STL2vec: Signal Temporal Logic Embeddings for Control Synthesis With Recurrent Neural Networks
Wataru Hashimoto, Kazumune Hashimoto, Shigemasa Takai
Abstract
In this letter, a method for learning a recurrent neural network (RNN) controller that maximizes the robustness of signal temporal logic (STL) specifications is presented. In contrast to previous methods, we consider synthesizing the RNN controller for which the user is able to select an STL specification arbitrarily from multiple STL specifications. To obtain such a controller, we propose a novel notion called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">STL2vec</i> , which represents a vector representation of the STL specifications and exhibits their similarities. The construction of the STL2vec is useful since it allows us to enhance the efficiency and performance of the controller. We validate our proposed method through the examples of the path planning problem.
BibTeX
@inproceedings{ral2022_stl2vecsignaltem,
title = {STL2vec: Signal Temporal Logic Embeddings for Control Synthesis With Recurrent Neural Networks},
author = {Wataru Hashimoto and Kazumune Hashimoto and Shigemasa Takai},
booktitle = {RA-L 2022},
year = {2022}
}