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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.24592 |
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| _version_ | 1866913159295533056 |
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| author | Feng, Yusen Wang, Xiang Yao, Heyuan Kang, Zixi Huo, Xinyu Yu, Boyang Qiu, Pengyun Zhao, Ruijie Chen, Baoquan Liu, Libin |
| author_facet | Feng, Yusen Wang, Xiang Yao, Heyuan Kang, Zixi Huo, Xinyu Yu, Boyang Qiu, Pengyun Zhao, Ruijie Chen, Baoquan Liu, Libin |
| contents | This paper presents MuGen, a data-driven framework for learning and deploying multi-skill locomotion on humanoid robots. MuGen enables a robot to perform expressive motions like humans under the guidance of example motion sequences. To achieve this, we employ vector-quantized autoencoders (VQ-VAEs) trained with model-based reinforcement learning, resulting in a generative representation of locomotion that captures key patterns of human motion from hours of heterogeneous human performance data. We employ a teacher-student learning framework and develop a new policy distillation strategy to enable a deployable student policy learning this efficient latent representation. This policy allows the robot to track and mimic unseen human motions and further enables the robot to reuse the learned latent space for other tasks. We demonstrate the effectiveness of our framework through a diverse set of motions and accurate execution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_24592 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MuGen: Multi-Skill Generative Locomotion Controller for Humanoid Robots Feng, Yusen Wang, Xiang Yao, Heyuan Kang, Zixi Huo, Xinyu Yu, Boyang Qiu, Pengyun Zhao, Ruijie Chen, Baoquan Liu, Libin Robotics This paper presents MuGen, a data-driven framework for learning and deploying multi-skill locomotion on humanoid robots. MuGen enables a robot to perform expressive motions like humans under the guidance of example motion sequences. To achieve this, we employ vector-quantized autoencoders (VQ-VAEs) trained with model-based reinforcement learning, resulting in a generative representation of locomotion that captures key patterns of human motion from hours of heterogeneous human performance data. We employ a teacher-student learning framework and develop a new policy distillation strategy to enable a deployable student policy learning this efficient latent representation. This policy allows the robot to track and mimic unseen human motions and further enables the robot to reuse the learned latent space for other tasks. We demonstrate the effectiveness of our framework through a diverse set of motions and accurate execution. |
| title | MuGen: Multi-Skill Generative Locomotion Controller for Humanoid Robots |
| topic | Robotics |
| url | https://arxiv.org/abs/2605.24592 |