We reformulate the signal temporal logic (STL) synthesis problem as a maximum a-posteriori (MAP) inference problem. To this end, we introduce the notion of random STL~(RSTL), which extends deterministic STL with random predicates. This new probabilistic extension naturally leads to a synthesis-as-inference approach. The proposed method allows for differentiable, gradient-based synthesis while extending the class of possible uncertain semantics. We demonstrate that the proposed framework scales well with GPU-acceleration, and present realistic applications of uncertain semantics in robotics that involve target tracking and the use of occupancy grids.
@article{arxiv.2105.06121,
title = {Signal Temporal Logic Synthesis as Probabilistic Inference},
author = {Ki Myung Brian Lee and Chanyeol Yoo and Robert Fitch},
journal= {arXiv preprint arXiv:2105.06121},
year = {2021}
}