A Comparison of Visualisation Methods for Disambiguating Verbal Requests in Human-Robot Interaction

TL;DR

This study compares three visualisation methods for disambiguating verbal requests in human-robot interaction, focusing on accuracy, engagement, and user preference in real-time workspace augmentation.

Contribution

It introduces and empirically evaluates three visualisation techniques for reference disambiguation in a human-robot interaction context.

Findings

Significant differences in accuracy and engagement across conditions

No significant difference in task completion time

Participants preferred augmented reality over other methods

Abstract

Picking up objects requested by a human user is a common task in human-robot interaction. When multiple objects match the user's verbal description, the robot needs to clarify which object the user is referring to before executing the action. Previous research has focused on perceiving user's multimodal behaviour to complement verbal commands or minimising the number of follow up questions to reduce task time. In this paper, we propose a system for reference disambiguation based on visualisation and compare three methods to disambiguate natural language instructions. In a controlled experiment with a YuMi robot, we investigated real-time augmentations of the workspace in three conditions -- mixed reality, augmented reality, and a monitor as the baseline -- using objective measures such as time and accuracy, and subjective measures like engagement, immersion, and display interference. Significant differences were found in accuracy and engagement between the conditions, but no differences were found in task time. Despite the higher error rates in the mixed reality condition, participants found that modality more engaging than the other two, but overall showed preference for the augmented reality condition over the monitor and mixed reality conditions.