Charting Visual Impression of Robot Hands

TL;DR

This study analyzes how users perceive various robot hands by collecting ratings and developing models to predict impressions based on design features, providing guidelines for improved social robot hand design.

Contribution

It introduces a dataset of 73 robot hands, develops predictive models for user impressions, and offers design guidelines to enhance social robot hand perception.

Findings

Shape of fingertips influences perception

Color scheme affects user ratings

Size impacts user impression

Abstract

A wide variety of robotic hands have been designed to date. Yet, we do not know how users perceive these hands and feel about interacting with them. To inform hand design for social robots, we compiled a dataset of 73 robot hands and ran an online study, in which 160 users rated their impressions of the hands using 17 rating scales. Next, we developed 17 regression models that can predict user ratings (e.g., humanlike) from the design features of the hands (e.g., number of fingers). The models have less than a 10-point error in predicting the user ratings on a 0-100 scale. The shape of the fingertips, color scheme, and size of the hands influence the user ratings the most. We present simple guidelines to improve user impression of robot hands and outline remaining questions for future work.