Robotic optimization of powdered beverages leveraging computer vision and Bayesian optimization

Abstract

The growing demand for innovative research in the food industry is driving the adoption of robots in large-scale experimentation, a shift that offers increased precision, repeatability, and efficiency in product manufacturing and evaluation. This paper addresses this need by introducing a robotic system that extends automation into optimization and closed-loop quality control, using powdered cappuccino preparation as a case study. By leveraging Bayesian Optimization and image analysis, the robot explores the parameter space to identify the ideal conditions for producing cappuccino with high foam quality. A computer vision-based feedback loop further improves the beverage by mimicking human-like corrections in preparation process. Findings demonstrate the effectiveness of robotic automation in achieving high repeatability and enabling extensive exploration of system parameters, paving the way for more advanced and reliable food product development.