Skill UI

A comprehensive guideline for researchers developing robot-learning papers. It details best practices for experimental design, emphasizing statistical rigor, handling stochasticity (training seeds, initial states), bridging the sim-to-real gap, and ensuring fairness across baseline policy families (BC/RL/VLA). This resource helps ensure claims are supported by credible evidence.

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).

A comprehensive guide detailing the submission rules and best practices for supplementary materials, including videos, appendices, code, and data, for top-tier robotics conferences. It outlines distinct submission channels, strict technical requirements (e.g., 250MB video cap, anonymity), and the necessary transition steps from the review phase to the final camera-ready publication format.

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 guide provides a comprehensive framework for conducting heterogeneity and conditionality analysis in Chinese Public Administration (CPA) research. It outlines theory-guided methods for selecting dimensions (e.g., administrative level, governance context, institutional structure) and details best practices for both quantitative (Chow test, interaction terms) and qualitative (QCA) comparison, ensuring findings are theoretically grounded and methodologically rigorous.

This skill provides rigorous standards and best practices for running and reporting analyses within Comparative Political Studies (CPS). It addresses common pitfalls in cross-national data, such as measurement error, few clusters, and spurious correlation, ensuring the resulting research is transparent, robust, and replication-ready for academic review.

This guide provides a comprehensive methodological framework and checklist for researchers conducting quasi-experimental and micro-panel studies in rural economics. It systematically addresses common econometric pitfalls, such as self-selection bias, reverse causality, and endogeneity, detailing best practices for advanced methods like Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity (RDD), and Propensity Score Matching (PSM).

This comprehensive guide provides a structured workflow for responding to reviewer reports and revising academic manuscripts, specifically tailored for high-impact journals like China Rural Economy. It details best practices for point-by-point responses, managing methodological critiques (e.g., endogeneity), and ensuring logical consistency between the revised text and the rebuttal letter.

A comprehensive guide for creating rigorous, self-contained, and scientifically accurate tables and figures for academic manuscripts, particularly in the field of criminology. It details best practices for visualizing complex statistical results, including age-crime curves, trajectory plots, survival analyses, and spatial hot-spot maps. The focus is on ensuring evidence is clear, reproducible, and communicates the research findings effectively to expert reviewers.

This comprehensive guide helps authors assess if their theoretical or interdisciplinary manuscript is a suitable fit for Critical Inquiry. It details the journal's high-theory expectations, the necessity of conceptual intervention, cross-disciplinary stakes, and advanced best practices for academic submission, ensuring work has broad intellectual impact.

A specialized routing tool designed for academic researchers submitting manuscripts to top-tier Computer Science and AI conferences. It helps users navigate complex decision points, such as selecting the best venue (e.g., ICML vs NeurIPS), matching contribution types (theory, system, empirical study), and adhering to specific submission guidelines and review constraints.

Provides comprehensive guidelines for researchers packaging and releasing artifacts (data, code, instruments) derived from real communities in CSCW research. It emphasizes ethical constraints, de-identification techniques (such as quote search and join attacks), and best practices for protecting studied communities' privacy, ensuring responsible sharing.