Towards Personalized Explanation of Robot Path Planning via User Feedback

TL;DR

This paper introduces a system that generates personalized explanations for robot path planning based on user feedback, enhancing transparency and user satisfaction through interactive, preference-based explanations and conflict resolution.

Contribution

It presents a novel algorithm for personalized explanation generation in robot path planning considering user preferences and interactive conflict resolution.

Findings

Personalized explanations increase user satisfaction.

Users appreciated question-answering and conflict resolution features.

System effectively detects and resolves preference conflicts.

Abstract

Prior studies have found that explaining robot decisions and actions helps to increase system transparency, improve user understanding, and enable effective human-robot collaboration. In this paper, we present a system for generating personalized explanations of robot path planning via user feedback. We consider a robot navigating in an environment modeled as a Markov decision process (MDP), and develop an algorithm to automatically generate a personalized explanation of an optimal MDP policy, based on the user preference regarding four elements (i.e., objective, locality, specificity, and corpus). In addition, we design the system to interact with users via answering users' further questions about the generated explanations. Users have the option to update their preferences to view different explanations. The system is capable of detecting and resolving any preference conflict via user interaction. The results of an online user study show that the generated personalized explanations improve user satisfaction, while the majority of users liked the system's capabilities of question-answering and conflict detection/resolution.