Awakening Facial Emotional Expressions in Human-Robot

TL;DR

This paper presents a novel end-to-end learning framework for autonomous facial expression generation in humanoid robots, utilizing a biomimetic face, a new dataset, and advanced neural mechanisms to improve naturalness and diversity.

Contribution

It introduces the first open-source facial dataset for humanoid robots and combines KAN and attention mechanisms for improved expression learning.

Findings

Achieves accurate facial mimicry across subjects

Demonstrates diverse expression generation

Provides a new dataset for future research

Abstract

The facial expression generation capability of humanoid social robots is critical for achieving natural and human-like interactions, playing a vital role in enhancing the fluidity of human-robot interactions and the accuracy of emotional expression. Currently, facial expression generation in humanoid social robots still relies on pre-programmed behavioral patterns, which are manually coded at high human and time costs. To enable humanoid robots to autonomously acquire generalized expressive capabilities, they need to develop the ability to learn human-like expressions through self-training. To address this challenge, we have designed a highly biomimetic robotic face with physical-electronic animated facial units and developed an end-to-end learning framework based on KAN (Kolmogorov-Arnold Network) and attention mechanisms. Unlike previous humanoid social robots, we have also meticulously designed an automated data collection system based on expert strategies of facial motion primitives to construct the dataset. Notably, to the best of our knowledge, this is the first open-source facial dataset for humanoid social robots. Comprehensive evaluations indicate that our approach achieves accurate and diverse facial mimicry across different test subjects.