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  • Conference paper
    Demiris Y, 2002,

    Mirror neurons, imitation and the learning of movement sequences

    , Singapore, 9th international conference on neural information processing (ICONIP), Singapore, Singapore, 18 - 22 November 2002, Publisher: Nanyang Technological Univ, Pages: 111-115

    We draw inspiration from properties of "mirror" neurons discovered in the macaque monkey brain area F5, to design and implement a distributed behaviour-based architecture that equips robots with movement imitation abilities. We combine this generative route with a learning route, and demonstrate how new composite behaviours that exhibit mirror neuron like properties can be learned from demonstration.

  • Book chapter
    Demiris Y, Hayes G, 2002,

    Imitation as a dual-route process featuring predictive and learning components: a biologically plausible computational model

    , Imitation in animals and artifacts, Editors: Dautenhahn, Nehaniv, Cambridge, Massachussetts, Publisher: MIT Press, Pages: 327-361, ISBN: 9780262042031
  • Conference paper
    Demiris Y, 2002,

    Biologically inspired robot imitation mechanisms and their application as models of mirror neurons

    , Proceedings of EPSRC/BBSRC workshop on biologically inspired robotics, Pages: 126-133
  • Book
    Balkenius C, Prince C, Demiris Y, Marom Y, Kozima Het al., 2001,

    Proceedings of the first international workshop on epigenetic robotics: modeling cognitive development in robotic systems

    , Lund, Publisher: Lund University, ISBN: 9789163114656
  • Conference paper
    , 2000,

    Advances in Robot Learning, 8th European Workshop on Learning Robots, EWLR-8, Lausanne, Switzerland, September 18, 1999, Proceedings

    , Publisher: Springer
  • Conference paper
    , 1998,

    Learning Robots, 6th European Workshop, EWLR-6, Brighton, England, UK, August 1-2, 1997, Proceedings

    , Publisher: Springer
  • Conference paper
    Demiris Y, Hayes G, 1997,

    Do Robots Ape?

    , AAAI Fall Symposium on Socially Intelligent Agents, Publisher: AAAI, Pages: 28-30

    Within the context of two sets of robotic experiments we have performed, we examine some representational and algorithmic issues that need to be addressed in order to equip robots with the capacity to imitate. We suggest that some of the di culties might be eased by placing imitation architectures within a wider social context.

  • Journal article
    Klingspor V, Demiris Y, Kaiser M, 1997,

    Human Robot communication and Machine Learning

    , Applied Artificial Intelligence: an international journal, Vol: 11, Pages: 719-746
  • Journal article
    Klingspor V, Demiris Y, Kaiser M, 1997,

    Human Robot Communication and Machine Learning

    , Applied Artificial Intelligence, Vol: 11, Pages: 719-746

    Human-Robot Interaction and especially Human-Robot Communication (HRC) is of primary importance for the development of robots that operate outside production lines and cooperate with humans. In this paper, we review the state of the art and discuss two complementary aspects of the role machine learning plays in HRC. First, we show how communication itself can benefit from learning, e.g. by building human-understandable symbols from a robot’s perceptions and actions. Second, we investigate the power of non-verbal communication and imitation learning mechanisms for robot programming.

  • Conference paper
    DEMIRIS J, 1994,

    EXPERIMENTS TOWARDS ROBOTIC LEARNING BY IMITATION

    , 12th National Conference on Artificial Intelligence, Publisher: M I T PRESS, Pages: 1439-1439

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