PLAT：通过特权潜在转移学习实现稀疏定时关键帧运动跟踪的人形控制
PLAT: Sparse Timed Keyframe Motion Tracking for Humanoid Control via Privileged Latent Transition Learning
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- Wuhan University（武汉大学）
- BeingBeyond
- Peking University（北京大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
针对人形运动跟踪依赖密集参考的问题，提出PLAT框架，通过特权潜在转移学习实现稀疏定时关键帧跟踪，在模拟和真实机器人上验证了其准确性与稳定性。
中文摘要 AI 辅助
人形运动跟踪策略依赖于密集的逐帧参考，这限制了它们作为高层运动控制器用于规划和交互式运动生成的用途。我们研究稀疏定时关键帧运动跟踪，其中策略仅接收稀疏的未来关键帧及其期望到达时间，并且必须执行稳定的全身运动以达到连续目标。我们提出PLAT，一个三阶段的稀疏定时关键帧运动跟踪策略学习框架，采用特权潜在转移学习。PLAT通过在训练期间利用密集目标序列作为特权监督，同时在部署时仅需要稀疏的定时关键帧命令，从而弥合了密集运动跟踪与稀疏目标条件控制之间的差距。一个预训练的密集跟踪专家首先提供稳健的运动先验。然后通过DAgger风格的模仿学习特权潜在先验，随后进行潜在残差强化学习，该学习优化潜在转移而非直接优化动作。广泛的模拟实验表明，PLAT在不同规划范围内保持准确且稳定的稀疏定时关键帧跟踪，在长时域命令下表现尤为强劲。在Unitree G1人形机器人上的成功部署进一步证明了PLAT用于稀疏人形运动控制的有效性和实用性。
英文摘要
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.
