Human-AI Interaction in Industrial Robotics: Design and Empirical Evaluation of a User Interface for Explainable AI-Based Robot Program Optimization

TL;DR

This paper introduces an Explanation User Interface for AI-based robot program optimization in manufacturing, aiming to improve user experience, understanding, and adoption of deep learning methods through tailored explanations and evaluation.

Contribution

It presents a novel XUI design with XAI features for industrial robotics, addressing user skill levels and providing a foundation for empirical evaluation in real-world settings.

Findings

Preliminary survey indicates improved user satisfaction.

XUI facilitates better understanding of AI decisions.

Design supports different user expertise levels.

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.