CMoE: 人型ロボットの運動制御と地形適応のための対比混合エキスパート
CMoE: Contrastive Mixture of Experts for Motion Control and Terrain Adaptation of Humanoid Robots
混合エキスパートのゲーティングが地形ごとに均一化する問題を、対比学習で専門化を促すことで解決し、複雑な地形を歩行できる人型ロボット制御手法を提案した。
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著者: Shihao Ma, Hongjin Chen, Zijun Xu, Yi Zhao, Ke Wu, Ruichen Yang, Leyao Zou, Zhongxue Gan, Wenchao Ding
分類: cs.RO
原文アブストラクト
For effective deployment in real-world environments, humanoid robots must autonomously navigate a diverse range of complex terrains with abrupt transitions. While the Vanilla mixture of experts (MoE) framework is theoretically capable of modeling diverse terrain features, in practice, the gating network exhibits nearly uniform expert activations across different terrains, weakening the expert specialization and limiting the model's expressive power. To address this limitation, we introduce CMoE, a novel single-stage reinforcement learning framework that integrates contrastive learning to refine expert activation distributions. By imposing contrastive constraints, CMoE maximizes the consistency of expert activations within the same terrain while minimizing their similarity across different terrains, thereby encouraging experts to specialize in distinct terrain types. We validated our approach on the Unitree G1 humanoid robot through a series of challenging experiments. Results demonstrate that CMoE enables the robot to traverse continuous steps up to 20 cm high and gaps up to 80 cm wide, while achieving robust and natural gait across diverse mixed terrains, surpassing the limits of existing methods. To support further research and foster community development, we release our code publicly.
