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arXiv · 2610.20157

Atomic-WAM: Execution-Grounded Atomic-Motion Reasoning for World Action Models

Shijun Shi·Zhe Liu·Zhi Hou·Yinan Chen·Tao Zhang·Kai Hu·Yong Liu·Jiangning Zhang
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Abstract

World Action Models (WAMs) typically adapt video foundation models to robotic control through joint video and action prediction. However, task-level instructions may leave the next physical interaction underspecified, requiring semantic grounding and state-dependent motion selection. To this end, we introduce Atomic-WAM, an understand-then-execute framework that connects task reasoning to action generation through atomic-motion descriptions. Specifically, a vision-language reasoner describes the next local motion from the task instruction and visual context, guiding a WAM to predict future video and an action chunk. Furthermore, an automatic annotation pipeline curates temporally aligned, contact-enriched supervision from multi-view DROID demonstrations. To make atomic-motion predictions executable by the WAM, we post-train the reasoner with Execution-Grounded GRPO (EG-GRPO). With the WAM fixed, EG-GRPO adapts the reasoner through rewards for motion appropriateness and execution success. Finally, Atomic-WAM achieves 39.6% task success under default instructions and 31.6% under vague instructions in a real-to-sim evaluation on RoboLab. Project page: https://ssj9596.github.io/Atomic-WAM-project

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Shijun Shi, Zhe Liu, Zhi Hou, Yinan Chen, Tao Zhang, Kai Hu, Yong Liu, Jiangning Zhang. 2026-10-14. Atomic-WAM: Execution-Grounded Atomic-Motion Reasoning for World Action Models. https://arxiv.org/abs/2610.20157

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