We introduce Lucid-XR, a generative data engine for creating diverse and realistic-looking multi-modal data to train real-world robotic 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 human-to-robot pose retargeting. Data collected is further amplified by a physics-guided video generation pipeline steerable via natural language specifications. We demonstrate zero-shot transfer of robot visual policies to unseen, cluttered, and badly lit evaluation environments, after training entirely on Lucid-XR's synthetic data. We include examples across dexterous manipulation tasks that involve soft materials, loosely bound particles, and rigid body contact. Project website: https://lucidxr.github.io
@article{arxiv.2605.00244,
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},
journal= {arXiv preprint arXiv:2605.00244},
year = {2026}
}