For a domestic personal robot, personalized services are as important as predesigned tasks, because the robot needs to adjust the home state based on the operator's habits. An operator's habits are composed of...For a domestic personal robot, personalized services are as important as predesigned tasks, because the robot needs to adjust the home state based on the operator's habits. An operator's habits are composed of cues, behaviors, and rewards. This article introduces behavioral footprints to describe the operator's behaviors in a house, and applies the inverse reinforcement learning technique to extract the operator's habits, represented by a reward function. We implemented the proposed approach with a mobile robot on indoor temperature adjustment, and compared this approach with a baseline method that recorded all the cues and behaviors of the operator. The result shows that the proposed approach allows the robot to reveal the operator's habits accurately and adjust the environment state accordingly.展开更多
Purpose–This paper aims to address the longitudinal control problem for person-following robots(PFRs)for the implementation of this technology.Design/methodology/approach–Nine representative car-following models are...Purpose–This paper aims to address the longitudinal control problem for person-following robots(PFRs)for the implementation of this technology.Design/methodology/approach–Nine representative car-following models are analyzed from PFRs application and the linear model and optimal velocity model/full velocity difference model are qualified and selected in the PFR control.Findings–A lab PFR with the bar-laser-perception device is developed and tested in the field,and the results indicate that the proposed models perform well in normal person-following scenarios.Originality/value–This study fills a gap in the research on PRFs longitudinal control and provides a useful and practical reference on PFRs longitudinal control for the related research.展开更多
基金supported in part by Hong Kong RGC GRC (CUHK14205914 and CUHK415512)
文摘For a domestic personal robot, personalized services are as important as predesigned tasks, because the robot needs to adjust the home state based on the operator's habits. An operator's habits are composed of cues, behaviors, and rewards. This article introduces behavioral footprints to describe the operator's behaviors in a house, and applies the inverse reinforcement learning technique to extract the operator's habits, represented by a reward function. We implemented the proposed approach with a mobile robot on indoor temperature adjustment, and compared this approach with a baseline method that recorded all the cues and behaviors of the operator. The result shows that the proposed approach allows the robot to reveal the operator's habits accurately and adjust the environment state accordingly.
基金supported by the Basal Research Fund of Central Public Research Institute of China(Grant No.20212702).
文摘Purpose–This paper aims to address the longitudinal control problem for person-following robots(PFRs)for the implementation of this technology.Design/methodology/approach–Nine representative car-following models are analyzed from PFRs application and the linear model and optimal velocity model/full velocity difference model are qualified and selected in the PFR control.Findings–A lab PFR with the bar-laser-perception device is developed and tested in the field,and the results indicate that the proposed models perform well in normal person-following scenarios.Originality/value–This study fills a gap in the research on PRFs longitudinal control and provides a useful and practical reference on PFRs longitudinal control for the related research.
