De-Diffusing WAMs: Action-Sufficient Direct Prediction for Robot Control
Cascade world-action models (WAMs) use predicted visual futures to condition robot actions, but inherit costly iterative inference from diffusion-pretrained experts. We ask what a single-step predicted future must preserve to support accurate single-step action prediction. In the evaluated WAM, resampling video noise has little effect on downstream actions, yet naively truncating both streams to one step substantially reduces task success. Video-space supervision only partially closes this gap, and a local sensitivity analysis shows that vision prediction errors have highly unequal consequences for action accuracy. These findings motivate DirectWAM, which converts pretrained video and act...
| Authors | Shaoyi Zheng, Zhongtao Jiang, Yue Peng et al. |
|---|---|
| Primary category | cs.RO |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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