Generating Explanations for Autonomous Robots: a Systematic Review

TL;DR

This systematic review examines current strategies for generating explanations in autonomous robots, highlighting advancements, existing gaps, and the need for standardized assessment methods to improve robot explainability and trust.

Contribution

The paper provides a comprehensive analysis of existing explanation generation methods for autonomous robots and identifies key gaps and future research directions.

Findings

Advancements in explainability systems are promising.

Current systems cannot fully cover complex robot behaviors.

There is a lack of consensus on explainability concepts and assessment methodologies.

Abstract

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.