Teleoperation serves as a powerful method for collecting on-robot data essential for robot learning from demonstrations. The intuitiveness and ease of use of the teleoperation system are crucial for ensuring high-quality, diverse, and scalable data. To achieve this, we propose an immersive teleoperation system Open-TeleVision that allows operators to actively perceive the robot's surroundings in a stereoscopic manner. Additionally, the system mirrors the operator's arm and hand movements on the robot, creating an immersive experience as if the operator's mind is transmitted to a robot embodiment. We validate the effectiveness of our system by collecting data and training imitation learning policies on four long-horizon, precise tasks (Can Sorting, Can Insertion, Folding, and Unloading) for 2 different humanoid robots and deploy them in the real world. The system is open-sourced at: https://robot-tv.github.io/

翻译：遥操作是收集机器人演示学习所需本体数据的一种有效方法。遥操作系统的直观性与易用性对于确保高质量、多样化且可扩展的数据至关重要。为此，我们提出了一种沉浸式遥操作系统 Open-TeleVision，该系统允许操作者以立体方式主动感知机器人周围环境。此外，系统将操作者的手臂和手部动作镜像映射到机器人上，创造出仿佛操作者意识被传输至机器人载体的沉浸式体验。我们通过在两种不同人形机器人上收集数据并针对四项长时程、高精度任务（易拉罐分拣、易拉罐插入、折叠与卸载）训练模仿学习策略，并在现实世界中部署验证了系统的有效性。该系统已在以下网址开源：https://robot-tv.github.io/