DWMP: Leveraging Dual World Models for Humanoid Obstacle Traversal

• URL: http://arxiv.org/abs/2609.12347v1
• Date: Fri, 11 Sep 2026 02:13:19 GMT
• Title: DWMP: Leveraging Dual World Models for Humanoid Obstacle Traversal
• Abstract summary: We propose a framework that provides the actor with separate but complementary world-model representations for humanoid obstacle traversal.<n>A Koopman-based dynamics world model lifts proprioceptive observations into a latent space where their temporal evolution is approximately linear.<n>An RSSM-based visual world model compresses egocentric depth observations into compact states while preserving obstacle-related geometry.<n> Experiments in simulation and on a Unitree G1 humanoid robot show that DWMP improves obstacle performance over baselines and supports real-world deployment.
• Score: 6.144157194223047
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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