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arXiv · 2605.03452

BifrostUMI: Bridging Robot-Free Demonstrations and Humanoid Whole-Body Manipulation

Hongwu Wang·Chenhao Yu·Youhao Hu·Jiachen Zhang·Yuanyuan Li·Shaqi Luo
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Abstract

High-quality demonstration data are essential for humanoid robot skill learning, especially for whole-body behaviors that require coordinated perception, locomotion, and manipulation. Existing data-collection methods largely rely on robot teleoperation, which is constrained by hardware accessibility, operator expertise, and limited efficiency. Inspired by the Universal Manipulation Interface (UMI), we propose BifrostUMI, a portable and robot-free framework for humanoid whole-body data collection. BifrostUMI uses lightweight VR devices and UMI-inspired grippers to collect sparse human keypoint trajectories, wrist-view observations, and gripper actions. These demonstrations train a high-level policy to predict future keypoints, which are retargeted to robot-native whole-body references and executed by a whole-body controller. Experiments in five real-world scenarios demonstrate the effectiveness of the proposed framework and validate the collected demonstrations for transferable humanoid whole-body skill learning.

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Hongwu Wang, Chenhao Yu, Youhao Hu, Jiachen Zhang, Yuanyuan Li, Shaqi Luo. 2026-05-05. BifrostUMI: Bridging Robot-Free Demonstrations and Humanoid Whole-Body Manipulation. https://arxiv.org/abs/2605.03452

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