Current humanoid motion tracking systems can execute routine and moderately dynamic behaviors, yet significant gaps remain near hardware performance limits and algorithmic robustness boundaries. Martial arts represent an extreme case of highly dynamic human motion, characterized by rapid center-of-mass shifts, complex coordination, and abrupt posture transitions. However, datasets tailored to such high-intensity scenarios remain scarce. To address this gap, we construct KungFuAthlete, a high-dynamic martial arts motion dataset derived from professional athletes' daily training videos. The dataset includes ground and jump subsets covering representative complex motion patterns. The jump subset exhibits substantially higher joint, linear, and angular velocities compared to commonly used datasets such as LAFAN1, PHUMA, and AMASS, indicating significantly increased motion intensity and complexity. Importantly, even professional athletes may fail during highly dynamic movements. Similarly, humanoid robots are prone to instability and falls under external disturbances or execution errors. Most prior work assumes motion execution remains within safe states and lacks a unified strategy for modeling unsafe states and enabling reliable autonomous recovery. We propose a novel training paradigm that enables a single policy to jointly learn high-dynamic motion tracking and fall recovery, unifying agile execution and stabilization within one framework. This framework expands robotic capability from pure motion tracking to recovery-enabled execution, promoting more robust and autonomous humanoid performance in real-world high-dynamic scenarios.

翻译：当前的人形机器人运动追踪系统能够执行常规及中等动态行为，但在硬件性能极限与算法鲁棒性边界附近仍存在显著差距。武术作为高动态人体运动的极端案例，具有重心快速转移、复杂协调性和姿态突变等特征。然而，专门针对此类高强度场景的数据集仍然稀缺。为填补这一空白，我们构建了KungFuAthlete——一个源自职业运动员日常训练视频的高动态武术运动数据集。该数据集包含地面与跳跃两个子集，涵盖具有代表性的复杂运动模式。与LAFAN1、PHUMA、AMASS等常用数据集相比，跳跃子集展现出显著更高的关节速度、线速度与角速度，表明其运动强度与复杂度大幅提升。值得注意的是，即使是职业运动员在执行高动态动作时也可能失败。类似地，人形机器人在外部干扰或执行误差下容易失稳跌倒。现有研究大多假设运动执行始终处于安全状态，缺乏对非安全状态建模及实现可靠自主恢复的统一策略。我们提出一种新型训练范式，使单一策略能够同时学习高动态运动追踪与跌倒恢复，将敏捷执行与稳定控制统一于同一框架。该框架将机器人能力从纯运动追踪扩展至具备恢复功能的执行，从而在现实世界的高动态场景中实现更鲁棒、更自主的人形机器人性能。