日本フィジカルAI新聞

世界のフィジカルAIを、日本語で。

週刊ニュースレター購読
arXiv:2605.00244

Lucid-XR: An Extended-Reality Data Engine for Robotic Manipulation

Lucid-XR: An Extended-Reality Data Engine for Robotic Manipulation

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著者: Yajvan Ravan, Adam Rashid, Alan Yu, Kai McClennen, Gio Huh, Kevin Yang, Zhutian Yang, Qinxi Yu, Xiaolong Wang, Phillip Isola, Ge Yang

分類: cs.RO, cs.CV

原文アブストラクト

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