Proprioception and Imitation: On the Road to Agent Individuation View Full Text


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Chapter Info

DATE

2010

AUTHORS

M. Lagarde , P. Andry , P. Gaussier , S. Boucenna , L. Hafemeister

ABSTRACT

In this paper, we will show that different kinds of interactive behaviors can emerge according to the kind of proprioceptive function available in a given sensori-motor system. We will study three different examples. In the first one, an internal proprioceptive signal is available for the learning of the visuo-motor coordination between an arm and a camera. An imitation behavior can emerge when the robot’s eye focuses on the hand of the experimenter instead of its own hand. The imitative behavior results from the error minimization between the visual signal and the proprioceptive signal. In the second example, we will show that similar modifications of the robot’s initial dynamics allows to learn some of the space-time properties of more complex behaviors under the form of a sequence of sensori-motor associations. In the third example, a robot head has to recognize the facial expression of the human caregiver. Yet, the robot has no visual feedback of its own facial expression. The human expressive resonance will allow the robot to select the visual features relevant for a particular facial expression. As a result, after few minutes of interactions, the robot can imitates the facial expression of the human partner. We will show that the different proprioceptive signals used in the examples can be seen as bootstrap mechanisms for more complex interactions. Applied as a crude model of the human, we will propose that these mechanisms play an important role in the process of individuation. More... »

PAGES

43-63

References to SciGraph publications

  • 2010. Learning to Imitate Human Actions through Eigenposes in FROM MOTOR LEARNING TO INTERACTION LEARNING IN ROBOTS
  • 2004-09. Can a robot empathize with people? in ARTIFICIAL LIFE AND ROBOTICS
  • 2007. The Role of Internal Oscillators for the One-Shot Learning of Complex Temporal Sequences in ARTIFICIAL NEURAL NETWORKS – ICANN 2007
  • 2002-10. Resonant spatiotemporal learning in large random recurrent networks in BIOLOGICAL CYBERNETICS
  • 2010. Abstraction Levels for Robotic Imitation: Overview and Computational Approaches in FROM MOTOR LEARNING TO INTERACTION LEARNING IN ROBOTS
  • 2009-07. Learning to search: Functional gradient techniques for imitation learning in AUTONOMOUS ROBOTS
  • 1999. A Neural Structure for Learning by Imitation in ADVANCES IN ARTIFICIAL LIFE
  • 1977-06. Dynamics of pattern formation in lateral-inhibition type neural fields in BIOLOGICAL CYBERNETICS
  • 2004. Toward a Cognitive System Algebra: Application to Facial Expression Learning and Imitation in EMBODIED ARTIFICIAL INTELLIGENCE
  • Book

    TITLE

    From Motor Learning to Interaction Learning in Robots

    ISBN

    978-3-642-05180-7
    978-3-642-05181-4

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-05181-4_3

    DOI

    http://dx.doi.org/10.1007/978-3-642-05181-4_3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1051391752


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    36 schema:description In this paper, we will show that different kinds of interactive behaviors can emerge according to the kind of proprioceptive function available in a given sensori-motor system. We will study three different examples. In the first one, an internal proprioceptive signal is available for the learning of the visuo-motor coordination between an arm and a camera. An imitation behavior can emerge when the robot’s eye focuses on the hand of the experimenter instead of its own hand. The imitative behavior results from the error minimization between the visual signal and the proprioceptive signal. In the second example, we will show that similar modifications of the robot’s initial dynamics allows to learn some of the space-time properties of more complex behaviors under the form of a sequence of sensori-motor associations. In the third example, a robot head has to recognize the facial expression of the human caregiver. Yet, the robot has no visual feedback of its own facial expression. The human expressive resonance will allow the robot to select the visual features relevant for a particular facial expression. As a result, after few minutes of interactions, the robot can imitates the facial expression of the human partner. We will show that the different proprioceptive signals used in the examples can be seen as bootstrap mechanisms for more complex interactions. Applied as a crude model of the human, we will propose that these mechanisms play an important role in the process of individuation.
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