HMM信念状態の勾配指標による高速・堅牢・汎用的なイベント検出
Fast, Robust, and Versatile Event Detection through HMM Belief State Gradient Measures
HMMのフィルタリング信念状態の対数尤度勾配と最新の出力確率の関係を理論的に示し、スキル・異常検出のための高速で汎用的な指標を提案した。
著者: Shuangqi Luo, Hongmin Wu, Hongbin Lin, Shuangda Duan, Yisheng Guan, Juan Rojas
分類: cs.RO, cs.AI
原文アブストラクト
Event detection is a critical feature in data-driven systems as it assists with the identification of nominal and anomalous behavior. Event detection is increasingly relevant in robotics as robots operate with greater autonomy in increasingly unstructured environments. In this work, we present an accurate, robust, fast, and versatile measure for skill and anomaly identification. A theoretical proof establishes the link between the derivative of the log-likelihood of the HMM filtered belief state and the latest emission probabilities. The key insight is the inverse relationship in which gradient analysis is used for skill and anomaly identification. Our measure showed better performance across all metrics than related state-of-the art works. The result is broadly applicable to domains that use HMMs for event detection.
