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arXiv · 2609.30247

Rolling-WAM: World Action Models with Rolling Imagination

Yinghua Zhou·Junjie Ye·Yiqi Zhao·Hao Dong·Celina Shiyu Wang·Ruohai Ge·Tingyi Yang·Basile Van Hoorick·Gaurav Sukhatme·Vitor Guizilini·Yue Wang
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Abstract

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 future chunks continue their denoising process. This distributes the computational cost over time while carrying an evolving visual-action context across chunk boundaries. Evaluations on LIBERO, RoboTwin, and a real-world Unitree G1 humanoid show that Rolling-WAM achieves competitive manipulation performance. By removing the need to denoise the entire prediction horizon from scratch, it delivers a 4.5x steady-state replanning speedup over standard joint WAMs.

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Yinghua Zhou, Junjie Ye, Yiqi Zhao, Hao Dong, Celina Shiyu Wang, Ruohai Ge, Tingyi Yang, Basile Van Hoorick, Gaurav Sukhatme, Vitor Guizilini, Yue Wang. 2026-09-24. Rolling-WAM: World Action Models with Rolling Imagination. https://arxiv.org/abs/2609.30247

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