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- W3200011036 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." @default.
- W3200011036 created "2021-09-27" @default.
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- W3200011036 date "2022-04-01" @default.
- W3200011036 modified "2023-10-14" @default.
- W3200011036 title "STL2vec: Signal Temporal Logic Embeddings for Control Synthesis With Recurrent Neural Networks" @default.
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- W3200011036 doi "https://doi.org/10.1109/lra.2022.3155197" @default.
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