# Optimizing Facial Expressions of an Android Robot Effectively: a Bayesian Optimization Approach

arXiv: https://arxiv.org/abs/2301.05620 · HTML: https://mlsanity.com/paper/2301.05620/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 13 Jan 2023.

## Builds on (most cited first, 5 of 5)

- [Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020](https://mlsanity.com/paper/2104.10201/) (2021)
- [Constrained Efficient Global Optimization of Expensive Black-box Functions](https://mlsanity.com/paper/2211.00162/) (2022)
- [Py-Feat: Python Facial Expression Analysis Toolbox](https://mlsanity.com/paper/2104.03509/) (2021)
- [Smile Like You Mean It: Driving Animatronic Robotic Face with Learned Models](https://mlsanity.com/paper/2105.12724/) (2021)
- [Facial emotion expressions in human-robot interaction: A survey](https://mlsanity.com/paper/2103.07169/) (2021)

## Built on by (most cited first, 3 of 3)

- [Driving Animatronic Robot Facial Expression From Speech](https://mlsanity.com/paper/2403.12670/) (2024)
- [HAPI: A Model for Learning Robot Facial Expressions from Human Preferences](https://mlsanity.com/paper/2503.17046/) (2025)
- [Learning to Control an Android Robot Head for Facial Animation](https://mlsanity.com/paper/2412.13641/) (2024)

## Abstract (arXiv)

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.