发表机构
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MimicX提出策略在环监督细化框架，利用执行反馈定位失败并调整跟踪目标与重置课程，在视频驱动人形运动跟踪中降低25.7%误差并提升255.6%执行视界。
AI 中文摘要
人类视频为人形机器人学习提供了丰富的运动目标，然而在物理执行下，视觉上合理的参考仍可能产生持续失败。这些失败揭示了训练监督需要调整之处。我们提出了MimicX，一个策略在环框架，利用执行反馈来细化视频驱动的人形运动跟踪。从重建和重定向的运动出发，MimicX定位困难过渡和受影响的身体区域，然后联合调整跟踪目标和重置课程以支持策略延续。重复的 rollout 验证在跟踪保护约束下选择执行优先的改进。在四个核心视频任务中，相对于固定参考基线，MimicX持续提高了跟踪精度和鲁棒执行视界。任务平均结果显示，身体跟踪误差降低了25.7%，执行视界增加了255.6%。额外的视频、运动参考和碰撞场景研究评估了方法在核心任务之外的表现，而MimicX-HLoop通过异构执行加速反馈。总体而言，MimicX将策略失败转化为可操作的监督，用于决定细化什么以及保留哪些细化。
英文摘要
Human videos provide rich motion targets for humanoid learning, yet visually plausible references can still produce persistent failures under physics-based execution. These failures reveal where training supervision should change. We present MimicX, a policy-in-the-loop framework that uses execution feedback to refine video-driven humanoid motion tracking. Starting from reconstructed and retargeted motion, MimicX localizes difficult transitions and affected body regions, then jointly adapts tracking objectives and the reset curriculum for policy continuation. Repeated rollout verification selects execution-priority improvements subject to tracking guards. Across four core video tasks, MimicX consistently improves tracking accuracy and Robust Execution Horizon relative to the Fixed Reference baseline. Task-averaged results show a 25.7% reduction in body-tracking error and a 255.6% increase in execution horizon. Additional video, motion-reference, and collision-scene studies evaluate the method beyond the core tasks, while MimicX-HLoop accelerates feedback through heterogeneous execution. Overall, MimicX turns policy failure into actionable supervision for deciding what to refine and which refinement to retain.
Comments33 pages, 26 figures, 19 tables, including references and appendices. Project website: https://nebulis-lab.com/MimicX
