Distributing prediction computation across replanning cycles via a rolling noise schedule achieves 4.5x speedup in robot control latency without sacrificing task performance.
Rolling-WAM speeds up robot control by spreading the computation of predicting future actions and images across multiple planning cycles instead of doing it all at once. Instead of fully planning the entire future from scratch each time, it maintains a sliding window of partially-computed predictions at different stages, letting them gradually refine as new camera data arrives.