跨平台机器人遥操作框架，提升数据采集效率与质量
XRoboToolkit: A Cross-Platform Framework for Robot Teleoperation
- 基于OpenXR标准构建，支持多种追踪方式和低延迟视觉反馈
- 在精密操作任务中验证，训练的视觉语言动作模型表现稳健
- 模块化设计适配不同机器人平台，适合大规模数据集构建
视觉-语言-动作模型的快速发展催生了对大规模、高质量机器人示范数据集的迫切需求。尽管遥操作是数据收集的主要方法，现有方案普遍存在可扩展性差、设置复杂和数据质量不高的问题。本文提出XRoboToolkit，一个基于扩展现实的跨平台机器人遥操作框架，采用OpenXR标准。系统具备低延迟立体视觉反馈、基于优化的逆运动学计算，并支持头显、控制器、手部及辅助运动追踪等多种追踪模态。其模块化架构可无缝集成于不同机器人平台与仿真环境，涵盖精密操作机械臂、移动机器人和灵巧手。通过精密操作任务验证框架有效性，并利用生成数据训练的VLA模型展现出稳健的自主性能，证明数据质量优异。
原文摘要 · Abstract (English)
The rapid advancement of Vision-Language-Action models has created an urgent need for large-scale, high-quality robot demonstration datasets. Although teleoperation is the predominant method for data collection, current approaches suffer from limited scalability, complex setup procedures, and suboptimal data quality. This paper presents XRoboToolkit, a cross-platform framework for extended reality based robot teleoperation built on the OpenXR standard. The system features low-latency stereoscopic visual feedback, optimization-based inverse kinematics, and support for diverse tracking modalities including head, controller, hand, and auxiliary motion trackers. XRoboToolkit's modular architecture enables seamless integration across robotic platforms and simulation environments, spanning precision manipulators, mobile robots, and dexterous hands. We demonstrate the framework's effectiveness through precision manipulation tasks and validate data quality by training VLA models that exhibit robust autonomous performance.
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