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

Qi WANG; Zhen FAN; Weihua SHENG; Senlin ZHANG; Meiqin LIU

doi:10.1631/FITEE.2000431

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

Regular Papers | Updated：2022-04-19

- Cloud-assisted cognition adaptation for service robots in changing home environments
  Enhanced Publication
- 面向变化用户家居环境的服务机器人云辅助认知适应
- Frontiers of Information Technology & Electronic Engineering Vol. 23, Issue 2, Pages: 246-257(2022)
- Affiliations：
  
  1.College of Electrical Engineering, Zhejiang University, Hangzhou310027, China
  2.School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK74078, USA
  3.Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an710049, China
- Author bio：
  
  E-mail: wang9562@zju.edu.cn;
  E-mail: fanzhen@zju.edu.cn;
  E-mail: weihua.sheng@okstate.edu;
  E-mail: slzhang@zju.edu.cn;
  ‡Corresponding author
- Funds：
- DOI：10.1631/FITEE.2000431
  CLC： TP242.6
- Received：26 August 2020，
  
  Accepted：10 January 2021，
  
  Published：0 February 2022
- Accepted：
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Qi WANG, Zhen FAN, Weihua SHENG, et al. Cloud-assisted cognition adaptation for service robots in changing home environments[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(2): 246-257.
DOI：

Qi WANG, Zhen FAN, Weihua SHENG, et al. Cloud-assisted cognition adaptation for service robots in changing home environments[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(2): 246-257. DOI： 10.1631/FITEE.2000431.

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。

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.

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references

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