Human-AI Interaction in Industrial Robotics: Design and Empirical Evaluation of a User Interface for Explainable AI-Based Robot Program Optimization
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arXiv
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| Format: | Preprint |
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2024
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| author | Alt, Benjamin Zahn, Johannes Kienle, Claudius Dvorak, Julia May, Marvin Katic, Darko Jäkel, Rainer Kopp, Tobias Beetz, Michael Lanza, Gisela |
| author_facet | Alt, Benjamin Zahn, Johannes Kienle, Claudius Dvorak, Julia May, Marvin Katic, Darko Jäkel, Rainer Kopp, Tobias Beetz, Michael Lanza, Gisela |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_19349 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Human-AI Interaction in Industrial Robotics: Design and Empirical Evaluation of a User Interface for Explainable AI-Based Robot Program Optimization Alt, Benjamin Zahn, Johannes Kienle, Claudius Dvorak, Julia May, Marvin Katic, Darko Jäkel, Rainer Kopp, Tobias Beetz, Michael Lanza, Gisela Robotics Artificial Intelligence Computational Engineering, Finance, and Science Human-Computer Interaction Machine Learning 68T40 I.2.1; I.2.9; I.2.2; J.6; J.7 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. |
| title | Human-AI Interaction in Industrial Robotics: Design and Empirical Evaluation of a User Interface for Explainable AI-Based Robot Program Optimization |
| topic | Robotics Artificial Intelligence Computational Engineering, Finance, and Science Human-Computer Interaction Machine Learning 68T40 I.2.1; I.2.9; I.2.2; J.6; J.7 |
| url | https://arxiv.org/abs/2404.19349 |