# HumanoidExo: Scalable Whole-Body Humanoid Manipulation via Wearable Exoskeleton

arXiv: https://arxiv.org/abs/2510.03022 · HTML: https://mlsanity.com/paper/2510.03022/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2025.

## Built on by (most cited first, 4 of 4)

- [AoE: Always-on Egocentric Human Video Collection for Embodied AI](https://mlsanity.com/paper/2602.23893/) (2026)
- [EgoHumanoid-V2: Human-to-Humanoid Transfer of Coordinated Whole-Body Skills for Loco-Manipulation](https://mlsanity.com/paper/2609.37181/) (2026)
- [Workhorse: Learning Robust Whole-Body Humanoid Loco-Manipulation from Human Data](https://mlsanity.com/paper/2610.09117/) (2026)
- [EgoExplorer: A Vision-Motion-Touch Observatory for Human Loco-Manipulation In Situ](https://mlsanity.com/paper/2610.18872/) (2026)

## Abstract (arXiv)

A significant bottleneck in humanoid policy learning is the acquisition of large-scale, diverse datasets, as collecting reliable real-world data remains both difficult and cost-prohibitive. To address this limitation, we introduce HumanoidExo, a novel system that transfers human motion to whole-body humanoid data. HumanoidExo offers a high-efficiency solution that minimizes the embodiment gap between the human demonstrator and the robot, thereby tackling the scarcity of whole-body humanoid data. By facilitating the collection of more voluminous and diverse datasets, our approach significantly enhances the performance of humanoid robots in dynamic, real-world scenarios. We evaluated our method across three challenging real-world tasks: table-top manipulation, manipulation integrated with stand-squat motions, and whole-body manipulation. Our results empirically demonstrate that HumanoidExo is a crucial addition to real-robot data, as it enables the humanoid policy to generalize to novel environments, learn complex whole-body control from only five real-robot demonstrations, and even acquire new skills (i.e., walking) solely from HumanoidExo data.