用频域先验让机器人跑得更快更稳，6米/秒突破人类级速度
SPRINT: Efficient Spectral Priors for Humanoid Athletic Sprints
- 通过频域分析五段参考动作，构建自适应频率的运动先验
- 在单位兔G1机器人上实现零样本迁移，最高速度达6米/秒
- 适合追求高速稳定跑步的机器人研发人员参考
人类型机器人冲刺面临运动参考数据稀缺和现有框架难以维持高速稳定性的问题。为此，本文提出SPRINT框架，利用高效、频率自适应的频域先验。通过一个包含五个离散运动序列的参考库，该方法在频域中刻画人类步态的基本周期性，生成覆盖广泛速度范围的运动学可行关节轨迹，并成功外推至超过参考分布的速度区间。基于预训练先验，SPRINT策略在Unitree G1平台上实现零样本模拟到现实的迁移，在实地实验中达到峰值冲刺速度6米/秒，同时保持自然步态转换与生物仿生性。本工作确立了频率自适应频域先验在人类型机器人冲刺中的高数据效率基础。
原文摘要 · Abstract (English)
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/.
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