HiFi-UMI造了一套便携数据采集系统，不用真机器人就能让策略直接部署，精度3mm，效果跟真实遥操作差不多，还开源了2000小时数据集，搞机器人操作的可以看看。
HiFi-UMI是一个便携式UMI数据生产系统，无需外部跟踪即可达到3毫米末端执行器精度。它通过头戴式立体惯性SLAM、原生相对位姿、微秒级GPIO触发和每只手两个广角相机（覆盖约200度）提升数据质量。使用该数据后训练的策略可直接部署到真实机器人，在StarVLA-QwenPI、OpenPI-pi_0.5和LingBot-VA三个骨干上成功率差异仅为-2.5、+3.1和-0.6个百分点。在精密插入任务上最强策略达到85%成功率。预训练4000小时可将十个未见任务的动作误差降低41%，并在StarVLA-QwenPI上将真实机器人成功提升18.1个百分点。研究团队开源了2000小时微秒同步超广角数据集HiFi-UMI-2K。
HiFi-UMI: Learning Deployable Manipulation Policies from High-Fidelity UMI Data Alone
Learning deployable manipulation policies is bottlenecked by the scarcity of data that is both high-fidelity and scalable. Real-robot teleoperation is accurate but costly to scale; robot-free UMI capture scales readily, and current practice uses the resulting data mainly for pre-training, adding a small real-robot "anchor" at post-training. We ask whether raising the fidelity of robot-free UMI data, rather than shrinking the real-robot fraction, can remove that anchor. We present HiFi-UMI, a portable UMI data-production system co-designed for trajectory accuracy, inter-gripper relative pose, synchronization, and field of view: head-mounted offline stereo-inertial SLAM, native rather than reconstructed relative pose, a shared microsecond GPIO trigger, and two wide-angle cameras per hand covering ~200 degrees. It reaches 3 mm workspace-local end-effector accuracy without external tracking infrastructure. Using this corpus, we demonstrate zero-robot post-training: a policy post-trained solely on HiFi-UMI demonstrations deploys directly on a real robot and matches in-domain teleoperation across three backbones spanning the vision-language-action and world-action-model families, with success-rate differences of -2.5, +3.1, and -0.6 percentage points on StarVLA-QwenPI, OpenPI-pi_0.5, and LingBot-VA; the strongest policy reaches 85% on a precision insertion task, even though the teleoperation baseline is collected in the evaluation scene and no HiFi-UMI trajectory is. Pre-training on 4,000 hours from the same corpus lowers action error on ten unseen tasks by 41% and, on StarVLA-QwenPI, raises real-robot success by a further 18.1 percentage points. We open-source HiFi-UMI-2K, 2,000 hours of microsecond-synchronized, ultra-wide-FoV demonstrations, each automatically reconstructed and validated through simulation replay, as a large-scale, high-fidelity resource for the robot-learning community.