# Neural Network-based Control for Multi-Agent Systems from Spatio-Temporal Specifications

arXiv: https://arxiv.org/abs/2104.02737 · HTML: https://mlsanity.com/paper/2104.02737/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 6 Apr 2021.

## Builds on (most cited first, 14 of 14)

- [On Robustness Metrics for Learning STL Tasks](https://mlsanity.com/paper/2003.06041/) (2020)
- [A Smooth Robustness Measure of Signal Temporal Logic for Symbolic Control](https://mlsanity.com/paper/2006.05239/) (2020)
- [Arithmetic-Geometric Mean Robustness for Control from Signal Temporal Logic Specifications](https://mlsanity.com/paper/1903.05186/) (2019)
- [Recurrent Neural Network Controllers for Signal Temporal Logic Specifications Subject to Safety Constraints](https://mlsanity.com/paper/2009.11468/) (2020)
- [Control from Signal Temporal Logic Specifications with Smooth Cumulative Quantitative Semantics](https://mlsanity.com/paper/1904.11611/) (2019)
- [Training Neural Network Controllers Using Control Barrier Functions in the Presence of Disturbances](https://mlsanity.com/paper/2001.08088/) (2020)
- [Model Predictive Control for Signal Temporal Logic Specification](https://mlsanity.com/paper/1703.09563/) (2017)
- [Monitoring Mobile and Spatially Distributed Cyber-Physical Systems](https://mlsanity.com/paper/1904.08847/) (2019)
- [Multirobot Coordination with Counting Temporal Logics](https://mlsanity.com/paper/1810.13087/) (2018)
- [Predictive Monitoring with Logic-Calibrated Uncertainty for Cyber-Physical Systems](https://mlsanity.com/paper/2011.00384/) (2020)
- [A Formal Methods Approach to Pattern Synthesis in Reaction Diffusion Systems](https://mlsanity.com/paper/1409.5671/) (2014)
- [Distributed Communication-aware Motion Planning for Multi-agent Systems from STL and SpaTeL Specifications](https://mlsanity.com/paper/1705.10259/) (2017)
- [Robotic Swarm Control from Spatio-Temporal Specifications](https://mlsanity.com/paper/1609.06283/) (2016)
- [Distributed Communication-aware Motion Planning for Networked Mobile Robots under Formal Specifications](https://mlsanity.com/paper/1812.01204/) (2018)

## Built on by (most cited first, 2 of 2)

- [Learning Spatio-Temporal Specifications for Dynamical Systems](https://mlsanity.com/paper/2112.10714/) (2021)
- [Multi-Agent Planning with Spatio-Temporal and Topological Constraints using STL-GO](https://mlsanity.com/paper/2607.28679/) (2026)

## Abstract (arXiv)

We propose a framework for solving control synthesis problems for multi-agent networked systems required to satisfy spatio-temporal specifications. We use Spatio-Temporal Reach and Escape Logic (STREL) as a specification language. For this logic, we define smooth quantitative semantics, which captures the degree of satisfaction of a formula by a multi-agent team. We use the novel quantitative semantics to map control synthesis problems with STREL specifications to optimization problems and propose a combination of heuristic and gradient-based methods to solve such problems. As this method might not meet the requirements of a real-time implementation, we develop a machine learning technique that uses the results of the off-line optimizations to train a neural network that gives the control inputs at current states. We illustrate the effectiveness of the proposed framework by applying it to a model of a robotic team required to satisfy a spatial-temporal specification under communication constraints.