Robot trajectory prediction and recognition based on a computational mirror neurons model

Junpei Zhong, Cornelius Weber, Stefan Wermter

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Mirror neurons are premotor neurons that are considered to play a role in goal-directed actions, action understanding and even social cognition. As one of the promising research areas in psychology, cognitive neuroscience and cognitive physiology, understanding mirror neurons in a social cognition context, whether with neural or computational models, is still an open issue [5]. In this paper, we mainly focus on the action understanding aspect of mirror neurons, which can be regarded as a fundamental function of social cooperation and social cognition. Our proposed initial architecture is to learn a simulation of the walking pattern of a humanoid robot and to predict where the robot is heading on the basis of its previous walking trajectory.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning, ICANN 2011 - 21st International Conference on Artificial Neural Networks, Proceedings
Pages333-340
Number of pages8
Volume6792 LNCS
EditionPART 2
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event21st International Conference on Artificial Neural Networks, ICANN 2011 - Espoo
Duration: 2011 Jun 142011 Jun 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6792 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other21st International Conference on Artificial Neural Networks, ICANN 2011
CityEspoo
Period11/6/1411/6/17

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Keywords

  • Mirror Neurons
  • Parametric Bias
  • Recurrent Neural Network
  • Robot Walking Pattern

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Zhong, J., Weber, C., & Wermter, S. (2011). Robot trajectory prediction and recognition based on a computational mirror neurons model. In Artificial Neural Networks and Machine Learning, ICANN 2011 - 21st International Conference on Artificial Neural Networks, Proceedings (PART 2 ed., Vol. 6792 LNCS, pp. 333-340). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6792 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-21738-8_43