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arXiv · 2606.27374

World Action Models Enable Continual Imitation Learning with Recurrent Generative Replays

Manish Kumar Govind·Dominick Reilly·Smit Patel·Hieu Le·Srijan Das
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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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Manish Kumar Govind, Dominick Reilly, Smit Patel, Hieu Le, Srijan Das. 2026-06-25. World Action Models Enable Continual Imitation Learning with Recurrent Generative Replays. https://arxiv.org/abs/2606.27374

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