Lucid-XR：一种用于机器人操控的扩展现实数据引擎
Lucid-XR: An Extended-Reality Data Engine for Robotic Manipulation
AI总结：
Lucid-XR通过XR头显直接运行的物理模拟环境生成多模态数据，实现零样本迁移至复杂环境，支持软材料、松散颗粒和刚体接触的精细操控任务。
摘要：
我们介绍了Lucid-XR，一种生成多样化且逼真多模态数据的生成数据引擎，用于训练现实世界的机器人系统。Lucid-XR的核心是vuer，一个基于网页的物理模拟环境，可直接在XR头显上运行，实现互联网级别的沉浸式无延迟虚拟交互，无需专用设备。整个系统集成了设备端物理模拟与人类到机器人的姿态重定向。收集的数据进一步通过受物理指导的视频生成管道放大，该管道可通过自然语言规范进行调节。我们展示了在完全基于Lucid-XR合成数据训练后，机器人视觉策略在未见的杂乱且照明不良的评估环境中实现零样本迁移。我们包括了涉及软材料、松散结合的颗粒和刚体接触的精细操控任务示例。项目网站：https://lucidxr.github.io
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
