Uncanny valley for interactive social agents: an experimental study

Abstract

Background

The uncanny valley hypothesis states that users may experience discomfort when interacting with almost human-like artificial characters. Advancements in artificial intelligence, robotics, and computer graphics have led to the development of life-like virtual humans and humanoid robots. Revisiting this hypothesis is necessary to check whether they positively or negatively affect the current population, who are highly accustomed to the latest technologies.

Methods

In this study, we present a unique evaluation of the uncanny valley hypothesis by allowing participants to interact live with four humanoid robots that have varying levels of human-likeness. Each participant completed a survey questionnaire to evaluate the affinity of each robot. Additionally, we used deep learning methods to quantify the participants’ emotional states using multimodal cues, including visual, audio, and text cues, by recording the participant–robot interactions.

Results

Multi-modal analysis and surveys provided interesting results and insights into the uncanny valley hypothesis.