保証付き自律性のための敵対的学習に基づくオンライン異常監視
Adversarial Learning-Based On-Line Anomaly Monitoring for Assured Autonomy
学習ベース制御系の安全性・セキュリティをリアルタイム監視する枠組みを提案し、センサ入力からアクチュエータ指令への写像とその逆の写像の妥当性を敵対的学習で検出する。
著者: Naman Patel, Apoorva Nandini Saridena, Anna Choromanska, Prashanth Krishnamurthy, Farshad Khorrami
分類: cs.LG, cs.RO, stat.ML
原文アブストラクト
The paper proposes an on-line monitoring framework for continuous real-time safety/security in learning-based control systems (specifically application to a unmanned ground vehicle). We monitor validity of mappings from sensor inputs to actuator commands, controller-focused anomaly detection (CFAM), and from actuator commands to sensor inputs, system-focused anomaly detection (SFAM). CFAM is an image conditioned energy based generative adversarial network (EBGAN) in which the energy based discriminator distinguishes between proper and anomalous actuator commands. SFAM is based on an action condition video prediction framework to detect anomalies between predicted and observed temporal evolution of sensor data. We demonstrate the effectiveness of the approach on our autonomous ground vehicle for indoor environments and on Udacity dataset for outdoor environments.
