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Backpropagation through signal temporal logic specifications: Infusing logical structure into gradient-based methods.

Authors :
Leung, Karen
Aréchiga, Nikos
Pavone, Marco
Source :
International Journal of Robotics Research. May2023, Vol. 42 Issue 6, p356-370. 15p.
Publication Year :
2023

Abstract

This paper presents a technique, named STLCG, to compute the quantitative semantics of Signal Temporal Logic (STL) formulas using computation graphs. STLCG provides a platform which enables the incorporation of logical specifications into robotics problems that benefit from gradient-based solutions. Specifically, STL is a powerful and expressive formal language that can specify spatial and temporal properties of signals generated by both continuous and hybrid systems. The quantitative semantics of STL provide a robustness metric, that is, how much a signal satisfies or violates an STL specification. In this work, we devise a systematic methodology for translating STL robustness formulas into computation graphs. With this representation, and by leveraging off-the-shelf automatic differentiation tools, we are able to efficiently backpropagate through STL robustness formulas and hence enable a natural and easy-to-use integration of STL specifications with many gradient-based approaches used in robotics. Through a number of examples stemming from various robotics applications, we demonstrate that STLCG is versatile, computationally efficient, and capable of incorporating human-domain knowledge into the problem formulation. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02783649
Volume :
42
Issue :
6
Database :
Academic Search Index
Journal :
International Journal of Robotics Research
Publication Type :
Academic Journal
Accession number :
165129558
Full Text :
https://doi.org/10.1177/02783649221082115