Provides an expert framework for authors submitting to the Conference on Robot Learning (CoRL). It guides writers on how to re-frame general computer science manuscripts into specialized, robust robot-learning narratives. The guide covers defining the contribution type, establishing the evidence bar (from simulation to real-world transfer), and strategically positioning the work against major robotics conferences like ICRA and IROS.
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.
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.
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>
