DynaWM: 動力学認識蒸留とワールドモデル・モーメンタムターゲットによる連続階段の滑らかな移動
DynaWM: Dynamics-Aware Distillation with World Model and Momentum Targets for Smooth Locomotion over Continuous Stairs
二足車輪ロボットの連続階段移動を可能にするため、ワールドモデルとモーメンタムターゲットを用いた動力学認識表現学習フレームワークDynaWMを提案した。
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？
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著者: Haidong Hou, Zhangguo Yu, Hengbo Qi, Jianlin Zhang
分類: cs.RO, cs.AI
原文アブストラクト
Recent advances in control have enabled bipedal-wheeled robots to traverse slopes and single-step obstacles, yet long staircase traversal remains challenging as current teacher-student frameworks suffer from weakened dynamics-aware representations and incomplete terrain geometry encoding. To bridge this gap, we propose DynaWM, a dynamics-aware representation learning framework. To enhance terrain encoding capability and enable transparent assessment, we introduce a world model as a regularizer to enforce forward-dynamics awareness, preserving comprehensive terrain geometry while facilitating hierarchical encoding visualization. To stabilize knowledge transfer, we employ a momentum target encoder to provide consistent distillation targets, preventing dimensional collapse from non-stationary teacher updates. Evaluation of the learned representations through Principal Component Analysis (PCA) visualization and quantitative metrics reveals that our encoder hierarchically captures terrain geometry with higher terrain encoding capability, leading to enhanced terrain adaptability and motion smoothness. Experimental results in simulation and real hardware demonstrate that our method achieves superior terrain adaptability and motion smoothness, enabling bipedal-wheeled robots to overcome diverse continuous stairs, as shown in Fig. 1.
