Dreaming when Necessary: Advancing World Action Models with Adaptive Multi-Modal Reasoning

Abstract:World Action Models (WAMs) offer a promising approach to embodied intelligence, yet existing methods rely heavily on video prediction as action priors and lack adaptive multimodal reasoning, limiting their effectiveness on long-horizon, complex tasks. We observe that WAMs require different multimodal reasoning modes under different execution contexts: textual reasoning is essential during task transitions to guide high-level action prediction, while visual reasoning is critical during fine-grained manipulation for precise control. Motivated by this observation, we propose \textbf{AdaWAM}, a world action model with adaptive multimodal reasoning abilities. AdaWAM integrates a lightweight dynamic router that autonomously triggers textual or visual reasoning as needed during task execution. Experiments on both simulated and real-world embodied tasks show that AdaWAM substantially improves inference efficiency while outperforming state-of-the-art embodied policies. Codes and demos are available at: this https URL.

Robotics (cs.RO)

arXiv:2606.07089 [cs.RO]

(or arXiv:2606.07089v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2606.07089 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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