Counter-example guided Imitation Learning of Feedback Controllers from Temporal Logic Specifications
Robotics
2024-03-26 v1 Systems and Control
Systems and Control
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.
Cite
@article{arxiv.2403.16593,
title = {Counter-example guided Imitation Learning of Feedback Controllers from Temporal Logic Specifications},
author = {Thao Dang and Alexandre Donzé and Inzemamul Haque and Nikolaos Kekatos and Indranil Saha},
journal= {arXiv preprint arXiv:2403.16593},
year = {2024}
}