# Automaton-Guided Control Synthesis for Signal Temporal Logic Specifications

arXiv: https://arxiv.org/abs/2207.03662 · HTML: https://mlsanity.com/paper/2207.03662/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2022.

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

- [Control Synthesis of Cyber-Physical Systems for Real-Time Specifications through Causation-Guided Reinforcement Learning](https://mlsanity.com/paper/2510.07715/) (2025)

## Abstract (arXiv)

This paper presents an algorithmic framework for control synthesis of continuous dynamical systems subject to signal temporal logic (STL) specifications. We propose a novel algorithm to obtain a time-partitioned finite automaton from an STL specification, and introduce a multi-layered framework that utilizes this automaton to guide a sampling-based search tree both spatially and temporally. Our approach is able to synthesize a controller for nonlinear dynamics and polynomial predicate functions. We prove the correctness and probabilistic completeness of our algorithm, and illustrate the efficiency and efficacy of our framework on several case studies. Our results show an order of magnitude speedup over the state of the art.