Lucid-XR: An Extended-Reality Data Engine for Robotic Manipulation
Yajvan Ravan, Adam Rashid, Alan Yu, Kai McClennen, Gio Huh, Kevin Yang, Zhutian Yang, Qinxi Yu
Abstract
We introduce Lucid-XR, a generative data engine for creating diverse and realistic-looking data to train real-world robot systems. At the core of Lucid-XR is vuer, a web-based physics simulation environment that runs directly on the XR headset, enabling internet-scale access to immersive, latency-free virtual interactions without requiring specialized equipment. The complete system integrates on-device physics simulation with on-device human-to-robot pose retargeting, that are further amplified by a physics-guided video generation pipeline commandable with natural language specifications. We demonstrate zero-shot sim-to-real transfer of robot visual policies, trained entirely on Lucid-XR's synthetic data, across bimanual and dexterous manipulation tasks that involve flexible materials, adhesive interaction between particles, and rigid body contact.
BibTeX
@inproceedings{
ravan2025lucidxr,
title={Lucid-{XR}: An Extended-Reality Data Engine for Robotic Manipulation},
author={Yajvan Ravan and Adam Rashid and Alan Yu and Kai McClennen and Gio Huh and Kevin Yang and Zhutian Yang and Qinxi Yu and Xiaolong Wang and Phillip Isola and Ge Yang},
booktitle={9th Annual Conference on Robot Learning},
year={2025},
url={https://openreview.net/forum?id=3p7rTnLJM8}
}