[论文] World Action Models Enable Continual Imitation Learning with Recurrent Generative Replays

论文概要

研究领域: CV
作者: Manish Kumar Govind, Dominick Reilly, Smit Patel
发布时间: 2026-06-27
arXiv: 2606.27374

中文摘要

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 robo...

原文摘要

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 trajectori...

自动采集于 2026-06-27

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