# Robust Control for Signal Temporal Logic Specifications using Average Space Robustness

arXiv: https://arxiv.org/abs/1607.07019 · HTML: https://mlsanity.com/paper/1607.07019/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2016.

## Built on by (most cited first, 4 of 4)

- [Assured Learning-enabled Autonomy: A Metacognitive Reinforcement Learning Framework](https://mlsanity.com/paper/2103.12558/) (2021)
- [Uncertainty-Aware Signal Temporal Logic Inference](https://mlsanity.com/paper/2105.11545/) (2021)
- [Risk-Awareness in Learning Neural Controllers for Temporal Logic Objectives](https://mlsanity.com/paper/2210.07439/) (2022)
- [Reward Machines for Signal Temporal Logic](https://mlsanity.com/paper/2608.13625/) (2026)

## Abstract (arXiv)

Control systems that satisfy temporal logic specifications have become increasingly popular due to their applicability to robotic systems. Existing control methods, however, are computationally demanding, especially when the problem size becomes too large. In this paper, a robust and computationally efficient model predictive control framework for signal temporal logic specifications is proposed. We introduce discrete average space robustness, a novel quantitative semantic for signal temporal logic, that is directly incorporated into the cost function of the model predictive controller. The optimization problem entailed in this framework can be written as a convex quadratic program when no disjunctions are considered and results in a robust satisfaction of the specification. Furthermore, we define the predicate robustness degree as a new robustness notion. Simulations of a multi-agent system subject to complex specifications demonstrate the efficacy of the proposed method.