Gaining Insights into a Robot Localization Monitor Using Explainable Artificial Intelligence

Matthias Josef Eder*, Laurent Frering, Gerald Steinbauer-Wagner

*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung

Abstract

Monitoring the state of a localization component in robotic systems has received increasing attention in recent years, as navigation behaviors of robots rely on a reliable pose estimation to a large extend. Nowadays, research focuses on the development of new approaches to monitor the localization state of a robot. Many of those approaches use Machine Learning techniques which do not provide direct insight into the decision making process and are thus often handled as a black box. In this work, we aim to open this black box by making use of an Explainable Artificial Intelligence (XAI) framework that allows us to improve the understanding of a machine learning based localization monitor. To gain insights into the machine learning model, we make use of the open-source framework SHapley Additive exPlanations (SHAP). Results show that investigations in the model structure of a localization monitor using XAI helps to improve the model’s transparency. Overall, XAI proves to be useful in understanding the decision-making process of a localization monitor and can even help to improve the model’s design quality.

Originalspracheenglisch
TitelAdvances in Service and Industrial Robotics - RAAD 2023
Redakteure/-innenTadej Petrič, Aleš Ude, Leon Žlajpah
Herausgeber (Verlag)Springer
Seiten170-177
Seitenumfang8
Band135
ISBN (Print)9783031326059
DOIs
PublikationsstatusVeröffentlicht - 2023

Publikationsreihe

NameMechanisms and Machine Science
Band135 MMS
ISSN (Print)2211-0984
ISSN (elektronisch)2211-0992

ASJC Scopus subject areas

  • Werkstoffmechanik
  • Maschinenbau

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