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Verti-WM: A Physics-Aided Exteroceptive World Model for Off-Road Reinforcement Learning

arXiv · AI, language, vision and robotics · article · Sep 19, 2026 · UTC

Reinforcement learning for off-road navigation requires extensive vehicle-terrain interaction data, which are costly to collect in high-fidelity simulation. World models offer a promising alternative by replacing simulator roll-outs during policy optimization. However, an off-road world model must condition state transitions on exteroceptive terrain information, which proprioception alone does not provide. This challenge is further amplified by the need to model both rigid and deformable terrain, where data-driven and physics-based approaches offer complementary strengths. We propose Verti-WM,

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