Towards Deployment of Mobile Robot driven Preference Learning for User-State-Specific Thermal Control in A Real-World Smart Space

Abstract

Indoor Environment Quality (IEQ) is one of the most important goals for smart spaces. Thermal comfort is typically considered the most emphasized factor in IEQ that depends on personalized thermal preference. In this paper, we explore technical challenges to deploying a robot-driven personalized thermal control system that uses a mobile robot for learning user-state-specific preference efficiently. We conduct a few experiments that give a clue to overcome such challenges (i.e. low image recognition) when the system is deployed in a real world. We present future directions to improve robot-driven preference learning from the exploration.