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

arXiv: https://arxiv.org/abs/2605.03452 · HTML: https://mlsanity.com/paper/2605.03452/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2026.

## Built on by (most cited first, 3 of 3)

- [EgoHumanoid-V2: Human-to-Humanoid Transfer of Coordinated Whole-Body Skills for Loco-Manipulation](https://mlsanity.com/paper/2609.37181/) (2026)
- [EWAM: Emergent Depth-Wise Specialization in a Unified Embodied Model -- From Semantic Understanding through Visual Foresight to Action](https://mlsanity.com/paper/2609.39973/) (2026)
- [Workhorse: Learning Robust Whole-Body Humanoid Loco-Manipulation from Human Data](https://mlsanity.com/paper/2610.09117/) (2026)

## Abstract (arXiv)

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.