RecallWAM: Unifying Recall and Foresight for Object Navigation

Object navigation requires agents to find specified objects in unfamiliar environments through exploration, using past observations to avoid redundant search. World-action models (WAMs) exploit such visual experience by coupling action learning with future-observation prediction (Foresight), providing dense supervision for navigation. However, identical scene appearances can correspond to different exploration states and call for different search decisions. We therefore extend world-action learning to recover history-dependent exploration state alongside predicting future observations, making past coverage explicit for navigation decisions. To this end, we introduce RecallWAM, which augments foresight-based world-action learning with a recall capability that recovers exploration state from historical observations to guide navigation decisions. To teach the policy how recalled coverage should affect its actions, we construct balanced exploration trajectories that trade off shortest-path guidance with randomized branch exploration, providing history-dependent search experience while limiting unnecessary detours. Experiments on the benchmarks HM3D, MP3D, and OVON demonstrate state-of-the-art performance. Controlled ablations show that joint Recall and Foresight learning yields gains exceeding the sum of their individual gains.