# Study of Signal Temporal Logic Robustness Metrics for Robotic Tasks Optimization

arXiv: https://arxiv.org/abs/2110.00339 · HTML: https://mlsanity.com/paper/2110.00339/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2021.

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

- [Learning Performance Graphs from Demonstrations via Task-Based Evaluations](https://mlsanity.com/paper/2204.05909/) (2022)
- [Spatiotemporal Robustness of Temporal Logic Tasks using Multi-Objective Reasoning](https://mlsanity.com/paper/2603.29868/) (2026)
- [Autonomous Vehicles Path Planning under Temporal Logic Specifications](https://mlsanity.com/paper/2410.07845/) (2024)
- [Robustness of Robotic Manipulation: Foundations and Frontiers](https://mlsanity.com/paper/2606.31494/) (2026)

## Abstract (arXiv)

Signal Temporal Logic (STL) is an efficient technique for describing temporal constraints. It can play a significant role in robotic manipulation, for example, to optimize the robot performance according to task-dependent metrics. In this paper, we evaluate several STL robustness metrics of interest in robotic manipulation tasks and discuss a case study showing the advantages of using STL to define complex constraints. Such constraints can be understood as cost functions in task optimization. We show how STL-based cost functions can be optimized using a variety of off-the-shelf optimizers. We report initial results of this research direction on a simulated planar environment.