Study of Signal Temporal Logic Robustness Metrics for Robotic Tasks Optimization
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.
Keywords
Cite
@article{arxiv.2110.00339,
title = {Study of Signal Temporal Logic Robustness Metrics for Robotic Tasks Optimization},
author = {Akshay Dhonthi and Philipp Schillinger and Leonel Rozo and Daniele Nardi},
journal= {arXiv preprint arXiv:2110.00339},
year = {2021}
}
Comments
3 pages, 4 figures and 2 tables. Presented in "IROS 2021 Workshop - Benchmarking of robotic grasping and manipulation: protocols, metrics and data analysis"