Action Images: End-to-End Policy Learning via Multiview Video Generation
World action models (WAMs) have emerged as a promising direction for robot policy learning, as they can leverage powerful video backbones to model the future states. However, existing approaches often rely on separate action modules, or use action representations that are not pixel-grounded, making it difficult to fully exploit the pretrained knowledge of video models and limiting transfer across viewpoints and environments. In this work, we present Action Images, a unified world action model that formulates policy learning as multiview video generation. Instead of encoding control as low-dimensional tokens, we translate 7-DoF robot actions into interpretable action images: multi-view act...
| Authors | Haoyu Zhen, Zixian Gao, Qiao Sun et al. |
|---|---|
| Primary category | cs.CV |
| Published | 7 April 2026 |
| Last updated at source | 15 April 2026 |
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