构建武术高动态动作数据集，让机器人学会追踪与自动防摔。
A Kung Fu Athlete Bot That Can Do It All Day: Highly Dynamic, Balance-Challenging Motion Dataset and Autonomous Fall-Resilient Tracking
- 用专业武者训练视频构建高动态动作数据集
- 跳跃动作速度远超LAFAN1等常用数据集
- 单一策略同时实现精准追踪与自主防摔
当前的人形运动追踪系统可执行常规及中等动态行为，但在接近硬件性能极限和算法鲁棒性边界时仍存在显著差距。武术代表了高度动态人类动作的极端情况，具有快速质心移动、复杂协调和突然姿态转换等特点。然而，针对此类高强度场景的数据集仍然稀缺。为此，我们构建了基于专业运动员日常训练视频的高动态武术动作数据集KungFuAthlete，包含地面动作与跳跃动作子集，涵盖典型复杂运动模式。跳跃子集的关节、线性和角速度显著高于LAFAN1、PHUMA和AMASS等常用数据集，表明其运动强度和复杂性大幅提升。值得注意的是，即使专业运动员在高度动态动作中也可能失衡。同样，人形机器人在外部干扰或执行误差下也易失稳跌倒。以往研究多假设动作执行始终处于安全状态，缺乏对非安全状态的统一建模及可靠自主恢复策略。我们提出一种新型训练范式，使单一策略能够联合学习高动态运动追踪与跌倒恢复，将敏捷执行与稳定化统一于一个框架内。该框架将机器人能力从单纯运动追踪扩展为具备恢复功能的执行，推动真实高动态场景下人形机器人更鲁棒、更自主的表现。
原文摘要 · Abstract (English)
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.
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