# Signal Temporal Logic Synthesis as Probabilistic Inference

arXiv: https://arxiv.org/abs/2105.06121 · HTML: https://mlsanity.com/paper/2105.06121/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2021.

## Built on by (most cited first, 1 of 1)

- [Model Predictive Robustness of Signal Temporal Logic Predicates](https://mlsanity.com/paper/2209.07881/) (2022)

## Abstract (arXiv)

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.