Optimizing Facial Expressions of an Android Robot Effectively: a Bayesian Optimization Approach
This work addresses the challenge of enabling androids to produce convincing human-like facial expressions for more effective social interactions, representing an incremental improvement over existing methods.
The researchers tackled the problem of optimizing facial expressions for an android robot to improve human-robot communication, using a Bayesian optimization approach that increased the rated strength of expressions like anger and surprise compared to a previous expert-based method.
Expressing various facial emotions is an important social ability for efficient communication between humans. A key challenge in human-robot interaction research is providing androids with the ability to make various human-like facial expressions for efficient communication with humans. The android Nikola, we have developed, is equipped with many actuators for facial muscle control. While this enables Nikola to simulate various human expressions, it also complicates identification of the optimal parameters for producing desired expressions. Here, we propose a novel method that automatically optimizes the facial expressions of our android. We use a machine vision algorithm to evaluate the magnitudes of seven basic emotions, and employ the Bayesian Optimization algorithm to identify the parameters that produce the most convincing facial expressions. Evaluations by naive human participants demonstrate that our method improves the rated strength of the android's facial expressions of anger, disgust, sadness, and surprise compared with the previous method that relied on Ekman's theory and parameter adjustments by a human expert.