用虚拟现实系统高效收集高质量机器人操作数据，降低成本并实现跨机器人零样本迁移。
XRZero-G0: Pushing the Frontier of Dexterous Robotic Manipulation with Interfaces, Quality and Ratios
- 设计专用虚拟现实接口与双夹爪，提升非机器人数据采集效率。
- 闭环流程实现85%数据有效率，10:1真实与虚拟数据混合性能媲美纯真实数据。
- 适合机器人研究者和工业界，解决数据获取成本高难题。
高质量、动作对齐的示范数据获取仍是扩展灵巧机器人操作基础模型的关键瓶颈。尽管无需机器人的真人示范（如UMI范式）提供了传统遥操作的可扩展替代方案，但现有系统受限于硬件人因工程不佳、开环工作流及缺乏系统的数据混合策略。为此，我们提出XRZero-G0，一个软硬件协同设计的具身数据采集与策略学习系统。该系统配备符合人体工学的虚拟现实界面、顶视摄像头及双专用夹爪，直接提升采集效率。为确保数据集可靠性，提出闭环采集、检查、训练与评估流程，用于非本体感觉数据，实现85%的数据有效性，并建立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缩放行为与最优混合比例。大量实验表明，将少量真实机器人数据与大规模无机器人数据结合（如10:1比例），性能可媲美纯真实数据集，同时降低采集成本达20倍。利用XRZero-G0，我们构建了一个2,000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，验证了通用现实世界操作的高可扩展方法论。
原文摘要 · Abstract (English)
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
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