Quadruped robots and the uncanny valley: a study on canine representation

Abstract

This research analyses how dog-like features in robotic quadrupeds influence their social perception. The initial part of the methodology describes the design and development of three robotic dog prototypes that include key features like a head and a tail. Further, Contrastive Language-Image Pre-Training (CLIP), a neural network that has demonstrated signatures of the uncanny valley effect before, was used to explore how the perception of quadrupeds evolves as their level of canine likeness intensifies. For this purpose, seven models were tested, ranging from a fully robotic quadruped to a living dog, and 252 images were assessed for each. Our findings indicate that the uncanny valley effect also develops in quadruped robots. This novel contribution serves as a reference to select an appropriate level of realism for four-legged robots. This is particularly valuable for robotic applications that look to incorporate human-dog dynamics.