記憶拡張検索を用いたイベント中心の世界モデルによる身体化意思決定
Event-Centric World Modeling with Memory-Augmented Retrieval for Embodied Decision-Making
環境を構造化されたセマンティックイベントとして表現し、過去の経験を検索して行動を生成するERAフレームワークを提案。UAVナビゲーションでリアルタイムかつ適応的な意思決定を実証した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Zhaowen Fan, Rongchao Zhang, Yunxiang Han
分類: cs.LG, cs.IR, cs.RO
原文アブストラクト
Autonomous agents operating in dynamic environments increasingly demand decision-making systems that are both efficient and interpretable. Hence we propose the Event-Retrieve-Action (ERA) framework, an alternative formulation for embodied decision-making that bridges the gap between black-box imitation and interpretable memory retrieval while enabling online refinement without retraining. The environment is represented as structured semantic events encoded into an interpretable latent representation, and decisions are generated by retrieving relevant prior experiences from a knowledge bank of event-action pairs. Final actions are produced through weighted aggregation of retrieved maneuvers, enabling transparent and physically consistent decision-making. Experiments in UAV navigation demonstrate real-time performance and adaptive behavior in dynamic environments as a representative embodied decision-making application scenario.
