behavior change but measuring only a liking scale — is the most common reason an HRI study is judged inadequate.
Choose the design deliberately
- Between-subjects — each participant sees one condition. Cleaner (no carryover, no demand from seeing the manipulation) but needs more participants for the same power. Default when exposure to the robot changes people.
- Within-subjects — each participant sees all conditions. More powerful per participant, but risks order effects (counterbalance) and demand characteristics (participants guess the hypothesis).
- Mixed — a between factor (e.g., robot type) crossed with a within factor (e.g., task phase).
- Justify the choice in the paper; reviewers will ask why. State counterbalancing and how you controlled order/demand for within-subjects designs.
Wizard-of-Oz, done honestly
Wizard-of-Oz (WoZ) — a human covertly controlling some robot behavior — is a legitimate and common HRI method, but it is a credibility minefield if mishandled:
- Disclose it. State clearly what was autonomous and what the wizard controlled. Presenting teleoperated behavior as autonomous is a serious integrity problem reviewers actively probe.
- Constrain and log the wizard. Define the wizard's action space, script or protocol, and report wizard error rates and training; an unconstrained wizard makes the stimulus irreproducible and the condition ill-defined.
- Watch consistency. Wizard behavior must be consistent across participants and conditions, or the "manipulation" is really wizard variance.
- Cite and follow the community's WoZ reporting guidance (a well-known systematic review lives in the JHRI journal — see the exemplars-library guard; it is a journal work, cite it as such).
Power, sample size, and analysis
- Power the study. Estimate sample size from an expected effect size (from pilots or prior HRI work) before collecting data; an underpowered null is uninformative. Report the basis for N.
- Report effect sizes and confidence intervals, always — Cohen's d, η², odds ratios, or the appropriate measure — not just p-values. HRI reviewers increasingly treat "significant, no effect size" as incomplete.
- Correct for multiple comparisons when you run many tests; a scale battery with no correction invites the "garden of forking paths" critique.
- Pre-register the primary hypothesis and analysis where you can, and clearly separate confirmatory from exploratory results. Label post-hoc findings as post-hoc.
- Check assumptions and use methods appropriate to your data (ordinal Likert data, repeated measures, nested/mixed models for multi-trial designs).
Validated scales and constructs
Prefer established, validated instruments over homemade items so results are comparable and the construct is defensible:
- Robot-perception constructs commonly use published scales for anthropomorphism/animacy/likeability, warmth and competence, trust, negative attitudes toward robots, and similar. Cite the scale to its source and report reliability (e.g., Cronbach's α) for your sample.
- If you must build a measure, justify it, pilot it, and report its properties — reviewers distrust an ad hoc single item for a rich construct.
- Include a manipulation check so you can show the intended difference was actually perceived.
Qualitative and mixed methods
HRI values qualitative rigor, not just experiments:
- Name the method (thematic analysis, grounded theory, interaction analysis) and follow it; report coding process, and inter-rater reliability where the method calls for it (not all do).
- Practice reflexivity — who coded, their stance, how themes were derived — instead of implying a false objectivity or a fake "n."
- In mixed-methods work, say how the strands integrate (triangulation, explanation, expansion), not just that both exist.
Ethics is not optional at HRI
Submitting binds you to ACM's Policy on Research Involving Human Participants and Subjects:
- Obtain and state IRB/ethics approval (or a documented exemption rationale); report informed consent.
- If the study uses deception (common with WoZ or staged robot failures), justify it and describe debriefing.
- Protect participants and data: de-identify, secure recordings, and mind vulnerable populations (children, older adults, clinical) with extra care.
- Anonymize ethics details for review (institution names can leak identity — see
hri-submission), but do not omit that approval exists.
Anti-patterns HRI reviewers flag
- Undisclosed or unconstrained Wizard-of-Oz.
- Underpowered study with a null spun as evidence of no effect.
- Scale battery + no correction + no pre-registration (fishing).
- Liking/rating as a stand-in for the behavioral or theory-linked outcome the claim needs.
- Missing manipulation check — no evidence the manipulation worked.
- No effect sizes; p < .05 treated as the whole result.
- Qualitative work reported as frequencies with no method named.
Output format
[Claim ↔ evidence] claim shape and whether the design measures it
[Design] between/within/mixed · justified · counterbalanced?
[WoZ] used? autonomy vs wizard disclosed · wizard constrained + error reported?
[Power/stats] N basis · effect sizes + CIs · multiple-comparison correction · pre-registered?
[Measures] validated scales cited + reliability · manipulation check present?
[Qualitative] method named · reflexivity · integration (if mixed)?
[Ethics] IRB/consent stated · deception justified + debriefed?
[Fix queue] <ordered, design fixes before data collection where possible>
版本历史
- 9f86f09 当前 2026-07-19 15:50
