通过闭环迭代生成动作数据，突破人形机器人控制的难度天花板
Iterative Closed-Loop Motion Synthesis for Scaling the Capabilities of Humanoid Control
- 构建闭环框架自动生成高质动作数据，涵盖武术、舞蹈等多种语义
- 仅用1/10的AMASS数据量，测试集失败率降低45%
- 适合需要大规模动作泛化能力的机器人控制研究者
基于物理的人形机器人控制依赖于具有多样化分布的动作数据集，但现有数据集的固定难度分布限制了训练策略的性能上限。同时，高质量数据获取依赖昂贵的专业动捕系统，难以实现大规模扩展。为此，我们提出一种闭环自动化动作数据生成与迭代框架，可生成包含武术、舞蹈、格斗、运动、体操等丰富动作语义的高质量数据。该框架通过物理指标与客观评估实现策略与数据的难度迭代，使训练出的追踪器突破原有难度限制。在PHC单原语追踪器上，仅使用约1/10的AMASS数据量，测试集（2201段）平均失败率相比基线降低45%。我们还进行了全面的消融与对比实验，验证了框架的合理性与优势。
原文摘要 · Abstract (English)
Physics-based humanoid control relies on training with motion datasets that have diverse data distributions. However, the fixed difficulty distribution of datasets limits the performance ceiling of the trained control policies. Additionally, the method of acquiring high-quality data through professional motion capture systems is constrained by costs, making it difficult to achieve large-scale scalability. To address these issues, we propose a closed-loop automated motion data generation and iterative framework. It can generate high-quality motion data with rich action semantics, including martial arts, dance, combat, sports, gymnastics, and more. Furthermore, our framework enables difficulty iteration of policies and data through physical metrics and objective evaluations, allowing the trained tracker to break through its original difficulty limits. On the PHC single-primitive tracker, using only approximately 1/10 of the AMASS dataset size, the average failure rate on the test set (2201 clips) is reduced by 45% compared to the baseline. Finally, we conduct comprehensive ablation and comparative experiments to highlight the rationality and advantages of our framework.
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