학술논문
Counter-Example Guided Imitation Learning of Feedback Controllers from Temporal Logic Specifications
Document Type
Conference
Source
2023 62nd IEEE Conference on Decision and Control (CDC) Decision and Control (CDC), 2023 62nd IEEE Conference on. :5339-5344 Dec, 2023
Subject
Language
ISSN
2576-2370
Abstract
We present a novel method for imitation learning for control requirements expressed using Signal Temporal Logic (STL). More concretely we focus on the problem of training a neural network to imitate a complex controller. The learning process is guided by efficient data aggregation based on counter-examples and a coverage measure. Moreover, we introduce a method to evaluate the performance of the learned controller via parameterization and parameter estimation of the STL requirements. We demonstrate our approach with a flying robot case study.