Personalizing a Service Robot by Learning Human Habits from Behavioral Footprints
- Creators
- Li, Kun
- Meng, Max Q.-H.
Abstract
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.
Additional Information
© The Author(s) 2015. Published by Engineering Sciences Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) Open Access funded by Chinese Academy of Engineering. This project was supported in part by Hong Kong RGC GRC (CUHK14205914 and CUHK415512), awarded to Max Q.-H. Meng. Kun Li and Max Q.-H. Meng declare that they have no conflict of interest or financial conflicts to disclose.Attached Files
Published - 1-s2.0-S2095809916300480-main.pdf
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Additional details
- Eprint ID
- 86121
- Resolver ID
- CaltechAUTHORS:20180430-101114280
- Chinese Academy of Engineering
- CUHK14205914
- Hong Kong Research Grant Council
- CUHK415512
- Hong Kong Research Grant Council
- Created
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2018-04-30Created from EPrint's datestamp field
- Updated
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2021-11-15Created from EPrint's last_modified field