Signal Temporal Logic Synthesis as Probabilistic Inference

Ki Myung Brian Lee; Chanyeol Yoo; Robert Fitch

ICRA 2021poster12 citations

Signal Temporal Logic Synthesis as Probabilistic Inference

Ki Myung Brian Lee, Chanyeol Yoo, Robert Fitch

Abstract

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.

BibTeX

@inproceedings{icra2021_signaltemporallo,
  title = {Signal Temporal Logic Synthesis as Probabilistic Inference},
  author = {Ki Myung Brian Lee and Chanyeol Yoo and Robert Fitch},
  booktitle = {ICRA 2021},
  year = {2021}
}