用专家组合框架让机器人快速精准跟踪动作，训练时间仅45分钟
LooperMuscle: Fast and Stable Learning of Humanoid Whole-Body Tracking via Structured Mixture-of-Experts
- 采用语义分组的专家混合策略，动态分配任务
- 45分钟完成训练，追踪精度接近耗时6小时的PPO
- 适合需要快速迭代的机器人控制研究者
FastSAC类方法虽大幅缩短人形机器人运动训练时间，但在全身动作追踪任务中性能明显弱于PPO。为此提出LooperMuscle，一种由语义结构化的混合专家策略、专家感知的分布式评判器以及贡献导向的回放缓冲与延迟课程调度组成的联合专家学习框架。三者构成闭环：专家贡献指导数据路由，路由数据塑造价值学习，价值梯度反向优化专家专长。实验表明，该方法在运动追踪精度上显著优于原始FastSAC，且训练时间仅需约45分钟，远少于耗时约6小时的PPO，实现了高效与高精度的平衡。代码将公开于https://loopermuscle.github.io/。
原文摘要 · Abstract (English)
FastSAC-style methods significantly reduce humanoid motion training time but often suffer from notable performance degradation compared with PPO in whole-body tracking tasks. We target this speed-performance gap by introducing LooperMuscle, a composed expert policy learning framework that restores tracking quality while preserving high training efficiency. LooperMuscle combines a semantically structured mixture-of-experts actor, an expert-aware distributional critic, and contribution-routed replay with deferred curriculum scheduling. These three components form a closed training loop in which expert contributions guide data routing, routed data shape value learning, and value gradients in turn refine expert specialization. Empirically, our approach substantially outperforms vanilla FastSAC in motion tracking accuracy while requiring far less wall-clock time than PPO: where FastSAC trains in about 15 minutes but underperforms, and PPO achieves stronger results but requires about 6 hours, LooperMuscle recovers a substantial fraction of the remaining gap to PPO in roughly 45 minutes of simulation training, delivering practical efficiency for rapid policy iteration. The code will be released to benefit the research community at https://loopermuscle.github.io/.
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