Skills Data Science HRI Artifact Packaging and Reproducibility Guidelines

HRI Artifact Packaging and Reproducibility Guidelines

v20260724
hri-artifact-evaluation
This comprehensive guide outlines the process for packaging artifacts from Human-Robot Interaction (HRI) research. Recognizing that HRI artifacts are complex, involving human subjects, robot behaviors, and extensive study materials, this skill provides guidelines for honest, ethical, and reproducible dissemination. It covers structuring de-identified data, analysis code, and behavioral specifications while adhering to participant consent and academic archiving standards.
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Overview

HRI Artifact Evaluation

HRI's artifact and reproducibility culture is less formalized than software-engineering venues': there is not a long-standing, uniformly enforced ACM artifact-badge track at every HRI edition, and whether a given year runs a formal reproducibility/artifact evaluation is 待核实 per cycle. So treat artifact work at HRI as a credibility and openness move first, and follow a formal badge process only if this edition offers one. Either way, the artifact of an HRI paper is unusual: it is the study, not a runnable system that reproduces a number. This skill packages it honestly. It builds on hri-reproducibility and the shared kit in ../../resources/code/README.md.

First: does this edition run a formal evaluation?

  • Check the current call for a reproducibility/artifact track, its badges, and its own deadline (待核实). If it exists, follow its instructions exactly.
  • If ACM badges are offered, they follow the ACM scheme (Artifacts Available / Evaluated - Functional / Reusable / Results Reproduced). Map your package to whichever the edition supports.
  • If no formal track runs, still release an open, well-documented artifact — reviewers and readers reward it, and it is the community norm even without a badge.

What an HRI artifact actually contains

Because the core evidence is a human study of an embodied robot, the artifact is rarely a one-command reproduction. Assemble:

  • Study materials — questionnaires (items + scoring), interview guides, task scripts, consent-form summary, and coding schemes.
  • Robot-behavior specification — the conditions/behaviors the robot exhibited: for autonomous behavior, the software version and parameters; for Wizard-of-Oz, the wizard's action space, protocol, and error rates (see hri-experiments).
  • De-identified data — participant-level data with identifiers removed, plus a codebook.
  • Analysis code — scripts that regenerate every reported statistic, figure, and table from the shared data, with pinned dependencies and fixed seeds.
  • Pre-registration pointer and any deviation notes.

Make it reusable, within honest limits

  • Reproducible = re-analyzable + re-runnable-as-a-method. An evaluator should be able to regenerate your results from the shared data and to understand the procedure well enough to run the study again — not to recreate your exact human responses. State this scope plainly.
  • Document the environment. For analysis code, a README with dependency versions and a single entry point; a container helps if the toolchain is heavy.
  • License it (an OSI-approved code license; a documented data-use/consent basis for the data).
  • Archive with a DOI (OSF, Zenodo, figshare, or Software Heritage) rather than a personal homepage, so the link is permanent and citable.

Human-subjects constraints on the artifact

An HRI artifact contains people, which limits what can be shared:

  • Share only data/media the participants consented to share for that use; if consent for public data or video was not obtained, share what you can and explain the gap honestly.
  • De-identify thoroughly — free-text responses and video are the usual re-identification risks.
  • Vulnerable-population studies (children, older adults, clinical) may permit little sharing; say so rather than force an unethical release.
  • A partial, honest artifact with a clear rationale beats an over-shared one that breaches consent.

Camera-ready badge mechanics (if applicable)

  • Badges, when a paper earns them, are displayed on the published version — coordinate with hri-camera-ready so any badge and the archive DOI appear correctly.
  • Keep the anonymized review-time archive and the de-anonymized public archive separate; the public one carries author names, the review one must not.

Anti-patterns

  • Overclaiming a "reproducible" human study as if it re-runs like a benchmark.
  • "Data available on request" — treated as unavailable.
  • Unlogged Wizard-of-Oz — the stimulus cannot be reproduced.
  • Identifiable data or video released without consent for that use.
  • Chasing a badge that does not exist this cycle instead of simply releasing a good artifact.

Output format

[Formal track?] does this edition run artifact/reproducibility evaluation + badges? (verify)
[Artifact contents] materials · robot-behavior spec (incl. WoZ) · de-identified data + codebook · analysis code
[Reproducibility scope] re-analysis + method replication; human responses framed as not re-runnable
[Openness] DOI archive · license · honest statement of consent-limited gaps
[Human-subjects] de-identified · shared only what consent allows
[Badge readiness] (if offered) which ACM badge(s) targeted · public vs review archive separated
[Fix queue] <ordered>
Info
Category Data Science
Name hri-artifact-evaluation
Version v20260724
Size 5.17KB
Updated At 2026-07-28
Language