Learning Linear Temporal Properties for Autonomous Robotic Systems

被引：3

作者：

Ghiorzi, Enrico ^{[1
]}

Colledanchise, Michele ^{[1
]}

Piquet, Gianluca ^{[2
]}

Bernagozzi, Stefano ^{[1
,2
]}

Tacchella, Armando ^{[2
]}

Natale, Lorenzo ^{[1
]}

机构：

[1] Ist Italiano Tecnol, I-40064 Genoa, Italy

[2] Univ Genoa, DIBRIS, I-16100 Genoa, Italy

来源：

IEEE ROBOTICS AND AUTOMATION LETTERS | 2023年 / 8卷 / 05期

关键词：

Robots; Task analysis; Skeleton; Syntactics; Search problems; Reactive power; Cost accounting; Formal methods in robotics and automation; autonomous agents; failure detection and recovery;

D O I：

10.1109/LRA.2023.3263368

中图分类号：

TP24 [机器人技术];

学科分类号：

080202 ; 1405 ;

摘要：

The problem of passive learning of linear temporal logic formulae consists in finding the best explanation for how two sets of execution traces differ, in the form of the shortest formula that separates the two sets. We approach the problem by implementing an exhaustive search algorithm optimized for execution speed. We apply it to the use-case of a robot moving in an unstructured environment as its battery discharges, both in simulation and in the real world. The results of our experiments confirm that our approach can learn temporal formulas explaining task failures in a case of practical interest.

引用

页码：2930 / 2937

页数：8

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