一个全天候的功夫运动员机器人：高度动态、平衡挑战性的动作数据集和自主防跌跟踪
KungfuAthleteBot: Learning High-Dynamic Motion from Videos with Unified Robust Recovery
AI总结：
本文提出KungFuAthlete数据集，通过高动态武术动作训练视频，结合单策略学习实现运动跟踪与跌倒恢复，提升人形机器人在高动态环境中的自主性和鲁棒性。
摘要：
当前的人形运动跟踪系统可以执行例行和中等动态行为，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运动的极端案例，其特点是快速的质心位移、复杂的协调和突然的姿态转换。然而，针对此类高强度场景定制的数据集仍然稀缺。为解决这一差距，我们构建了KungFuAthlete，一个源自专业运动员日常训练视频的高动态武术动作数据集。该数据集包含地面和跳跃子集，涵盖了代表性复杂的运动模式。跳跃子集表现出明显高于常用数据集如LAFAN1、PHUMA和AMASS的关节、线性和角速度，表明显著增加的运动强度和复杂性。重要的是，即使是专业运动员在高度动态运动中也可能失败。同样，人形机器人在受到外部干扰或执行错误时也容易不稳定和跌倒。大多数先前工作假设运动执行始终在安全状态内，缺乏统一的策略来建模不安全状态并实现可靠的自主恢复。我们提出了一种新的训练范式，使单个策略能够联合学习高动态运动跟踪和跌倒恢复，将敏捷执行和稳定化整合到一个框架中。该框架将机器人的能力从纯粹的运动跟踪扩展到具有恢复能力的执行，促进在现实世界高动态场景中更加稳健和自主的人形性能。
英文摘要：
Learning highly dynamic motions from video provides a scalable alternative to expensive data collection for humanoid robots, yet it is challenged by height floating, missing kinematic and force information, and limited robustness. We present KungfuAthleteBot (KAB), a unified framework that systematically addresses these issues. We first build a high-dynamic motion dataset in collaboration with professional martial artists, alleviating data scarcity while improving motion quality. To eliminate height drift and global jitter during both aerial and landing phases, we introduce a physics-guided parabolic trajectory correction that enforces physical plausibility. To bridge the discrepancy between human and robot actuation in highly dynamic behaviors, we design a three-stage curriculum that progressively enables stable acquisition of such motions. In addition, we propose a direct, teacher-free training paradigm that jointly learns disturbance rejection and fall recovery within a single policy. To the best of our knowledge, KAB achieves the fastest recovery time reported for a unified policy ($\approx 0.7$s). Experiments on a real humanoid robot demonstrate successful learning of highly dynamic motions from video and strong robustness, including rapid recovery under severe disturbances.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
