RM-WAM: Reliable Memory-Augmented World Action Models for Long-Horizon Robot Manipulation
From the abstract
World Action Models (WAMs) improve robotic manipulation by jointly predicting future visual states and actions, yet most existing methods make decisions primarily from the current observation. This limits their reliability in long-horizon tasks, where visually similar states may correspond to different interaction histories and task stages.
From the abstract. Our summary is in progress.