Human-AI Interaction in Industrial Robotics: Design and Empirical Evaluation of a User Interface for Explainable AI-Based Robot Program Optimization
Abstract
While recent advances in deep learning have demonstrated its transformative potential, its adoption for real-world manufacturing applications remains limited. We present an Explanation User Interface (XUI) for a state-of-the-art deep learning-based robot program optimizer which provides both naive and expert users with different user experiences depending on their skill level, as well as Explainable AI (XAI) features to facilitate the application of deep learning methods in real-world applications. To evaluate the impact of the XUI on task performance, user satisfaction and cognitive load, we present the results of a preliminary user survey and propose a study design for a large-scale follow-up study.
Cite
@article{arxiv.2404.19349,
title = {Human-AI Interaction in Industrial Robotics: Design and Empirical Evaluation of a User Interface for Explainable AI-Based Robot Program Optimization},
author = {Benjamin Alt and Johannes Zahn and Claudius Kienle and Julia Dvorak and Marvin May and Darko Katic and Rainer Jäkel and Tobias Kopp and Michael Beetz and Gisela Lanza},
journal= {arXiv preprint arXiv:2404.19349},
year = {2024}
}
Comments
6 pages, 4 figures, accepted at the 2024 CIRP International Conference on Manufacturing Systems (CMS)