World Action Models Enable Continual Imitation Learning with Recurrent Generative Replays

• URL: http://arxiv.org/abs/2606.27374v1
• Date: Thu, 25 Jun 2026 17:59:56 GMT
• Title: World Action Models Enable Continual Imitation Learning with Recurrent Generative Replays
• Abstract summary: World Action Models (WAMs) can generate future visual observations.<n>We build on this generative capability to propose Recurrent Generative Replay (REGEN)<n>REGEN synthesizes pseudo-replay trajectories conditioned only on prior task instructions and current-task observations.
• Score: 20.499876853160206
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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