Full Text:   <1956>

Summary:  <140>

CLC number: TP242.6

On-line Access: 2022-02-28

Received: 2020-08-26

Revision Accepted: 2022-04-22

Crosschecked: 2021-01-10

Cited: 0

Clicked: 3224

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Qi WANG

https://orcid.org/0000-0003-4231-860X

Meiqin LIU

https://orcid.org/0000-0003-0693-6574

-   Go to

Article info.
Open peer comments

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

http://doi.org/10.1631/FITEE.2000431


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.

@article{title="Cloud-assisted cognition adaptation for service robots in changing home environments",
author="Qi WANG, Zhen FAN, Weihua SHENG, Senlin ZHANG, Meiqin LIU",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="2",
pages="246-257",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000431"
}

%0 Journal Article
%T Cloud-assisted cognition adaptation for service robots in changing home environments
%A Qi WANG
%A Zhen FAN
%A Weihua SHENG
%A Senlin ZHANG
%A Meiqin LIU
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 2
%P 246-257
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000431

TY - JOUR
T1 - Cloud-assisted cognition adaptation for service robots in changing home environments
A1 - Qi WANG
A1 - Zhen FAN
A1 - Weihua SHENG
A1 - Senlin ZHANG
A1 - Meiqin LIU
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 2
SP - 246
EP - 257
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000431


Abstract: 
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.

面向变化用户家居环境的服务机器人云辅助认知适应

王祺1,樊臻1,盛卫华2,张森林1,刘妹琴1,3
1浙江大学电气工程学院,中国杭州市,310027
2俄克拉荷马州立大学电气与计算机工程学院,美国俄克拉荷马州斯蒂尔沃特,74078
3西安交通大学人工智能与机器人研究院,中国西安市,710049
摘要:机器人需要更强的智能以胜任家居环境中的认知任务。本文提出一种新的云辅助家居服务机器人认知适应机制,它可以从其他机器人处学习新知识。在该机制中,在机器人处部署一种变化检测方法以检测用户家居环境变化,并触发认知适应过程,实现经云端从其他机器人处学习新知识。而认知适应是通过模型融合方法将知识从云端全局模型迁移至机器人本地模型得以实现。首先,提出3种不同模型融合方法执行认知适应过程,并给出影响模型融合方法的两个关键因素。其次,确定最适合云端至机器人知识转移的模型融合方法及其设置。再次,在一个变化的用户家居环境中进行案例研究,,实验结果验证了所提方案的效率和有效性。基于实验结果,提出一种云端至机器人知识转移模型融合的经验准则。

关键词:家居服务机器人;云端至机器人知识迁移;模型融合

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

Reference

[1]Arumugam R, Enti VR, Liu BB, et al., 2010. DAvinCi: a cloud computing framework for service robots. IEEE Int Conf on Robotics and Automation, p.3084-3089. doi: 10.1109/ROBOT.2010.5509469

[2]Baena-García M, del Campo-Ávila J, Fidalgo R, et al., 2006. Early drift detection method. Proc 4th Int Workshop on Knowledge Discovery from Data Streams, p.77-86.

[3]Disabato S, Roveri M, 2019. Learning convolutional neural networks in presence of concept drift. Int Joint Conf on Neural Networks, p.1-8. doi: 10.1109/IJCNN.2019.8851731

[4]Ditzler G, Roveri M, Alippi C, et al., 2015. Learning in nonstationary environments: a survey. IEEE Comput Intell Mag, 10(4):12-25. doi: 10.1109/MCI.2015.2471196

[5]Fachantidis N, Dimitriou AG, Pliasa S, et al., 2018. Android OS mobile technologies meets robotics for expandable, exchangeable, reconfigurable, educational, stem-enhancing, socializing robot. Interactive Mobile Communication Technologies and Learning, p.487-497. doi: 10.1007/978-3-319-75175-7_48

[6]Gama J, Medas P, Castillo G, et al., 2004. Learning with drift detection. Proc 17th Brazilian Symp on Artificial Intelligence, p.286-295. doi: 10.1007/978-3-540-28645-5_29

[7]Gouveia BD, Portugal D, Silva DC, et al., 2015. Computation sharing in distributed robotic systems: a case study on SLAM. IEEE Trans Autom Sci Eng, 12(2):410-422. doi: 10.1109/TASE.2014.2357216

[8]Graf B, Staab H, 2009. Service robots and automation for the disabled/limited. In: Nof SY (Ed.), Springer Handbook of Automation. Springer Berlin Heidelberg, p.1485-1502. doi: 10.1007/978-3-540-78831-7_84

[9]Huaimin W, Bo D, Xu J, 2018. Cloud robotics: a distributed computing view. In: Jones C, Wang J, Zhan NJ (Eds.), Symp on Real-Time and Hybrid Systems. Springer, Cham, p.231-245. doi: 10.1007/978-3-030-01461-2_12

[10]Hunziker D, Gajamohan M, Waibel M, et al., 2013. Rapyuta: the RoboEarth cloud engine. IEEE Int Conf on Robotics and Automation, p.438-444. doi: 10.1109/ICRA.2013.6630612

[11]Kuncheva LI, 2013. Change detection in streaming multivariate data using likelihood detectors. IEEE Trans Knowl Data Eng, 25(5):1175-1180. doi: 10.1109/TKDE.2011.226

[12]LeCun Y, Bengio Y, Hinton G, 2015. Deep learning. Nature, 521(7553):436-444. doi: 10.1038/nature14539

[13]McMahan HB, Moore E, Ramage D, et al., 2017. Communication-efficient learning of deep networks from decentralized data. https://arxiv.org/abs/1602.05629

[14]Page ES, 1954. Continuous inspection schemes. Biometrika, 41(1-2):100-115. doi: 10.1093/biomet/41.1-2.100

[15]Pedregosa F, Varoquaux G, Gramfort A, et al., 2011. Scikit-learn: machine learning in Python. J Mach Learn Res, 12:2825-2830.

[16]Secker J, Hill R, Villeneau L, et al., 2003. Promoting independence: but promoting what and how? Ageing Soc, 23(3):375-391. doi: 10.1017/S0144686X03001193

[17]Shuai W, Chen XP, 2019. KeJia: towards an autonomous service robot with tolerance of unexpected environmental changes. Front Inform Technol Electron Eng, 20(3):307-317. doi: 10.1631/FITEE.1900096

[18]Siciliano B, Khatib O, 2016. Springer Handbook of Robotics. Springer Berlin Heidelberg, Germany.

[19]Simonyan K, Zisserman A, 2014. Very deep convolutional networks for large-scale image recognition. https://arxiv.org/abs/1409.1556

[20]TensorFlow, 2020. TensorFlow-Slim Image Classification Model Library. https://github.com/tensorflow/models/tree/master/research/slim

[21]United Nations, 2020. Ageing. https://www.un.org/en/sections/issues-depth/ageing/

[22]Wang Q, Zhang S, Sheng W, et al., 2021. Multi-style learning for adaptation of perception intelligence in home service robots. Patt Recogn Lett, 151:243-251.

[23]Yang Z, Al-Dahidi S, Baraldi P, et al., 2020. A novel concept drift detection method for incremental learning in nonstationary environments. IEEE Trans Neur Netw Learn Syst, 31(1):309-320. doi: 10.1109/TNNLS.2019.2900956

[24]Yoon J, Jeong W, Lee G, et al., 2020. Federated continual learning with adaptive parameter communication. https://arxiv.org/abs/2003.03196v1

[25]Yosinski J, Clune J, Bengio Y, et al., 2014. How transferable are features in deep neural networks? https://arxiv.org/abs/1411.1792v1

[26]Žliobaitė I, 2010. Learning under concept drift: an overview. https://arxiv.org/abs/1010.4784

[27]Žliobaitė I, Pechenizkiy M, Gama J, 2016. An overview of concept drift applications. In: Japkowicz N, Stefanowski J (Eds.), Big Data Analysis: New Algorithms for a New Society. Springer, Cham, p.91-114. doi: 10.1007/978-3-319-26989-4_4

[28]Zweigle O, van de Molengraft R, d’Andrea R, et al., 2009. RoboEarth: connecting robots worldwide. Proc 2nd Int Conf on Interaction Sciences: Information Technology, p.184-191. doi: 10.1145/1655925.1655958

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2022 Journal of Zhejiang University-SCIENCE