arXiv cs.AI· Govind Velamoor, Adrian Lin, Sam Jung, Spencer Mateega, Brandon Y. Yang, Amish Sethi·· 3 小时前AI 评分55
UMI-Live 开源全栈双臂机器人数据采集系统
UMI-Live for In-The-Wild Bimanual Robotics Data Collection
AI 导读
论文提出 UMI-Live，一个面向真实场景采集的全栈开源双臂 UMI 系统，硬件集成相机与 LiDAR 惯性感知、机载计算和电源，无需外部追踪设备或采集环境内标定。
正文 · 原文
Abstract:Universal Manipulation Interface (UMI) devices allow human experts to demonstrate tasks for robot learning without teleoperating physical robots. Using these demonstrations as training data still requires a consistent hardware setup, calibration, and data processing. We present UMI-Live, a full-stack open-source bimanual UMI system for in-the-wild data collection. We design our custom UMI-Live hardware to integrate camera and LiDAR--inertial sensing, onboard compute, and power, without external tracking equipment or calibration within the collection environment. We implement a simple user interface for recording data while an asynchronous pipeline performs parallel SLAM reconstruction and publishes training-ready datasets. We also release software that supports dual-YAM teleoperation and VLA policy training and inference. We validate UMI-Live data quality by fine-tuning $\pi_{0.5}$ and MolmoAct2, achieving success rates of 10/10 and 8/10, respectively on a bimanual pick-and-place task. Furthermore, we release 40 hours of UMI-Live data across three tasks, and complete hardware designs, software, and reproduction guides at our $\href{this https URL}{\text{project website}}$.
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.39385 [cs.RO] |
| (or arXiv:2610.39385v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.39385 arXiv-issued DOI via DataCite |
Submission history
From: Brandon Yang [view email]
[v1]
Thu, 29 Oct 2026 05:18:16 UTC (39,364 KB)
来源：arXiv cs.AI · arxiv.org