HumanoidExo: Scalable Whole-Body Humanoid Manipulation via Wearable Exoskeleton
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908574585716736 |
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| author | Zhong, Rui Sun, Yizhe Wen, Junjie Li, Jinming Cheng, Chuang Dai, Wei Zeng, Zhiwen Lu, Huimin Zhu, Yichen Xu, Yi |
| author_facet | Zhong, Rui Sun, Yizhe Wen, Junjie Li, Jinming Cheng, Chuang Dai, Wei Zeng, Zhiwen Lu, Huimin Zhu, Yichen Xu, Yi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03022 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HumanoidExo: Scalable Whole-Body Humanoid Manipulation via Wearable Exoskeleton Zhong, Rui Sun, Yizhe Wen, Junjie Li, Jinming Cheng, Chuang Dai, Wei Zeng, Zhiwen Lu, Huimin Zhu, Yichen Xu, Yi Robotics 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. |
| title | HumanoidExo: Scalable Whole-Body Humanoid Manipulation via Wearable Exoskeleton |
| topic | Robotics |
| url | https://arxiv.org/abs/2510.03022 |