Toward Enabling Facial Emotional Expressions in Humanoid Robots

ABSTRACT The ability to generate facial expressions is essential for humanoid social robots to engage in natural, human‐like interactions. This capability significantly enhances the fluidity of human‐robot communication and the precision of emotional expression. However, current methods rely heavily on pre‐programmed behavioral patterns, which are manually implemented at considerable cost in both time and human labor. To enable humanoid robots to autonomously acquire generalized expressive capabilities, they must learn human‐like expressions via self‐supervised training. To address this challenge, we present a highly biomimetic robotic face equipped with physically actuated electronic facial units, alongside an end‐to‐end learning framework that integrates Kolmogorov‐Arnold Networks (KAN) with attention mechanisms. In contrast to previous approaches, we have also developed an automated data collection system guided by expert‐designed facial motion primitives, enabling the construction of a high‐quality dataset. Notably, to the best of our knowledge, this constitutes the first facial expression dataset specifically designed for humanoid social robots. Extensive evaluations demonstrate that our method enables accurate and diverse facial mimicry across a range of test subjects.