CogWAM: イベント駆動型インターフェースによる意味認知と世界行動モデリングの整合
CogWAM: Aligning Semantic Cognition with World Action Modeling via Event-Driven Interfaces
タスク推論と世界行動学習の間に持続的な意味状態を介した明示的なインターフェースを設け、進捗条件付きクエリで未来予測と行動生成を整合させる認知誘導型世界行動モデルを提案。
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著者: Sen Wang, Liu Liu, Xinjiang Wang, Zequn Chen, Haoyi Jiang, Taojun Ding, Tingyang Xiao, Zhizhong Su, Jie Wang, Sanping Zhou
分類: cs.RO, cs.CV
原文アブストラクト
Robot policies increasingly incorporate semantic reasoning and future-world prediction, yet combining these capabilities does not guarantee that local predictions and actions remain aligned with task progress. We introduce CogWAM, a cognition-guided world-action model that establishes an explicit semantic interface between task reasoning and world-action learning through a persistent Semantic State, which stores completed task events and the active subtask. CogWAM updates this state only when observations indicate semantic transitions, allowing task-level context to persist across multiple action chunks. To bridge semantic context with physical prediction and control, CogWAM employs progress-conditioned WORLD and ACTION queries that selectively extract task-relevant information for future-world prediction and action generation. During training, the Semantic State provides shared task-progress context for both branches, while inference removes the future-prediction branch and directly generates actions from observations and the maintained state. We further introduce semantic training strategies to improve transition learning and closed-loop conditioning. Without additional robot-action pretraining, CogWAM achieves 15.56 / 11.70 % Score/SR on RoboDojo and state-of-the-art performance on BiCoord, while real-world experiments demonstrate closed-loop dual-arm manipulation with 16.4 fewer Semantic State regenerations than step-wise updating.