DWMP：双対世界モデルによるヒューマノイドの障害物踏破
DWMP: Leveraging Dual World Models for Humanoid Obstacle Traversal
固有感覚と視覚の特性の違いに着目し、Koopmanベースの動力学世界モデルとRSSMベースの視覚世界モデルを組み合わせてヒューマノイドの障害物踏破を実現する手法を提案した。
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著者: Rongjun Jin, Jianming Ma, Yue Gao
分類: cs.RO
原文アブストラクト
Humanoid robots must traverse cluttered obstacle fields using onboard proprioceptive and visual observations, yet existing methods usually process multimodal observations without explicitly considering their different characteristics: proprioceptive observations are low-dimensional but governed by highly nonlinear robot dynamics, while egocentric visual observations are high-dimensional, noisy, and redundant. We propose DWMP (Dual World Model Policy), a framework that provides the actor with separate but complementary world-model representations for humanoid obstacle traversal. A Koopman-based dynamics world model lifts proprioceptive observations into a latent space where their temporal evolution is approximately linear, making the dynamics features easier for the actor to learn from. An RSSM-based visual world model compresses egocentric depth observations into compact stochastic states while preserving obstacle-related geometry. The student policy receives the fused latent representation for action generation, combining linearized proprioceptive dynamics with compressed visual perception. Experiments in simulation and on a Unitree G1 humanoid robot show that DWMP improves obstacle traversal performance over baselines and supports real-world deployment under randomized obstacle layouts.