# HoMMI: Learning Whole-Body Mobile Manipulation from Human Demonstrations

arXiv: https://arxiv.org/abs/2603.03243 · HTML: https://mlsanity.com/paper/2603.03243/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2026.

## Built on by (most cited first, 16 of 16)

- [World Action Models: The Next Frontier in Embodied AI](https://mlsanity.com/paper/2605.12090/) (2026)
- [Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack](https://mlsanity.com/paper/2606.14409/) (2026)
- [UMI-3D: Extending Universal Manipulation Interface from Vision-Limited to 3D Spatial Perception](https://mlsanity.com/paper/2604.14089/) (2026)
- [Learning Panorama-Aware VLA for Mobile Manipulation with Whole-Body Teleoperation](https://mlsanity.com/paper/2608.02257/) (2026)
- [ACE-Data-0: Human-Centric Ambient Capture as Embodied Data Engine](https://mlsanity.com/paper/2607.28625/) (2026)
- [Universal Manipulation Exoskeleton: Learning Compliant Whole-body Policies with Real-time Torque Feedback](https://mlsanity.com/paper/2606.14218/) (2026)
- [EgoKit: Towards Unified Low-Cost Egocentric Data Collection with Heterogeneous Devices](https://mlsanity.com/paper/2605.16797/) (2026)
- [Whole-Body Mobile Manipulation using Offline Reinforcement Learning on Sub-optimal Controllers](https://mlsanity.com/paper/2604.12509/) (2026)
- [ModPack: Extensible Teleoperation Interface for Bimanual Mobile Manipulation](https://mlsanity.com/paper/2607.19479/) (2026)
- [MAVP: Map-Aware Visuomotor Policies for Mobile Manipulation](https://mlsanity.com/paper/2609.26378/) (2026)
- [EgoHumanoid-V2: Human-to-Humanoid Transfer of Coordinated Whole-Body Skills for Loco-Manipulation](https://mlsanity.com/paper/2609.37181/) (2026)
- [UniWAM Technical Report: Unified Mobile Manipulation via Mixed-Stream World-Action Modeling and Manipulation Anchor Pose Supervision](https://mlsanity.com/paper/2609.39388/) (2026)
- [VOMMI: Collecting and Leveraging Portable Demonstrations for Mobile Manipulation](https://mlsanity.com/paper/2610.08220/) (2026)
- [Workhorse: Learning Robust Whole-Body Humanoid Loco-Manipulation from Human Data](https://mlsanity.com/paper/2610.09117/) (2026)
- [I3L: Iterative Interactive Imitation Learning for Whole-Body Mobile Manipulation](https://mlsanity.com/paper/2610.17748/) (2026)
- [Human Demonstrations as Post-Training Data for Any Robot Body: Retargeting the Object, Not the Hand](https://mlsanity.com/paper/2610.20351/) (2026)

## Abstract (arXiv)

We present Whole-Body Mobile Manipulation Interface (HoMMI), a data collection and policy learning framework that learns whole-body mobile manipulation directly from robot-free human demonstrations. We augment UMI interfaces with egocentric sensing to capture the global context required for mobile manipulation, enabling portable, robot-free, and scalable data collection. However, naively incorporating egocentric sensing introduces a larger human-to-robot embodiment gap in both observation and action spaces, making policy transfer difficult. We explicitly bridge this gap with a cross-embodiment hand-eye policy design, including an embodiment agnostic visual representation; a relaxed head action representation; and a whole-body controller that realizes hand-eye trajectories through coordinated whole-body motion under robot-specific physical constraints. Together, these enable long-horizon mobile manipulation tasks requiring bimanual and whole-body coordination, navigation, and active perception. Results are best viewed on: https://hommi-robot.github.io