With advances in the field of robotic manipulation,
sensing and machine learning, robotic chefs are expected to
become prevalent in our kitchens and restaurants. Robotic chefs
are envisioned to replicate human skills in order to reduce
the burden of the cooking process. However, the potential
of robots as a means to enhance the dining experience is
unrecognised. This work introduces the concept of food quality
optimization and its challenges with an automated omelette
cooking robotic system. The design and control of the robotic
system that uses general kitchen tools is presented first. Next, we
investigate new optimization strategies for improving subjective
food quality rating, a problem challenging because of the
qualitative nature of the objective and strongly constrained
number of function evaluations possible. Our results show that
through appropriate design of the optimization routine using
Batch Bayesian Optimization, improvements in the subjective
evaluation of food quality can be achieved reliably, with very
few trials and with the ability for bulk optimization. This study
paves the way towards a broader vision of personalized food
for taste-and-nutrition and transferable recipes