Theoretical Framework for Human-Like Robotic Taste with Reference to Nutritional Needs

Abstract

This paper contains a theoretical framework for an intelligent robot that can feed itself with organic matter and learns to like and want certain foods. Furthermore, being tied to the onboard generation of power, the robot’s learning is grounded to physical requirements that need to be met for continued robot operation. We posit a system wherein the electrical power is generated by a microbial fuel cell(MFC), and the food value can be assessed by measuring the current generated. Interestingly, the MFC requires feed similar in many parameters to food preferred by most people with respect to parameters like salinity or pH. This property is theorized to be a good start for teaching a robotic chef human preferences. We also propose a circuit that injects additional current, simulating social cues for some foods, and effectively resulting in learning an acquired taste.