ワールドアクションモデルによる再帰的生成リプレイを用いた継続的模倣学習
World Action Models Enable Continual Imitation Learning with Recurrent Generative Replays
ロボットの行動予測に加えて未来の視覚観測も生成できるワールドアクションモデルを用いて、過去のタスクの疑似リプレイ軌道を合成し、元のデモデータを保存せずに継続的模倣学習を行うフレームワークを提案した。実験により、破滅的忘却を最大50%削減し、実データを用いる手法に匹敵する性能を示した。
著者: Manish Kumar Govind, Dominick Reilly, Smit Patel, Hieu Le, Srijan Das
分類: cs.RO, cs.CV
原文アブストラクト
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.