On development and perception of theomorphic robots: the uncanny valley effect to Artificial Intelligence (AI)

Abstract

This thesis presents the development and evaluation of a theomorphic robot, the Korean Pensive Buddha Robot, a life-sized, articulated robotic statue modelled after the Korean National Treasure No. 83, the Pensive Bodhisattva. The project integrates three-dimensional (3D) printing, servomotor actuation, and conversational Artificial Intelligence (AI) to create a robot capable of symbolic movement and interaction. While initially conceived as a platform for Human–Robot Interaction (HRI) studies, this research adopts a novel methodological shift: rather than directly testing with human participants, the evaluation uses OpenAI’s Contrastive Language–Image Pre-training (CLIP) model to simulate perception and classification processes. Specifically, the research uses the conceptual structure of the Godspeed Questionnaire, widely used in research on the Uncanny Valley effect, to investigate how an AI system categorizes a robot relative to other entities. The Uncanny Valley, introduced by Masahiro Mori (1970), describes how human affinity toward anthropomorphic robots and avatars increases with human likeness until subtle imperfections trigger discomfort or eeriness, causing a sharp drop in emotional acceptance. This phenomenon produces the characteristic “valley” between realism and affective response. CLIP is tasked with evaluating images of four categories: the Pensive Buddha Robot, its first half-scale prototype, a still statue, and a real human. The model’s outputs are analyzed across Godspeed dimensions of anthropomorphism, animacy, likability, perceived intelligence, and safety. By applying an AI-based lens to questions traditionally addressed through human evaluation, this research also investigates whether machine perception aligns with the Uncanny Valley effect. The results provide a new perspective on how AI vision systems interpret robotic embodiment and symbolic form, while also testing the applicability of social robotics assessment tools in computational contexts. Ultimately, this research contributes to both cultural robotics and methodological innovation in evaluating robot perception, without relying exclusively on human subjects.