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

IF 0.4 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Applied Computing Review Pub Date : 2023-03-27 DOI:10.1145/3555776.3577760
Geon Kim, Hyunju Kim, Dongman Lee
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引用次数: 1

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.
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在现实世界的智能空间中,移动机器人驱动的偏好学习用于用户状态特定的热控制
室内环境质量(IEQ)是智能空间最重要的目标之一。热舒适通常被认为是IEQ中最重要的因素,它取决于个性化的热偏好。在本文中,我们探讨了部署机器人驱动的个性化热控制系统的技术挑战,该系统使用移动机器人有效地学习用户特定状态的偏好。当系统部署在现实世界中时,我们进行了一些实验,为克服这些挑战(即低图像识别)提供了线索。我们从探索中提出了改进机器人驱动偏好学习的未来方向。
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来源期刊
Applied Computing Review
Applied Computing Review COMPUTER SCIENCE, INFORMATION SYSTEMS-
自引率
40.00%
发文量
8
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