Skill UI

This detailed guide teaches authors how to structure and position their research paper within the highly interdisciplinary field of robot learning. It covers essential best practices, including reviewing multiple concurrent literature streams (ML methods, classical robotics, robot lineage, VLA models) and adhering to strict academic citation hygiene (e.g., handling PMLR year offsets).

This comprehensive guide establishes a rigorous framework for ensuring reproducibility in robot learning research. It details how to document both the "rerunnable half" (software, simulation versions, training configs, seeds) and the "auditable half" (physical hardware specs, sensors, firmware). Adhering to these standards allows experts to verify and rebuild the experimental results.

A comprehensive pre-upload audit checklist for submitting papers to the CoRL (Conference on Robot Learning). This guide details specific requirements for the submission package, including the mandatory 8-page limit (which must account for a dedicated Limitations section), adherence to double-anonymous rules, and detailed sweeps for preserving author and lab anonymity in text, figures, and videos. Essential for ensuring compliance with complex conference guidelines.

This guide helps researchers determine if their embodied AI or robotics project is best suited for the Conference on Robot Learning (CoRL). It provides a rigorous two-axis test—assessing if learning is the core contribution and if embodiment is essential to the claim—to decide between submitting to CoRL, ICRA, IROS, NeurIPS, or other journals.

This skill provides a comprehensive, year-round workflow guide for submitting research papers to the CoRL Conference on Robot Learning. It meticulously breaks down the annual cycle into manageable phases—including pre-submission preparation, managing reviewer feedback, crafting rebuttals, and preparing camera-ready versions. It assists teams in planning their efforts across critical milestones and navigating the academic publishing process.

This comprehensive style guide is written for authors submitting research papers to the CoRL conference. It provides specific directives on structuring the narrative to satisfy two distinct audiences: machine learning theorists and robotics engineers. It emphasizes quantifying all claims with concrete evidence scales (e.g., success rates, frequency), structuring the argument within a limited page count, and properly integrating mandatory sections like Limitations.

A comprehensive guide for hardening deep learning papers, particularly in Computer Vision (CVPR). It details best practices for achieving rigorous reproducibility by utilizing the Compute Reporting Form (CRF), maintaining a detailed recipe ledger, ensuring benchmark split hygiene, and meticulously documenting all training hyperparameters, seeds, and variance analyses for academic publication.

This guide provides best practices for authors writing the Related Work section for top-tier AI conferences (e.g., ECAI). It instructs users on how to cover diverse AI lanes (symbolic, learning, multi-agent, application) for broad reviewers, employ 'Delta-first' positioning to state novelty concisely, and adhere to strict page budgets and double-blind review rules.

A comprehensive guide for advanced econometric techniques in causal inference, covering modern approaches for quasi-experimental designs such as staggered Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity Design (RDD), and Double Machine Learning (DML). It details best practices, critical diagnostic tests (e.g., weak instruments, parallel trend checks, bias decomposition), and robust reporting standards required for high-impact economic research.

This skill provides a strategic guide for authors evaluating if their AI/data mining manuscript is a suitable fit for the ECML PKDD conference. It covers scope definition, evidence requirements, academic framing, and strategic positioning relative to major global venues (e.g., ICML, NeurIPS). Use this to refine your submission narrative and maximize your chances of acceptance.

This guide provides an in-depth framework for assessing the foundational significance of theoretical computer science results. It details the rigorous criteria used by top-tier conferences like FOCS (Foundations of Computer Science) to judge a paper's impact, focusing on its consequence, how it overcomes known obstacles, and its durability beyond best constants. It also maps various research types (e.g., cryptographic, algorithmic, learning theory) to the most suitable venue (e.g., FOCS, STOC, SODA, CCC).

This guide details the best practices for packaging, documenting, and releasing research artifacts (code, models, data) for top-tier ML conferences. It covers two stages: preparing a non-identifiable archive for anonymous review, and managing the public release runway, focusing on reproducibility, licensing, and sustained maintenance.