Open-TeleVision: Teleoperation with Immersive Active Visual Feedback

Xuxin Cheng, Jialong Li, Shiqi Yang, Ge Yang, Xiaolong Wang

Keywords: Teleoperation, VR/AR, Imitation Learning

TL;DR: An immersive teleoperation system with active visual feedback

Abstract: 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 $\textbf{Open-TeleVision}$ 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 entire system will be open-sourced.

Video: https://youtu.be/d9EQDjU1gyQ

Website: https://robot-tv.github.io

Code: https://github.com/OpenTeleVision/TeleVision

Student Paper: yes

Submission Number: 36