Skill UI

Provides expert guidance on designing, implementing, and optimizing cutting-edge computer vision pipelines. Expertise spans real-time object detection (YOLO26), advanced promptable segmentation (SAM 3), and leveraging Vision Language Models (VLMs) for complex visual reasoning. Ideal for building high-performance systems in robotics, autonomous vehicles, and industrial inspection, including 3D reconstruction and edge deployment optimization.

PyLabRobot is a vendor-agnostic, pure Python SDK designed for autonomous and automated laboratory workflows. It provides a unified interface to control a wide array of laboratory equipment, including liquid handling robots (Hamilton, Opentrons, Tecan), plate readers, pumps, shakers, and incubators. Ideal for developing complex, multi-device, reproducible protocols and simulating experiments before physical execution.

Orchestrates a robotics-specific literature survey, novel idea generation, novelty check, and critical review for embodied AI and robotics research. Helps turn a broad robotics direction (manipulation, locomotion, navigation, drones, humanoids) into benchmark-grounded, simulation-first pilot ideas. Use when you need robotics idea discovery or sim2real topic selection.

Guides fine-tuning and evaluation of OpenVLA-OFT/OFT+ for robot action generation, covering LoRA adaptation, continuous action heads, FiLM conditioning, and LIBERO/ALOHA benchmarks plus deployment considerations.

A comprehensive guide for designing and presenting rigorous experiments and simulations for top-tier journals in Control Theory, Robotics, and Smart Systems. Covers RQ-driven design, establishing fair baselines, providing theoretical proofs of stability/convergence, performing ablation studies, and ensuring statistical significance, helping authors build an undeniable evidence chain for double-blind external review.

This skill guides researchers to determine if their topic fits the scope of Acta Automatica Sinica (AAS). It provides detailed boundaries for control theory, pattern recognition, robotics, and smart systems, helping authors distinguish their contribution from pure algorithms or system software, and advising on appropriate article type (Review/Paper/Short) to avoid initial rejection.

Provides comprehensive style guidance for writing and refining scientific manuscripts for Acta Automatica Sinica (AAS). This guide covers the structure of the reporting abstract (Purpose/Method/Result/Conclusion), handling bilingual requirements, formatting rules for equations, figures, and adherence to national standards for units and citations (GB/T 7714). Ideal for papers in control theory, robotics, and intelligent systems.

This tool guides authors on whether their manuscript fits the scope of Advanced Functional Materials. It emphasizes the critical structure-function-application chain, covering highly advanced fields such as optoelectronics, biomedical devices, and soft robotics. It provides criteria for methodology, required evidence, and journal-specific house style.

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.

This comprehensive guide outlines best practices for packaging and releasing scientific artifacts—including code, training configurations, demonstration data, and policy checkpoints—for robotics research papers. It details how to prepare materials for anonymous reviewer assessment and for durable public release, ensuring the work is fully reproducible by the academic community.

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.