"Nice to meet you!": Expressing Emotions with Movement Gestures and Textual Content in Automatic Handwriting Robots

TL;DR

This paper explores how a handwriting robot can express emotions through movement gestures and textual content, revealing that textual output strongly influences emotional perception, with movement parameters providing contextual cues.

Contribution

It introduces a method to encode emotional expressions in handwriting robots using both textual and gestural cues, enhancing their ability to convey emotions.

Findings

Textual output significantly influences emotion perception.

Movement parameters like speed and pressure provide contextual emotional cues.

Participants accurately interpreted robot's emotional expressions based on combined cues.

Abstract

Text-writing robots have been used in assistive writing and drawing applications. However, robots do not convey emotional tones in the writing process due to the lack of behaviors humans typically adopt. To examine how people interpret designed robotic expressions of emotion through both movements and textual output, we used a pen-plotting robot to generate texts by performing human-like behaviors like stop-and-go, speed, and pressure variation. We examined how people convey emotion in the writing process by observing how they wrote in different emotional contexts. We then mapped these human expressions during writing to the handwriting robot and measured how well other participants understood the robot's affective expression. We found that textual output was the strongest determinant of participants' ability to perceive the robot's emotions, whereas parameters of gestural movements of the robots like speed, fluency, pressure, size, and acceleration could be useful for understanding the context of the writing expression.