TY - GEN
T1 - Gaining Insights into a Robot Localization Monitor Using Explainable Artificial Intelligence
AU - Eder, Matthias Josef
AU - Frering, Laurent
AU - Steinbauer-Wagner, Gerald
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Explainable Artificial Intelligence
KW - Localization Monitoring
KW - Machine Learning
KW - SHAP
KW - XAI
UR - http://www.scopus.com/inward/record.url?scp=85163299363&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-32606-6_20
DO - 10.1007/978-3-031-32606-6_20
M3 - Conference paper
SN - 9783031326059
VL - 135
T3 - Mechanisms and Machine Science
SP - 170
EP - 177
BT - Advances in Service and Industrial Robotics - RAAD 2023
A2 - Petrič, Tadej
A2 - Ude, Aleš
A2 - Žlajpah, Leon
PB - Springer
ER -