Rolling-WAM: World Action Models with Rolling Imagination
TL;DR - Rolling-WAM accelerates closed-loop robotic manipulation by spreading joint video-action denoising across successive replanning cycles instead of recomputing the full prediction horizon each time. It achieves competitive performance with a 4.5Ă— steady-state replanning speedup over standard joint World Action Models.
- Maintains a sliding window of video-action chunks at staggered noise levels.
- Fully denoises the imminent action chunk while progressively refining future chunks.
- Reuses evolving visual-action context as new camera observations advance the window.
- Evaluated on LIBERO, RoboTwin, and a real-world Unitree G1 humanoid.