World Action Models Enable Continual Imitation Learning with Recurrent Generative Replays
For continual robot learning, this work addresses catastrophic forgetting without storing original demonstrations, offering a practical solution for lifelong policy adaptation.
World Action Models (WAMs) generate future visual observations, enabling Recurrent Generative Replay (REGEN) for continual imitation learning. REGEN reduces catastrophic forgetting by up to 50% relative to sequential fine-tuning, approaching privileged experience replay performance.
Going beyond predicting robot actions, World Action Models (WAMs) can also generate future visual observations. We build on this generative capability to propose Recurrent Generative Replay (REGEN), a continual imitation learning framework that synthesizes pseudo-replay trajectories, enabling a robot policy to rehearse previously learned tasks without storing their original human demonstrations. During continual adaptation, REGEN recursively queries the WAM to synthesize pseudo-replay trajectories conditioned only on prior task instructions and current-task observations. Experiments in both simulation and real-world manipulation settings show that REGEN reduces catastrophic forgetting by up to $50\%$ relative to sequential fine-tuning, while approaching the performance of privileged experience replay methods that require access to real replay data. Finally, we analyze the factors limiting generated replay, identifying long-horizon visual degradation and action-observation inconsistency as the primary bottlenecks. Our results establish WAMs as a promising foundation for continual robot learning without stored demonstrations.