CASIA OpenIR  > 类脑智能研究中心  > 类脑认知计算
Toward Robot Self-Consciousness (II): Brain-Inspired Robot Bodily Self Model for Self-Recognition
Zeng, Yi1,2,3; Zhao, Yuxuan1; Bai, Jun1; Xu, Bo1,2,3
AbstractThe neural correlates and nature of self-consciousness is an advanced topic in Cognitive Neuroscience. Only a few animal species have been testified to be with this cognitive ability. From artificial intelligence and robotics point of view, few efforts are deeply rooted in the neural correlates and brain mechanisms of biological self-consciousness. Despite the fact that the scientific understanding of biological self-consciousness is still in preliminary stage, we make our efforts to integrate and adopt known biological findings of self-consciousness to build a brain-inspired model for robot self-consciousness. In this paper, we propose a brain-inspired robot bodily self model based on extensions to primate mirror neuron system and apply it to humanoid robot for self recognition. In this model, the robot firstly learns the correlations between self-generated actions and visual feedbacks in motion by learning with spike timing dependent plasticity (STDP), and then learns the appearance of body part with the expectation that the visual feedback is consistent with its motion. Based on this model, the robot uses multisensory integration to learn its own body in real world and in mirror. Then it can distinguish itself from others. In a mirror test setting with three robots with the same appearance, with the proposed brain-inspired robot bodily self model, each of them can recognize itself in the mirror after these robots make random movements at the same time. The theoretic modeling and experimental validations indicate that the brain-inspired robot bodily self model is biologically inspired, and computationally feasible as a foundation for robot self recognition.
KeywordRobot Self-consciousness Robot Bodily Self Model Stdp Learning Self-recognition
WOS HeadingsScience & Technology ; Technology ; Life Sciences & Biomedicine
Indexed BySCI ; SSCI
Funding OrganizationStrategic Priority Research Program of the Chinese Academy of Sciences(XDB02060007) ; Beijing Municipal Commission of Science and Technology(Z161100000216124)
WOS Research AreaComputer Science ; Neurosciences & Neurology
WOS SubjectComputer Science, Artificial Intelligence ; Neurosciences
WOS IDWOS:000430190600011
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
2.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zeng, Yi,Zhao, Yuxuan,Bai, Jun,et al. Toward Robot Self-Consciousness (II): Brain-Inspired Robot Bodily Self Model for Self-Recognition[J]. COGNITIVE COMPUTATION,2018,10(2):307-320.
APA Zeng, Yi,Zhao, Yuxuan,Bai, Jun,&Xu, Bo.(2018).Toward Robot Self-Consciousness (II): Brain-Inspired Robot Bodily Self Model for Self-Recognition.COGNITIVE COMPUTATION,10(2),307-320.
MLA Zeng, Yi,et al."Toward Robot Self-Consciousness (II): Brain-Inspired Robot Bodily Self Model for Self-Recognition".COGNITIVE COMPUTATION 10.2(2018):307-320.
Files in This Item: Download All
File Name/Size DocType Version Access License
Toward Robot Self-Co(2959KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zeng, Yi]'s Articles
[Zhao, Yuxuan]'s Articles
[Bai, Jun]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zeng, Yi]'s Articles
[Zhao, Yuxuan]'s Articles
[Bai, Jun]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zeng, Yi]'s Articles
[Zhao, Yuxuan]'s Articles
[Bai, Jun]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Toward Robot Self-Consciousness Brain-Inspired.pdf
Format: Adobe PDF
This file does not support browsing at this time
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.