# Adapting Robot's Explanation for Failures Based on Observed Human Behavior in Human-Robot Collaboration

arXiv: https://arxiv.org/abs/2504.09717 · HTML: https://mlsanity.com/paper/2504.09717/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 13 Apr 2025.

## Builds on (most cited first, 9 of 9)

- [The Emerging Landscape of Explainable AI Planning and Decision Making](https://mlsanity.com/paper/2002.11697/) (2020)
- [Explainable AI for Robot Failures: Generating Explanations that Improve User Assistance in Fault Recovery](https://mlsanity.com/paper/2101.01625/) (2021)
- [Why did I fail? A Causal-based Method to Find Explanations for Robot Failures](https://mlsanity.com/paper/2204.04483/) (2022)
- [Towards Social & Engaging Peer Learning: Predicting Backchanneling and Disengagement in Children](https://mlsanity.com/paper/2007.11346/) (2020)
- [Modeling Human Response to Robot Errors for Timely Error Detection](https://mlsanity.com/paper/2208.00565/) (2022)
- [User Study Exploring the Role of Explanation of Failures by Robots in Human Robot Collaboration Tasks](https://mlsanity.com/paper/2303.16010/) (2023)
- [Effects of Explanation Strategies to Resolve Failures in Human-Robot Collaboration](https://mlsanity.com/paper/2309.10127/) (2023)
- [Detecting Interlocutor Confusion in Situated Human-Avatar Dialogue: A Pilot Study](https://mlsanity.com/paper/2206.02436/) (2022)
- [REFLEX Dataset: A Multimodal Dataset of Human Reactions to Robot Failures and Explanations](https://mlsanity.com/paper/2502.14185/) (2025)

## Abstract (arXiv)

This work aims to interpret human behavior to anticipate potential user confusion when a robot provides explanations for failure, allowing the robot to adapt its explanations for more natural and efficient collaboration. Using a dataset that included facial emotion detection, eye gaze estimation, and gestures from 55 participants in a user study, we analyzed how human behavior changed in response to different types of failures and varying explanation levels. Our goal is to assess whether human collaborators are ready to accept less detailed explanations without inducing confusion. We formulate a data-driven predictor to predict human confusion during robot failure explanations. We also propose and evaluate a mechanism, based on the predictor, to adapt the explanation level according to observed human behavior. The promising results from this evaluation indicate the potential of this research in adapting a robot's explanations for failures to enhance the collaborative experience.