Rolling-WAM: World Action Models with Rolling Imagination
World Action Models (WAMs) couple action generation with future visual prediction for robotic manipulation. However, completing the joint video-action denoising process at each replanning cycle incurs substantial latency, delaying action updates and limiting closed-loop responsiveness. We present Rolling-WAM, a formulation that distributes joint denoising across successive replanning cycles. Our method maintains a sliding window of video-action chunks at staggered noise levels. At each step, a rolling noise schedule fully denoises the imminent action chunk for execution, while partially refining farther-future chunks. As the window advances with new camera observations, the retained futur...
| Authors | Yinghua Zhou, Junjie Ye, Yiqi Zhao et al. |
|---|---|
| Primary category | cs.RO |
| Published | 24 September 2026 |
| Last updated at source | 5 October 2026 |
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