Robot learning is a confluence field: any given CoRL paper inherits from at least three literatures moving at different speeds. Reviewers here are typically expert in one lane and conversant in the rest — so a related-work section that covers only the authors' home lane reads as blinkered to a third of the pool.
| Lane | What reviewers check | Typical miss |
|---|---|---|
| Robot-learning lineage (CoRL, RSS, ICRA/IROS learning papers) | Do you know the last 2–3 years of the task family you claim? | Citing only your lab's chain of prior work |
| ML methods (NeurIPS/ICLR/ICML) | Is the algorithmic idea actually new, or a known method re-embodied? | Renaming an established technique with a robotics word |
| Classical robotics (planning, control, estimation) | Does a non-learning baseline solve your task? Why learn at all? | Dismissing model-based methods without citation or comparison |
| Overlay: foundation-model / VLA wave | Position relative to the current generation of pretrained robot policies | Comparing against a wave that is two generations stale |
| Overlay: concurrent arXiv | Preprint culture is aggressive here; overlap appears mid-review | Silence on a widely known concurrent preprint |
Practical breadth check: your citation list should contain entries from at least two recent CoRL volumes, at least one ML venue, and at least one classical robotics source — or an explicit reason why a lane is genuinely empty.
Between the late-May CoRL deadline and September decisions, the arXiv state of your subfield will change. Norms to apply:
corl-experiments covers fairness mechanics).CoRL proceedings publish through PMLR, and two mechanical errors recur:
PMLR v164, 2022, and SayCan from CoRL 2022
appears with 2023 pagination metadata. Cite the conference edition in prose
("at CoRL 2021") even when the BibTeX year says otherwise, and keep the pair
consistent across your references.@inproceedings{chen2022inhand,
title = {A System for General In-Hand Object Re-Orientation},
author = {Chen, Tao and Xu, Jie and Agrawal, Pulkit},
booktitle = {Proceedings of the 5th Conference on Robot Learning (CoRL 2021)},
series = {Proceedings of Machine Learning Research},
volume = {164},
pages = {297--307},
year = {2022}, % PMLR publication year != conference year — keep both visible
publisher = {PMLR}
}
Prefer arXiv-to-PMLR upgrade passes before camera-ready: many robot-learning papers you cited as preprints during writing will have acquired proceedings entries by October.
[ ] All three lanes represented (or absence argued), plus VLA-wave currency
[ ] ≥2 recent CoRL volumes cited within the claimed task family
[ ] Best classical/non-learning approach acknowledged, compared or excused
[ ] Concurrent-work paragraph drafted if a known preprint is close
[ ] Every "closest work" citation appears in the experiments, or is excused
[ ] PMLR volume/year pairs verified against volume indexes; no misattribution
[ ] Self-citations third-person; no possessive framing; no transitive leaks
Verify volume anchors and any new proceedings arrangements at https://proceedings.mlr.press/ and the live corl.org pages; this file's anchors were checked 2026-07-08.