发表机构
School of Automation, Beijing Institute of Technology; Humanoid Robotics (Shanghai) Co., Ltd.; School of Mechanical Engineering, Shandong University（北京理工大学自动化学院; 人形机器人（上海）有限公司; 山东大学机械工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对类人机器人控制器通用与专业能力权衡问题，提出两阶段持续学习框架极端-RGMT。先学通用运动跟踪基础策略，再用特定机制约束策略漂移，结合两种采样方法解决数据问题，实现最优通用全身运动跟踪性能，推动控制器向人类专家水平发展。
AI 中文摘要
人类能够在保持可靠日常运动能力的同时逐步获得高动态运动技能。相比之下，现有的类人机器人控制器在通用能力和专业能力之间面临权衡。我们引入了极端-RGMT，这是一个用于鲁棒通用类人机器人控制的两阶段持续学习框架。该方法首先从多样的多源运动数据中学习通用运动跟踪基础策略，然后采用非对称技能获取和能力巩固机制来约束已掌握动作上的策略漂移，同时强调困难的动态部分。为解决高动态运动的稀缺性、高失败率以及由此导致的信息样本短缺问题，极端-RGMT将难度感知采样与优势优先轨迹重采样相结合以强调关键部分。实验表明，极端-RGMT实现了当前最优的通用全身运动跟踪性能，包括显著提高具有挑战性的高动态运动的完成率。由此产生的控制器能够在固定参考和在线惯性运动捕捉输入下直接执行各种未见的高动态运动，推动通用全身运动跟踪控制器向人类专家水平的高动态运动能力发展。
英文摘要
Humans can progressively acquire highly dynamic motor skills while preserving reliable everyday motor abilities. In contrast, existing humanoid controllers face a trade-off between generalist and specialist capabilities: generalist motion tracking policies struggle to reliably execute rare highly dynamic motions, whereas specialist training can degrade previously acquired behaviors. We introduce Extreme-RGMT, a two-stage continual learning framework for robust generalist humanoid control. The method first learns a generalist motion-tracking base policy from diverse multi-source motion data, then employs an asymmetric skill acquisition and capability consolidation mechanism to constrain policy drift on mastered motions while emphasizing difficult dynamic segments. To address the scarcity of highly dynamic motions, their high failure rates, and the resulting shortage of informative samples, Extreme-RGMT combines difficulty-aware sampling with advantage-prioritized trajectory resampling to emphasize critical segments. Experiments show that Extreme-RGMT achieves state-of-the-art generalist whole-body motion-tracking performance, including substantially improved completion of challenging highly dynamic motions. The resulting controller directly executes diverse unseen highly dynamic motions under fixed references and online inertial motion-capture inputs, advancing generalist whole-body motion-tracking controllers toward highly dynamic motor capabilities at the human-expert level.
