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

arXiv: https://arxiv.org/abs/2404.19349 · HTML: https://mlsanity.com/paper/2404.19349/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 30 Apr 2024.

## Builds on (most cited first, 4 of 4)

- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [Robot Program Parameter Inference via Differentiable Shadow Program Inversion](https://mlsanity.com/paper/2103.14452/) (2021)
- [Heuristic-free Optimization of Force-Controlled Robot Search Strategies in Stochastic Environments](https://mlsanity.com/paper/2207.07524/) (2022)
- [How the Experts Do It: Assessing and Explaining Agent Behaviors in Real-Time Strategy Games](https://mlsanity.com/paper/1711.06953/) (2017)

## Built on by (most cited first, 1 of 1)

- [AI-based Framework for Robust Model-Based Connector Mating in Robotic Wire Harness Installation](https://mlsanity.com/paper/2503.09409/) (2025)

## Abstract (arXiv)

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.