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arXiv · 2510.03022

HumanoidExo: Scalable Whole-Body Humanoid Manipulation via Wearable Exoskeleton

Rui Zhong·Yizhe Sun·Junjie Wen·Jinming Li·Chuang Cheng·Wei Dai·Zhiwen Zeng·Huimin Lu·Yichen Zhu·Yi Xu
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Abstract

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.

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Rui Zhong, Yizhe Sun, Junjie Wen, Jinming Li, Chuang Cheng, Wei Dai, Zhiwen Zeng, Huimin Lu, Yichen Zhu, Yi Xu. 2025-10-03. HumanoidExo: Scalable Whole-Body Humanoid Manipulation via Wearable Exoskeleton. https://arxiv.org/abs/2510.03022

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