The acquisition of high-quality, action-aligned demonstration data remains a fundamental bottleneck in scaling foundation models for dexterous robot manipulation. Although robot-free human demonstrations (e.g., the UMI paradigm) offer a scalable alternative to traditional teleoperation, current systems are constrained by sub-optimal hardware ergonomics, open-loop workflows, and a lack of systematic data-mixing strategies. To address these limitations, we present XRZero-G0, a hardware-software co-designed system for embodied data collection and policy learning. The system features an ergonomic, virtual reality interface equipped with a top-view camera and dual specialized grippers to directly improve collection efficiency. To ensure dataset reliability, we propose a closed-loop collection, inspection, training, and evaluation pipeline for non-proprioceptive data. This workflow achieves an 85% data validity rate and establishes a transparent mechanism for quality control. Furthermore, we investigate the empirical scaling behaviors and optimal mixing ratios of robot-free data. Extensive experiments indicate that combining a minimal volume of real-robot data with large-scale robot-free data (e.g., a 10:1 ratio) achieves performance comparable to exclusively real-robot datasets, while reducing acquisition costs by a factor of twenty. Utilizing XRZero-G0, we construct a 2,000-hour robot-free dataset that enables zero-shot cross-embodiment transfer to a target physical robot, demonstrating a highly scalable methodology for generalized real-world manipulation.Our project repository: https://github.com/X-Square-Robot/XRZero-G0

翻译：高质量、与动作对齐的示范数据的获取，仍然是扩展灵巧机器人操控基础模型的核心瓶颈。尽管无机器人人类示范（例如UMI范式）为传统遥操作提供了一种可扩展的替代方案，但当前系统受限于次优的硬件人体工学、开环工作流程以及缺乏系统性的数据混合策略。为应对这些局限，我们提出了XRZero-G0，一种面向具身数据收集与策略学习的硬件-软件协同设计系统。该系统配备了人体工学虚拟现实接口，集成俯视摄像头与双专用夹爪，直接提升了数据收集效率。为保障数据集可靠性，我们提出了一套针对非本体感知数据的闭环收集、检查、训练与评估流程。该工作流程实现了85%的数据有效率，并建立了透明的质量控制机制。此外，我们研究了无机器人数据的经验性扩展行为与最优混合比例。大量实验表明，将最小量的真实机器人数据与大规模无机器人数据（例如10:1比例）结合，可达到与纯真实机器人数据集相当的性能，同时将采集成本降低二十倍。利用XRZero-G0，我们构建了一个2000小时的无机器人数据集，实现了对目标物理机器人的零样本跨具身迁移，展示了一种高度可扩展的通用现实世界操控方法。我们的项目仓库：https://github.com/X-Square-Robot/XRZero-G0