Conference On Robot Learning

Conference on Robot Learning (CoRL)

Conference positioning

Conference on Robot Learning (CoRL) is a top computer-science conference venue for robot learning, imitation, reinforcement learning, dexterous manipulation, sim-to-real, and embodied foundation models. It rewards a robot-learning paper where learning contributes to robust embodied behavior, not only simulated reward curves. Treat this skill as a fit / venue-selection / re-framing tool for conference submission strategy, not as a substitute for the current year's CFP, author kit, ethics policy, or submission portal.

Because CS conferences change deadlines, templates, page limits, review workflow, artifact rules, AI-use policy, and rebuttal formats every cycle, always verify the live official instructions before making a submission-ready recommendation. Start from the official source anchor recorded for this venue in ../../resources/conference-roster.md and ../../resources/official-source-map.md.

When to trigger

• The author names CoRL / Conference on Robot Learning as the target venue.
• A manuscript in robot learning needs a conference-fit read before being formatted or submitted.
• The paper must be re-framed from journal style or arXiv style into a selective CS conference narrative.
• The author needs an evidence-gap, anonymity, artifact, rebuttal, or re-routing diagnosis for this venue.

Scope & topic fit

• Core fit: robot learning, imitation, reinforcement learning, dexterous manipulation, sim-to-real, and embodied foundation models.
• Best submissions make a precise contribution type visible: algorithm, theorem, system, dataset, benchmark, empirical finding, design artifact, tool, or socio-technical analysis.
• The paper should explain why the result matters to CoRL's reviewers, not just why it is interesting to the authors' lab or product context.
• Position related work against the most recent conference-cycle papers in this venue and its closest siblings; stale comparisons are a common early-review weakness.
• If the contribution is interdisciplinary, state which part is CS research and which part is domain evidence.

Venue-specific calibration

• Reviewer lens: Treat CoRL as a robot learning venue whose reviewers expect the scope and evidence to match its own community. Do not submit a generic CS paper until the introduction names the exact subcommunity, contribution type, and proof or empirical standard.
• Contribution hook to foreground: the venue-specific contribution bar.
• Scope vocabulary to use naturally in the abstract and introduction: robot learning, imitation, reinforcement learning, dexterous manipulation, sim-to-real, and embodied foundation models.
• Distinctive fingerprint for reviewer calibration: robot, learning, imitation, reinforcement, dexterous, manipulation, sim-to-real, embodied, foundation, models, venue-specific, contribution, corl.
• Official anchor domain: www.corl.org. Quote annual rules only after opening that source and the current-year CFP/author kit.

Close-neighbor routing guardrail

• Use this profile only when the manuscript's central contribution is genuinely in robot learning and the author can say why CoRL reviewers are the primary audience, not merely a convenient deadline.
• Closest roster neighbors to compare before final routing: robotics-science-and-systems (RSS), ieee-international-conference-on-robotics-and-automation (ICRA), ieee-rsj- international-conference-on-intelligent-robots-and-systems (IROS), and acm-ieee- international-conference-on-human-robot-interaction (HRI). Break ties by contribution type, embodied-evidence shape, reviewer community, and the current official CFP from www.corl.org.

What distinguishes CoRL from its closest siblings

• CoRL vs HRI: choose CoRL when the paper's main novelty is the learning algorithm, policy representation, data regime, sim-to-real strategy, or embodied foundation-model behavior; choose HRI when the decisive evidence is human interaction quality, trust, collaboration, or user-study design.
• CoRL vs RSS: choose CoRL for learning-centric claims with strong robot evidence; choose RSS when the contribution is broader robotics science, planning, perception, mechanics, or systems integration where learning is only one component.
• CoRL vs ICRA/IROS: choose CoRL when reviewers must evaluate learning generalization, imitation/RL baselines, data scaling, or policy robustness; route to ICRA/IROS when the paper is a robotics application, system, hardware, or control result with a wider robotics audience.

Method & evidence bar

• Report hardware, simulation, environment, task distribution, reset procedure, and failure cases; embodied evidence must be inspectable.
• Compare against meaningful robot-learning, planning, or control baselines under matched assumptions.
• Separate simulation gains from real-world transfer and quantify reliability, not only best-case success.
• For CoRL, the evidence must support the venue-specific signature: a robot-learning paper where learning contributes to robust embodied behavior, not only simulated reward curves.
• Include limitations, negative results, compute/resource reporting, data provenance, and ethics details when they affect the claim.

Structure & house style

• Lead with the robot task and system constraint before the algorithmic component.
• Use video or supplementary material only as allowed by the current anonymous-review policy.
• Use the current official template exactly; do not guess page limits, font sizes, supplement rules, anonymity exceptions, or camera-ready requirements from old cycles.
• The introduction should answer: problem, why now, what is new, why this venue, and what evidence proves the claim.
• Put the strongest result in the main paper, not only in the appendix or supplement; reviewers should not have to reconstruct the contribution.

Official-cycle checklist

• Open the live official venue page: https://www.corl.org/
• Re-check the current cycle's CFP, author kit, submission system, abstract/paper deadlines, page limits, supplementary-material rules, anonymity policy, dual-submission policy, ethics policy, AI-use policy, artifact/code/data expectations, rebuttal/author-response format, and camera-ready requirements.
• Confirm the review workflow and portal: OpenReview / CMT / HotCRP / PCS / START or society portal, as specified for the current cycle.
• Check whether accepted papers require in-person presentation, separate registration, artifact badges, proceedings copyright, or post-acceptance release forms.
• If the live official instructions conflict with this skill, the official instructions win.

Pre-submission self-check

• One sentence states why this manuscript belongs at CoRL, using the venue's scope rather than generic "top conference" language.
• The claim is calibrated to the evidence: no broader than the datasets, proofs, systems, user studies, deployments, or threat model support.
• Related work includes the nearest current-cycle robot learning papers and explains the technical delta.
• The paper satisfies the current official template, anonymity, ethics, artifact, and rebuttal requirements.
• The main paper is self-contained enough for reviewers to evaluate novelty and correctness without hunting through external links.

Common desk-reject triggers

• Simulation-only evidence for a claim about real robots.
• No clear task distribution, few trials, or missing failure analysis.
• A learning curve without robot-specific insight or system integration.
• Formatting, anonymity, dual-submission, external-link, or supplement violations under the current-year policy.
• A contribution framed for a neighboring field while giving CoRL reviewers too little technical or empirical substance.

Re-routing decision

If the paper misses CoRL's bar, compare against ieee-international-conference-on-robotics-and-automation / ieee-rsj-international-conference-on-intelligent-robots-and-systems / robotics-science-and-systems / acm-ieee-international-conference-on-human-robot-interaction. Re-route based on contribution type, not prestige: theory to a theory venue, systems to a systems venue, application-heavy work to a domain venue, and early ideas to workshops or shorter tracks when the official CFP supports them.

Output format

[Fit] High / Medium / Low (one-line reason)
[Target] Conference on Robot Learning (CoRL)
[Contribution type] algorithm / theory / system / dataset / benchmark / empirical / design / security / other
[Main evidence gap] <single most important missing proof, experiment, study, artifact, or policy check>
[Official items to re-check] CFP / author kit / deadline / format / anonymity / ethics / AI-use / artifact / rebuttal / camera-ready
[Top rejection risk] <venue-specific risk>
[Re-route suggestion] <better-matched conference or journal if not a fit>

Source: brycewang-stanford/Awesome-Journal-Skills → Computer-Science-Conference-Skills/skills/conference-on-robot-learning/SKILL.md