GuidedWAM: Training-Free Energy Guidance for World Action Models in Autonomous Driving
Overview
This paper introduces GuidedWAM, a model-agnostic framework that enables injecting driving objectives into frozen World Action Models (WAMs) without retraining. By constructing objectives as differentiable energies on intermediate action estimates and applying sampling-time guidance scheduling, normalization, and velocity-bounded correction, the method improves performance across three WAMs on the NAVSIM v2 hard-split by 1.8 to 3.6 EPDMS while adding less than 10% to sampling time per step.
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- arXiv Robotics — research abstractsSignalGuidedWAM: Training-Free Energy Guidance for World Action Models in Autonomous Driving
This paper introduces GuidedWAM, a model-agnostic framework that enables injecting driving objectives into frozen World Action Models (WAMs) without retraining. By constructing objectives as differentiable energies on intermediate action estimates and applying sampling-time guidance scheduling, normalization, and velocity-bounded correction, the method improves performance across three WAMs on the NAVSIM v2 hard-split by 1.8 to 3.6 EPDMS while adding less than 10% to sampling time per step.
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