Rolling-WAM: World Action Models with Rolling Imagination
Rolling-WAM speeds robotic replanning 4.5x by spreading joint denoising across successive cycles.
Rolling-WAM is a world-action-model formulation for robotic manipulation that spreads joint video-action denoising across replanning cycles instead of restarting the full horizon each time. A sliding window holds chunks at staggered noise levels, fully denoising the next action while only partially refining farther-future chunks as new camera observations arrive. On LIBERO, RoboTwin, and a real Unitree G1 humanoid, it matches competitive manipulation performance and reports a 4.5x steady-state replanning speedup over standard joint WAMs.
- Rolling-WAM keeps video-action chunks at staggered noise levels in a sliding window.
- Each step fully denoises the imminent action and partially refines future chunks.
- Evaluated on LIBERO, RoboTwin, and a real Unitree G1 humanoid.
- Steady-state replanning is 4.5 times faster than standard joint world action models.
- Vendors
- Unitree
- Products
- Unitree G1
From the original article
1 min read at arxiv.orgWorld 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.
By Yinghua Zhou · arxiv.org
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.