外观
论文arXiv 2610.1484018 天前
挑战极限：在全尺寸人形机器人上学习高速冲刺与激进过弯
原文链接：Push the Limit: Learning High-Speed Sprinting and Aggressive Cornering on a Full-Scale Humanoid（arXiv 2610.14840 · 发布日期：2026-10-10）
Push the Limit: Learning High-Speed Sprinting and Aggressive Cornering on a Full-Scale Humanoid
提出学习框架，结合因果MHA、航向奖励和技能条件MAMP，在仿真与真机实现全尺寸双足人形高速冲刺和急转。
核心要点
- 高速步态中足部周期冲击会引发高频偏航振荡，干扰平坦MLP本体历史反馈。
- 方法采用因果多头注意力建模高频时序，并用航向方向奖励替代偏航率跟踪。
- 引入技能条件MAMP判别器，使风格奖励随指令技能变化，避免共享先验折中。
- 消融显示三项机制均必要，框架在仿真和真机展示稳定高速冲刺与急转。
验证范围与局限：验证范围为摘要所述的仿真与真实机器人高速冲刺和急转部署。
英文摘要 (Abstract)
We present a learning-based framework that enables a full-scale bipedal humanoid to sprint at over 7 m/s with motion-prior-guided gait and perform aggressive cornering at high speed with a pronounced trunk lean of up to $25^\circ$. Achieving this combination is challenging: above approximately 4 m/s, periodic foot impacts induce severe, high-frequency yaw-rate oscillations intrinsic to high-speed gaits. This high-frequency temporal structure corrupts the history of proprioceptive feedback that flat-MLP policies rely on, while rendering conventional yaw-rate tracking rewards counterproductive, as they fight against the robot's natural oscillatory dynamics. Moreover, a single style prior shared across skills cannot tell which skill is commanded and inherently compromises the low-speed skill fidelity. To overcome these limitations, we adopt a causal multi-head attention (MHA) module to explicitly model the high-frequency temporal feedback. In place of the oscillatory yaw rate, we design a heading-direction reward that supervises only the low-frequency velocity direction, allowing agile turning at high speed without suppressing natural gait oscillations. We further introduce skill-conditioned multi-adversarial-motion-prior (MAMP) discriminators that condition the style reward on the commanded skill. Ablation experiments confirm that each mechanism is essential: replacing structured temporal encoding with a flat-history MLP caps the top speed; reverting to yaw-rate tracking prevents high-speed turning; and a single shared discriminator makes in-place turning collapse into a quasi-static stance. Our framework bridges the gap between raw sprinting speed and reference-motion fidelity, demonstrating stable high-speed sprinting and aggressive cornering in both simulation and real-robot deployment.
作者：Huaxing Huang, Ruidong Li, Jinchao Han, Ziyang Dong, Wenhao Cui
来源
本页事实对应的公开页面，以原文为准。