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CLC number: TP242.6

On-line Access: 2022-02-28

Received: 2020-08-26

Revision Accepted: 2022-04-22

Crosschecked: 2021-01-10

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Citations:  Bibtex RefMan EndNote GB/T7714




Meiqin LIU


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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.2 P.246-257


Cloud-assisted cognition adaptation for service robots in changing home environments

Author(s):  Qi WANG, Zhen FAN, Weihua SHENG, Senlin ZHANG, Meiqin LIU

Affiliation(s):  College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   wang9562@zju.edu.cn, fanzhen@zju.edu.cn, weihua.sheng@okstate.edu, slzhang@zju.edu.cn, liumeiqin@zju.edu.cn

Key Words:  Home service robot, Cloud–, robot knowledge transfer, Model fusion

Qi WANG, Zhen FAN, Weihua SHENG, Senlin ZHANG, Meiqin LIU. Cloud-assisted cognition adaptation for service robots in changing home environments[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(2): 246-257.

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A1 - Qi WANG
A1 - Zhen FAN
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A1 - Meiqin LIU
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DOI - 10.1631/FITEE.2000431

Robots need more intelligence to complete cognitive tasks in home environments. In this paper, we present a new cloud-assisted cognition adaptation mechanism for home service robots, which learns new knowledge from other robots. In this mechanism, a change detection approach is implemented in the robot to detect changes in the user‘s home environment and trigger the adaptation procedure that adapts the robot‘s local customized model to the environmental changes, while the adaptation is achieved by transferring knowledge from the global cloud model to the local model through model fusion. First, three different model fusion methods are proposed to carry out the adaptation procedure, and two key factors of the fusion methods are emphasized. Second, the most suitable model fusion method and its settings for the cloud–;robot knowledge transfer are determined. Third, we carry out a case study of learning in a changing home environment, and the experimental results verify the efficiency and effectiveness of our solutions. The experimental results lead us to propose an empirical guideline of model fusion for the cloud–;robot knowledge transfer.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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