Open-sourced, user-friendly tools form the bedrock of scientific advancement across disciplines. The widespread adoption of data-driven learning has led to remarkable progress in multi-fingered dexterity, bimanual manipulation, and applications ranging from logistics to home robotics. However, existing data collection platforms are often proprietary, costly, or tailored to specific robotic morphologies. We present OPEN TEACH, a new teleoperation system leveraging VR headsets to immerse users in mixed reality for intuitive robot control. Built on the affordable Meta Quest 3, which costs $500, OPEN TEACH enables real-time control of various robots, including multi-fingered hands and bimanual arms, through an easy-to-use app. Using natural hand gestures and movements, users can manipulate robots at up to 90Hz with smooth visual feedback and interface widgets offering closeup environment views. We demonstrate the versatility of OPEN TEACH across 38 tasks on different robots. A comprehensive user study indicates significant improvement in teleoperation capability over the AnyTeleop framework. Further experiments exhibit that the collected data is compatible with policy learning on 10 dexterous and contact-rich manipulation tasks. Currently supporting Franka, xArm, Jaco, and Allegro platforms, OPEN TEACH is fully open-sourced to promote broader adoption. Videos are available at https://open-teach.github.io/.

翻译：开源、用户友好的工具构成了各学科科学进步的基石。数据驱动学习的广泛应用推动多指灵巧操作、双臂协作以及从物流到家庭机器人等应用领域取得了显著进展。然而，现有数据采集平台往往具有专有性、成本高昂或仅适配特定机器人形态。我们提出开放教学（OPEN TEACH），这是一种新型遥操作系统，利用VR头显使用户沉浸于混合现实中，实现直观的机器人控制。该系统基于售价500美元、价格亲民的Meta Quest 3构建，通过简易操作的应用程序支持实时控制多种机器人，包括多指手和双臂机械臂。用户可通过自然手势与动作，以高达90Hz频率操控机器人，并借助提供近景环境视图的交互式界面控件获得流畅视觉反馈。我们在不同机器人上开展的38项任务中验证了开放教学的通用性。全面用户研究表明，与AnyTeleop框架相比，其遥操作能力显著提升。进一步实验显示，所采集数据可兼容10项灵巧且包含密集接触的操纵任务中的策略学习。目前开放教学支持Franka、xArm、Jaco及Allegro平台，并已完全开源以促进更广泛采用。相关视频见https://open-teach.github.io/。