Open-TeleVision: Teleoperation with Immersive Active Visual Feedback

By Xuxin Cheng, Jialong Li, Shiqi Yang, Ge Yang, Xiaolong Wang. Conference on Robot Learning, 2024

308 citations recorded by the source corpus.

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/

Subfields: Sociology and Political Science, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/c66570cfe127512dde91da977ce973e42abb7e2e