CoRL Artifact Evaluation

CoRL has no separate artifact-evaluation track or badge system in the 2026 materials verified for this pack (a formal AE track remains 待核实 each cycle). Artifacts are instead judged twice, informally: by reviewers deciding whether to trust your tables, and by the community deciding — for years afterward — whether your method becomes a baseline. Package for both audiences from the start.

Artifact inventory for a robot-learning paper

| Artifact | Review-time form (anonymous) | Public form (post-acceptance) | |---|---|---| | Training code + configs | Scrubbed ZIP in the supplementary | GitHub repo, tagged release matching the paper | | Evaluation harness | Same ZIP; scripts + fixed init-state lists | Same repo; the part reused most by others | | Demonstration data | Small sample in ZIP; full set described | Archive with DOI + datasheet, license stated | | Policy checkpoints | Optional if small; else described | Hosted weights keyed to each results table | | Sim environments / tasks | Env definitions + versions in ZIP | Repo or upstream PR to the benchmark suite | | Hardware recipe | Appendix spec (rig, control interface) | Project page: BOM-level detail, photos | | Overview video | Supplementary upload (≤ 250 MB, 2026 cap) | Project page / video host — PMLR takes no video |

The reuse test

Design the release around one question: can a stranger reproduce your evaluation without emailing you? Concretely, a competent robot-learning grad student should be able to (1) install, (2) run evaluation with a released checkpoint, and (3) regenerate one paper table, in an afternoon, in simulation. Training reproduction and hardware reproduction are aspirational tiers above this floor — label the tiers honestly rather than implying all three.

text
Release tiers — declare one per artifact in the README:
  T1  evaluate: released checkpoint + eval script reproduce Table N in sim
  T2  retrain:  configs + data (or data recipe) reproduce the training run
                within the reported seed spread
  T3  re-embody: rig documentation sufficient to attempt hardware replication

Review-time packaging (anonymous)

• One ZIP, one top-level README, a table mapping paper table → command.
• Determinize what you can: fixed evaluation seeds, fixed init-state lists,

pinned dependency versions (corl-reproducibility owns the manifest format).

• Anonymize mechanically, then by eyeball: repo history stripped, paths cleaned,

no W&B/HF org names, no grant numbers in license headers, and remember the URL rule — an anonymized page on a lab-named domain still leaks.

• Keep it runnable without a robot: reviewers do not have your hardware, so the

artifact's demonstrable slice is sim evaluation plus logs/video of the hardware runs. Ship episode-level logs (CSV) for every hardware table.

Post-acceptance: durable release

• Timing. The 2026 camera-ready deadline (October 12) is when links get

frozen into the PMLR record — stand up the public repo, dataset archive, and project page before finalizing the PDF so the printed URLs are real.

• Durability ladder. Lab web servers die with funding cycles: put datasets

and static artifacts in DOI-granting archives, code in a tagged repository release, and treat the project page as a pointer hub rather than the sole home.

• Checkpoint provenance. Key each released checkpoint to its table and seed

("ckpt_t3_s2 = Table 3, seed 2"); publishing only a best-seed checkpoint while the paper reports seed means invites a mismatch report you'll answer publicly — CoRL reviews of accepted papers are public, and so is the follow-up scrutiny.

• License deliberately. Code (permissive vs copyleft), data (usage terms,

consent constraints for human video), and weights (increasingly their own license class) are three separate decisions; "no license" means "nobody may legally build on this."

Benchmarks and datasets as first-class artifacts

If the artifact is the contribution (a task suite, a large demo corpus), the bar rises from reuse to stewardship:

• Version the benchmark explicitly (v1.0 at camera-ready) and changelog any

post-publication fix; silent edits corrupt every downstream comparison.

• Publish the evaluation protocol as executable code, not prose — episode

counts, init distributions, success criteria — so future papers cite numbers produced by your harness rather than reimplementations.

• Provide a datasheet: collection method, operator demographics if teleop,

filtering, known biases, consent/licensing status of any human footage.

Failure patterns that surface after publication

Because CoRL reviews of accepted papers are public and the community reuses artifacts aggressively, release defects surface fast and visibly:

• The vanished environment: the eval harness imports a benchmark fork whose

branch was deleted; vendor the environment code or pin an archived copy.

• The config drift: the repo's default config differs from the paper's

runs; ship the exact configs used, named per table, and make defaults match.

• The GPU-only afternoon: an evaluation that silently requires 8×A100 to

run at all; document minimum hardware and offer a reduced smoke-test target.

• The consent surprise: teleop or human-video data released without the

consent scope to permit it; resolve data-rights questions before the paper promises release, not after.

Release checklist

text
[ ] Tier (T1/T2/T3) declared per artifact; T1 actually tested by a
    teammate on a clean machine
[ ] README: install steps, table→command map, expected tolerances
[ ] Checkpoints keyed to tables and seeds; selection rule stated
[ ] Dataset archived with DOI, datasheet, and license
[ ] Hardware logs + uncut eval video published for hardware tables
[ ] Licenses chosen for code, data, and weights separately
[ ] All URLs live before camera-ready freeze (2026: Oct 12 AoE)
[ ] Anonymous variant retired only after decisions are out

Re-check the live cycle for any new artifact policy, badge program, or required availability statement at https://www.corl.org/contributions/instruction-for-authors — venue policy here is young and moves quickly, like the field itself.