EpicWorldModel: 潜在世界モデルによる探索駆動型プランニング
EpicWorldModel: Exploration-driven Planning with Latent World Models
部分観測環境で不確実性を考慮した確率的JEPAを学習し、予測エントロピーを探索信号としてCEMプランニングに組み込むことで、隠れた目標の発見と到達を両立させる手法を提案。
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著者: Bowen Feng, Julian Ost, May Mei, Anirudha Majumdar, Felix Heide
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
Latent world models based on Joint-Embedding Predictive Architecture (JEPA) are deterministic by design. While successful in fully observable scenarios, this paradigm breaks down when past observations and actions lead to multiple plausible future possibilities, e.g., due to occlusion. We introduce EpicWorldModel, a framework to train stochastic JEPAs for environments and tasks with inherent uncertainty under partially observability. We jointly train the EpicWorldModel predictor with its latent representation space to directly predict multiple potential future states using a flow-matching objective, when the goal-relevant scene content is absent from the conditioning history. We show that flow predictive variance, motivated by its relation to an upper bound on predictive entropy, serves as a useful exploration guidance for planning. By incorporating this uncertainty signal into Cross-Entropy Method (CEM)-based planning, our approach balances goal-reaching with exploration of uncertain regions where occluded goals are most likely to be located. We demonstrate the effectiveness of EpicWorldModel through a series of latent planning experiments with the best or on-par performance across tasks, showing up to 22% empirical improvement in success rate over LeWorldModel.