Problem
Research questions and friction points this paper is trying to address.
Sparse Timed Keyframe Motion Tracking
Humanoid Control
Privileged Latent Transition Learning
PLAT: Sparse Timed Keyframe Motion Tracking for Humanoid Control via Privileged Latent Transition Learning
🤖 AI Summary
本文提出PLAT框架，通过特权潜变量转换学习解决稀疏定时关键帧运动跟踪问题，使类人机器人能够基于稀疏关键帧执行稳定动作。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Sparse Timed Keyframe Motion Tracking
Privileged Latent Transition Learning
Dense Goal Sequences as Supervision
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