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Abstraction Levels for Robotic Imitation: Overview and Computational Approaches

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Book cover From Motor Learning to Interaction Learning in Robots

Part of the book series: Studies in Computational Intelligence ((SCI,volume 264))

Abstract

This chapter reviews several approaches to the problem of learning by imitation in robotics. We start by describing several cognitive processes identified in the literature as necessary for imitation. We then proceed by surveying different approaches to this problem, placing particular emphasys on methods whereby an agent first learns about its own body dynamics by means of self-exploration and then uses this knowledge about its own body to recognize the actions being performed by other agents. This general approach is related to the motor theory of perception, particularly to the mirror neurons found in primates. We distinguish three fundamental classes of methods, corresponding to three abstraction levels at which imitation can be addressed. As such, the methods surveyed herein exhibit behaviors that range from raw sensory-motor trajectory matching to high-level abstract task replication. We also discuss the impact that knowledge about the world and/or the demonstrator can have on the particular behaviors exhibited.

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Authors and Affiliations

  1. Instituto de Sistemas e Robótica, Instituto Superior Técnico, Lisbon, Portugal

    Manuel Lopes

  2. Carnegie Mellon University, Pittsburgh, PA, USA

    Francisco Melo

  3. Universidad de Zaragoza, Zaragoza, Spain

    Luis Montesano

  4. Instituto de Sistemas e Robótica, Instituto Superior Técnico, Lisbon, Portugal

    José Santos-Victor

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  1. Manuel Lopes
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  2. Francisco Melo
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  3. Luis Montesano
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  4. José Santos-Victor
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  1. Institut des Systèmes Intelligents et de Robotique (CNRS UMR 7222), Université Pierre et Marie Curie Pyramide, Tour 55 Boîte courrier 173, 4 Place Jussieu, 75252, PARIS cedex 05, France

    Olivier Sigaud

  2. Dept. Schölkopf, Max-Planck Institute for Biological Cybernetics, Spemannstraße 38,Rm 223, 72076, Tübingen, Germany

    Jan Peters

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Lopes, M., Melo, F., Montesano, L., Santos-Victor, J. (2010). Abstraction Levels for Robotic Imitation: Overview and Computational Approaches. In: Sigaud, O., Peters, J. (eds) From Motor Learning to Interaction Learning in Robots. Studies in Computational Intelligence, vol 264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05181-4_14

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