Towards Robot Self-consciousness (I): Brain-Inspired Robot Mirror Neuron System Model and Its Application in Mirror Self-recognition

Conference paper

DOI: 10.1007/978-3-319-49685-6_2

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10023)
Cite this paper as:
Zeng Y., Zhao Y., Bai J. (2016) Towards Robot Self-consciousness (I): Brain-Inspired Robot Mirror Neuron System Model and Its Application in Mirror Self-recognition. In: Liu CL., Hussain A., Luo B., Tan K., Zeng Y., Zhang Z. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2016. Lecture Notes in Computer Science, vol 10023. Springer, Cham

Abstract

Mirror Self-Recognition is a well accepted test to identify whether an animal is with self-consciousness. Mirror neuron system is believed to be one of the most important biological foundation for Mirror Self-Recognition. Inspired by the biological mirror neuron system of the mammalian brain, we propose a Brain-inspired Robot Mirror Neuron System Model (Robot-MNS-Model) and we apply it to humanoid robots for mirror self-recognition. This model evaluates the similarity between the actual movements of robots and their visual perceptions. The association for self-recognition is supported by STDP learning which connects the correlated visual perception and motor control. The model is evaluated on self-recognition mirror test for 3 humanoid robots. Each robot has to decide which one is itself after a series of random movements facing a mirror. The results show that with the proposed model, multiple robots can pass the self-recognition mirror test at the same time, which is a step forward towards robot self-consciousness.

Keywords

Robot self-consciousness Mirror self-recognition Mirror neuron system Associative learning 

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Institute of Automation, Chinese Academy of SciencesBeijingChina
  2. 2.Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghaiChina

Personalised recommendations