運動事前分布の再構築：複雑な四足歩行移動のための平地スキル適応
Motion Priors Reimagined: Adapting Flat-Terrain Skills for Complex Quadruped Mobility
平地での動物模倣で低レベル方策を事前学習し、その上に高レベル方策を階層的に学習させることで、四足ロボットが不整地や障害物環境でも自然な歩容を保ちながら移動できるようにした研究。
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著者: Zewei Zhang, Chenhao Li, Takahiro Miki, Marco Hutter
分類: cs.RO
原文アブストラクト
Reinforcement learning (RL)-based motion imitation methods trained on demonstration data can effectively learn natural and expressive motions with minimal reward engineering but often struggle to generalize to novel environments. We address this by proposing a hierarchical RL framework in which a low-level policy is first pre-trained to imitate animal motions on flat ground, thereby establishing motion priors. A subsequent high-level, goal-conditioned policy then builds on these priors, learning residual corrections that enable perceptive locomotion, local obstacle avoidance, and goal-directed navigation across diverse and rugged terrains. Simulation experiments illustrate the effectiveness of learned residuals in adapting to progressively challenging uneven terrains while still preserving the locomotion characteristics provided by the motion priors. Furthermore, our results demonstrate improvements in motion regularization over baseline models trained without motion priors under similar reward setups. Real-world experiments with an ANYmal-D quadruped robot confirm our policy's capability to generalize animal-like locomotion skills to complex terrains, demonstrating smooth and efficient locomotion and local navigation performance amidst challenging terrains with obstacles.
