SPRINT: Efficient Spectral Priors for Humanoid Athletic Sprints
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866911725005045760 |
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| author | Wei, Yantong Huang, Kaihong Pan, Hainan Luo, Jiawei Zhou, Jiawei Mai, Ziyan Zeng, Zhiwen Wang, Yaonan Lu, Huimin |
| author_facet | Wei, Yantong Huang, Kaihong Pan, Hainan Luo, Jiawei Zhou, Jiawei Mai, Ziyan Zeng, Zhiwen Wang, Yaonan Lu, Huimin |
| contents | The pursuit of humanoid athletic sprints is hindered by a scarcity of humanoid-viable kinematic reference data and the inability of existing frameworks to maintain stability during sprints. To overcome these limitations, we introduce SPRINT, a novel framework driven by efficient, frequency-adaptive spectral priors. By characterizing the fundamental periodicity of human locomotion in the frequency domain using a reference library of five discrete motion sequences, these priors generate kinematically feasible joint trajectories across a broad velocity spectrum, successfully extrapolating to speeds that exceed the reference distribution. Guided by these pretrained priors, the SPRINT policy achieves zero-shot sim-to-real transfer in field experiments on the Unitree G1 platform, reaching a peak sprinting velocity of 6 m/s and demonstrating seamless gait transitions while preserving biomimetic naturalness. Ultimately, this work establishes frequency-adaptive spectral priors as a highly data-efficient foundation for humanoid athletic sprints. The project page is available at https://anonymous.4open.science/w/SPRINT-138A/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_28549 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SPRINT: Efficient Spectral Priors for Humanoid Athletic Sprints Wei, Yantong Huang, Kaihong Pan, Hainan Luo, Jiawei Zhou, Jiawei Mai, Ziyan Zeng, Zhiwen Wang, Yaonan Lu, Huimin Robotics Machine Learning The pursuit of humanoid athletic sprints is hindered by a scarcity of humanoid-viable kinematic reference data and the inability of existing frameworks to maintain stability during sprints. To overcome these limitations, we introduce SPRINT, a novel framework driven by efficient, frequency-adaptive spectral priors. By characterizing the fundamental periodicity of human locomotion in the frequency domain using a reference library of five discrete motion sequences, these priors generate kinematically feasible joint trajectories across a broad velocity spectrum, successfully extrapolating to speeds that exceed the reference distribution. Guided by these pretrained priors, the SPRINT policy achieves zero-shot sim-to-real transfer in field experiments on the Unitree G1 platform, reaching a peak sprinting velocity of 6 m/s and demonstrating seamless gait transitions while preserving biomimetic naturalness. Ultimately, this work establishes frequency-adaptive spectral priors as a highly data-efficient foundation for humanoid athletic sprints. The project page is available at https://anonymous.4open.science/w/SPRINT-138A/. |
| title | SPRINT: Efficient Spectral Priors for Humanoid Athletic Sprints |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2605.28549 |