外观
论文arXiv 2609.257541 个月前
PLAT：通过特权潜变量转移学习实现人形控制的稀疏定时关键帧运动跟踪
针对稠密逐帧参考限制人形高层控制的问题，PLAT用特权潜变量转移学习稀疏定时关键帧跟踪，并在仿真和Unitree G1上验证。
宇树 G1原文链接：PLAT: Sparse Timed Keyframe Motion Tracking for Humanoid Control via Privileged Latent Transition Learning（arXiv 2609.25754 · 发布日期：2026-09-22）
PLAT: Sparse Timed Keyframe Motion Tracking for Humanoid Control via Privileged Latent Transition Learning
针对稠密逐帧参考限制人形高层控制的问题，PLAT用特权潜变量转移学习稀疏定时关键帧跟踪，并在仿真和Unitree G1上验证。
核心要点
- 现有人形运动跟踪依赖稠密逐帧参考，限制其用于规划和交互式运动生成的高层控制。
- PLAT训练时用稠密目标序列作特权监督，部署时仅需稀疏未来关键帧及到达时间。
- 框架结合预训练稠密跟踪专家、DAgger式模仿学习和潜变量残差强化学习。
- 仿真显示其在不同规划时域下保持准确稳定，长时域命令下表现尤强，并成功部署到Unitree G1。
验证范围与局限：验证范围为仿真和Unitree G1人形机器人上的稀疏定时关键帧运动控制。
英文摘要 (Abstract)
Humanoid motion tracking policies rely on dense frame-by-frame references, limiting their use as high-level motion controllers for planning and interactive motion generation. We study \emph{Sparse Timed Keyframe Motion Tracking}, where a policy receives only sparse future keyframes and their desired arrival times, and must execute stable whole-body motions that reach successive goals. We propose \textbf{PLAT}, a three-stage sparse timed keyframe motion tracking policy learning framework with \textbf{P}rivileged \textbf{LA}tent \textbf{T}ransition learning. PLAT bridges dense motion tracking and sparse goal-conditioned control by exploiting dense goal sequences as privileged supervision during training while requiring only sparse timed keyframe commands at deployment. A pretrained dense tracking expert first provides robust motion priors. A privileged latent prior is then learned through DAgger-style imitation, followed by latent residual reinforcement learning that refines latent transitions instead of directly optimizing actions. Extensive simulation experiments demonstrate that PLAT maintains accurate and stable sparse timed keyframe tracking across varying planning horizons, with particularly strong performance under long-horizon commands. Successful deployment on a Unitree G1 humanoid robot further demonstrates the effectiveness and practicality of PLAT for sparse humanoid motion control.
作者：Zepeng Wang, Jiangxing Wang, Chao Ma, Xiaochuan Shi, Zongqing Lu
来源
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