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arXiv AI By Yinghua Zhou, Junjie Ye, Yiqi Zhao, Hao Dong, Celina Shiyu Wang, Ruohai Ge, Tingyi Yang, Basile Van Hoorick, Gaurav Sukhatme, Vitor Guizilini, Yue Wang

Rolling-WAM: World Action Models with Rolling Imagination

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Rolling-WAM is a new formulation for World Action Models that spreads the joint video-action denoising process across multiple replanning cycles. It keeps a sliding window of video-action chunks at different noise levels, fully denoising the immediate chunk for execution while partially refining future chunks. This approach reduces latency, improves closed-loop responsiveness, and achieves a 4.5× speedup in steady-state replanning compared to standard WAMs while maintaining competitive manipulation performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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