Cognitive Computation

, Volume 10, Issue 2, pp 307–320 | Cite as

Toward Robot Self-Consciousness (II): Brain-Inspired Robot Bodily Self Model for Self-Recognition

  • Yi ZengEmail author
  • Yuxuan Zhao
  • Jun Bai
  • Bo Xu


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.


Robot self-consciousness Robot bodily self model STDP learning Self-recognition 


Compliance with Ethical Standards

This study was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB02060007), and Beijing Municipal Commission of Science and Technology (Z161100000216124).

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Conflict of interests

All authors declare that they have no conflict of interest.


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghaiChina
  3. 3.University of Chinese Academy of SciencesBeijingChina

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