Effects of Explanation Strategies to Resolve Failures in Human-Robot Collaboration

TL;DR

This study investigates how different explanation strategies by robots during failures affect human-robot collaboration success and user satisfaction, highlighting the importance of explanation quality and prior information.

Contribution

The paper introduces a user study analyzing the impact of varied robot failure explanations on collaboration effectiveness and user satisfaction in human-robot tasks.

Findings

Success depends on explanation level and failure type.

Novice users prefer higher explanation levels.

Prior robot information influences user satisfaction.

Abstract

Despite significant improvements in robot capabilities, they are likely to fail in human-robot collaborative tasks due to high unpredictability in human environments and varying human expectations. In this work, we explore the role of explanation of failures by a robot in a human-robot collaborative task. We present a user study incorporating common failures in collaborative tasks with human assistance to resolve the failure. In the study, a robot and a human work together to fill a shelf with objects. Upon encountering a failure, the robot explains the failure and the resolution to overcome the failure, either through handovers or humans completing the task. The study is conducted using different levels of robotic explanation based on the failure action, failure cause, and action history, and different strategies in providing the explanation over the course of repeated interaction. Our results show that the success in resolving the failures is not only a function of the level of explanation but also the type of failures. Furthermore, while novice users rate the robot higher overall in terms of their satisfaction with the explanation, their satisfaction is not only a function of the robot's explanation level at a certain round but also the prior information they received from the robot.