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GuidedWAM introduces a training-free method to inject new driving objectives into frozen World Action Models by treating them as differentiable energies during sampling. This approach improves performance on the NAVSIM v2 benchmark with minimal computational overhead, offering a practical solution for adapting generative world models without retraining.
GuidedWAM: Training-Free Energy Guidance for World Action Models in Autonomous Driving
arXiv:2611.07912v1 Announce Type: new Abstract: World action models (WAMs) for autonomous driving couple the driving policy and the visual world model in a single generative network whose objectives are fixed post-training. Conditioning on a new driving objective requires retraining. We study energy guidance as a general mechanism and present GuidedWAM, a model-agnostic framework for injecting driving objectives into frozen WAMs at sampling time. Our framework constructs each objective as differentiable energies on the sampler's intermediate clean-action estimate, which needs only forward access to the frozen model, and is stabilized by sampling-time guidance scheduling, per-energy normalization, and velocity-bounded correction. On the NAVSIM v2 hard-split, guidance improves all three frozen, publicly released WAMs by 1.8 to 3.6 EPDMS, averaged over three sampler seeds, and adds less than 10% to the sampling time per step. These results position energy guidance as a practical post-training control channel for WAMs.
Abstract:World action models (WAMs) for autonomous driving couple the driving policy and the visual world model in a single generative network whose objectives are fixed post-training. Conditioning on a new driving objective requires retraining. We study energy guidance as a general mechanism and present GuidedWAM, a model-agnostic framework for injecting driving objectives into frozen WAMs at sampling time. Our framework constructs each objective as differentiable energies on the sampler's intermediate clean-action estimate, which needs only forward access to the frozen model, and is stabilized by sampling-time guidance scheduling, per-energy normalization, and velocity-bounded correction. On the NAVSIM v2 hard-split, guidance improves all three frozen, publicly released WAMs by 1.8 to 3.6 EPDMS, averaged over three sampler seeds, and adds less than 10% to the sampling time per step. These results position energy guidance as a practical post-training control channel for WAMs.
Artificial Intelligence (cs.AI); Robotics (cs.RO)
arXiv:2611.07912 [cs.AI]
(or arXiv:2611.07912v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2611.07912 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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