UniTracker: ヒューマノイドロボットのための汎用全身運動トラッカー学習
UniTracker: Learning Universal Whole-Body Motion Tracker for Humanoid Robots
3段階の学習フレームワークで、多様な人間の動きをヒューマノイドロボットが追従できる汎用全身運動トラッカーを実現し、実機でも高い性能を示した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
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著者: Kangning Yin, Weishuai Zeng, Ke Fan, Minyue Dai, Zirui Wang, Qiang Zhang, Zheng Tian, Jingbo Wang, Jiangmiao Pang, Weinan Zhang
分類: cs.RO
原文アブストラクト
Achieving expressive and generalizable whole-body motion control is essential for deploying humanoid robots in real-world environments. In this work, we propose UniTracker, a three-stage training framework that enables robust and scalable motion tracking across a wide range of human behaviors. In the first stage, we train a teacher policy with privileged observations to generate high-quality actions. In the second stage, we introduce a Conditional Variational Autoencoder (CVAE) to model a universal student policy that can be deployed directly on real hardware. The CVAE structure allows the policy to learn a global latent representation of motion, enhancing generalization to unseen behaviors and addressing the limitations of standard MLP-based policies under partial observations. Unlike pure MLPs that suffer from drift in global attributes like orientation, our CVAE-student policy incorporates global intent during training by aligning a partial-observation prior to the full-observation encoder. In the third stage, we introduce a fast adaptation module that fine-tunes the universal policy on harder motion sequences that are difficult to track directly. This adaptation can be performed both for single sequences and in batch mode, further showcasing the flexibility and scalability of our approach. We evaluate UniTracker in both simulation and real-world settings using a Unitree G1 humanoid, demonstrating strong performance in motion diversity, tracking accuracy, and deployment robustness.
