| 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
| |
| Source Publication | COGNITIVE COMPUTATION
 |
| 2018-04-01 | |
| Volume | 10Issue:2Pages:307-320 |
| Subtype | Article |
| Abstract | The 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. |
| Keyword | Robot Self-consciousness Robot Bodily Self Model Stdp Learning Self-recognition |
| WOS Headings | Science & Technology ; Technology ; Life Sciences & Biomedicine |
| DOI | 10.1007/s12559-017-9505-1 |
| WOS Keyword | MIRROR NEURON SYSTEM ; FACE RECOGNITION ; OBJECT RECOGNITION ; SCHIZOPHRENIA ; METAANALYSIS ; DYSFUNCTION ; MOTION ; FMRI ; BODY ; PERCEPTION |
| Indexed By | SCI ; SSCI |
| Language | 英语 |
| Funding Organization | Strategic Priority Research Program of the Chinese Academy of Sciences(XDB02060007) ; Beijing Municipal Commission of Science and Technology(Z161100000216124) |
| WOS Research Area | Computer Science ; Neurosciences & Neurology |
| WOS Subject | Computer Science, Artificial Intelligence ; Neurosciences |
| WOS ID | WOS:000430190600011 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/15380 |
| Collection | 类脑智能研究中心 |
| Affiliation | 1.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 Affilication | Institute 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-SA | View Download | |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment