Study of Signal Temporal Logic Robustness Metrics for Robotic Tasks Optimization

TL;DR

This paper evaluates various Signal Temporal Logic (STL) robustness metrics for robotic manipulation, demonstrating how STL-based cost functions can optimize robot performance in simulated environments.

Contribution

It provides a comparative analysis of STL robustness metrics and illustrates their application in defining and optimizing complex constraints for robotic tasks.

Findings

STL robustness metrics can effectively represent task constraints.

STL-based cost functions are compatible with existing optimizers.

Initial simulation results show promising optimization outcomes.

Abstract

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.