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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.00244 |
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| _version_ | 1866909007137996800 |
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| author | Ravan, Yajvan Rashid, Adam Yu, Alan McClennen, Kai Huh, Gio Yang, Kevin Yang, Zhutian Yu, Qinxi Wang, Xiaolong Isola, Phillip Yang, Ge |
| author_facet | Ravan, Yajvan Rashid, Adam Yu, Alan McClennen, Kai Huh, Gio Yang, Kevin Yang, Zhutian Yu, Qinxi Wang, Xiaolong Isola, Phillip Yang, Ge |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_00244 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Lucid-XR: An Extended-Reality Data Engine for Robotic Manipulation Ravan, Yajvan Rashid, Adam Yu, Alan McClennen, Kai Huh, Gio Yang, Kevin Yang, Zhutian Yu, Qinxi Wang, Xiaolong Isola, Phillip Yang, Ge Robotics Computer Vision and Pattern Recognition 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 |
| title | Lucid-XR: An Extended-Reality Data Engine for Robotic Manipulation |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.00244 |