TY - GEN
T1 - Using a Bayesian Network to Predict User Trust in Teleoperation Robots
AU - Cárdenas, Juan José García
AU - Tapus, Adriana
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - Trust plays a crucial role in human-robot interaction (HRI), especially in teleoperation scenarios where users control robots remotely. This paper develops a static trust prediction model using a Bayesian approach to improve system design, user experience, and robotic reliability. By integrating prior knowledge with empirical data, we constructed a Bayesian model that estimates trust levels in teleoperated robotic systems. Our model incorporates physiological measures and task performance data to provide a comprehensive trust prediction framework. We evaluated the model’s performance using precision, recall, and F1 score, achieving high precision (0.92), good recall (0.84), and a balanced F1 score (0.79). These metrics demonstrate the model’s effectiveness in accurately predicting trust levels in teleoperated robotic systems. The results underscore the importance of trustworthiness in maintaining user confidence and improving system interactions. This study highlights the potential of Bayesian models in enhancing the reliability and user experience of teleoperated robots, offering valuable insights for future developments in HRI research.
AB - Trust plays a crucial role in human-robot interaction (HRI), especially in teleoperation scenarios where users control robots remotely. This paper develops a static trust prediction model using a Bayesian approach to improve system design, user experience, and robotic reliability. By integrating prior knowledge with empirical data, we constructed a Bayesian model that estimates trust levels in teleoperated robotic systems. Our model incorporates physiological measures and task performance data to provide a comprehensive trust prediction framework. We evaluated the model’s performance using precision, recall, and F1 score, achieving high precision (0.92), good recall (0.84), and a balanced F1 score (0.79). These metrics demonstrate the model’s effectiveness in accurately predicting trust levels in teleoperated robotic systems. The results underscore the importance of trustworthiness in maintaining user confidence and improving system interactions. This study highlights the potential of Bayesian models in enhancing the reliability and user experience of teleoperated robots, offering valuable insights for future developments in HRI research.
KW - Bayesian network
KW - Human-Robot Interaction
KW - Teleoperated Robotic Systems
KW - Trust
UR - https://www.scopus.com/pages/publications/105002117839
U2 - 10.1007/978-981-96-3525-2_8
DO - 10.1007/978-981-96-3525-2_8
M3 - Conference contribution
AN - SCOPUS:105002117839
SN - 9789819635245
T3 - Lecture Notes in Computer Science
SP - 91
EP - 104
BT - Social Robotics - 16th International Conference, ICSR + AI 2024, Proceedings
A2 - Palinko, Oskar
A2 - Bodenhagen, Leon
A2 - Cabibihan, John-John
A2 - Fischer, Kerstin
A2 - Šabanović, Selma
A2 - Winkle, Katie
A2 - Behera, Laxmidhar
A2 - Ge, Shuzhi Sam
A2 - Chrysostomou, Dimitrios
A2 - Jiang, Wanyue
A2 - He, Hongsheng
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th International Conference on Social Robotics, ICSR + AI 2024
Y2 - 23 October 2024 through 26 October 2024
ER -