We present UMI-3D, a multimodal extension of the Universal Manipulation Interface (UMI) for robust and scalable data collection in embodied manipulation. While UMI enables portable, wrist-mounted data acquisition, its reliance on monocular visual SLAM makes it vulnerable to occlusions, dynamic scenes, and tracking failures, limiting its applicability in real-world environments. UMI-3D addresses these limitations by introducing a lightweight and low-cost LiDAR sensor tightly integrated into the wrist-mounted interface, enabling LiDAR-centric SLAM with accurate metric-scale pose estimation under challenging conditions. We further develop a hardware-synchronized multimodal sensing pipeline and a unified spatiotemporal calibration framework that aligns visual observations with LiDAR point clouds, producing consistent 3D representations of demonstrations. Despite maintaining the original 2D visuomotor policy formulation, UMI-3D significantly improves the quality and reliability of collected data, which directly translates into enhanced policy performance. Extensive real-world experiments demonstrate that UMI-3D not only achieves high success rates on standard manipulation tasks, but also enables learning of tasks that are challenging or infeasible for the original vision-only UMI setup, including large deformable object manipulation and articulated object operation. The system supports an end-to-end pipeline for data acquisition, alignment, training, and deployment, while preserving the portability and accessibility of the original UMI. All hardware and software components are open-sourced to facilitate large-scale data collection and accelerate research in embodied intelligence: \href{https://umi-3d.github.io}{https://umi-3d.github.io}.

翻译：我们提出UMI-3D，这是通用操作接口（UMI）的多模态扩展，旨在实现具身操作中鲁棒且可扩展的数据采集。尽管UMI实现了便携式腕戴数据采集，但其依赖单目视觉SLAM的方式使其易受遮挡、动态场景和跟踪失败的影响，限制了其在现实环境中的适用性。UMI-3D通过引入轻量级低成本激光雷达传感器并紧密集成至腕戴接口来克服这些局限，实现了在挑战性条件下具备精确度量尺度位姿估计的以激光雷达为中心的SLAM。我们进一步开发了硬件同步的多模态传感流水线以及统一时空标定框架，将视觉观测与激光雷达点云对齐，生成演示的一致三维表示。尽管保留了原始二维视觉运动策略框架，但UMI-3D显著提升了采集数据的质量与可靠性，直接转化为增强的策略性能。大量真实世界实验表明，UMI-3D不仅在标准操作任务上实现高成功率，还能学习原始纯视觉UMI设置难以或无法完成的任务，包括大型可变形物体操作和铰接物体操作。该系统支持数据采集、对齐、训练与部署的端到端流水线，同时保持原始UMI的便携性与可访问性。所有硬件和软件组件均已开源，以促进大规模数据采集并加速具身智能研究：\href{https://umi-3d.github.io}{https://umi-3d.github.io}