# Generating Explanations for Autonomous Robots: a Systematic Review

arXiv: https://arxiv.org/abs/2412.18516 · HTML: https://mlsanity.com/paper/2412.18516/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2024.

## Built on by (most cited first, 2 of 2)

- [Self-Explainability in Self-Adaptive and Self-Organising Systems: Status and Research Directions](https://mlsanity.com/paper/2606.09568/) (2026)
- [ConceptTree: Bringing Semantic Transparency to Black-Box Decision Making for Robotic Manipulation](https://mlsanity.com/paper/2607.17861/) (2026)

## Abstract (arXiv)

Building trust between humans and robots has long interested the robotics community. Various studies have aimed to clarify the factors that influence the development of user trust. In Human-Robot Interaction (HRI) environments, a critical aspect of trust development is the robot's ability to make its behavior understandable. The concept of an eXplainable Autonomous Robot (XAR) addresses this requirement. However, giving a robot self-explanatory abilities is a complex task. Robot behavior includes multiple skills and diverse subsystems. This complexity led to research into a wide range of methods for generating explanations about robot behavior. This paper presents a systematic literature review that analyzes existing strategies for generating explanations in robots and studies the current XAR trends. Results indicate promising advancements in explainability systems. However, these systems are still unable to fully cover the complex behavior of autonomous robots. Furthermore, we also identify a lack of consensus on the theoretical concept of explainability, and the need for a robust methodology to assess explainability methods and tools has been identified.