title: A Developmental Approach for low-level Imitations
creator: Andry, Pierre
creator: Gaussier, Philippe
creator: Nadel, Jacqueline
creator: Courant, Michele
subject: Machine Learning
subject: Neural Nets
subject: Robotics
description: Historically, a lot of authors in psychology and in robotics tend to separate "true imitation" and its related high-level mechanisms which seem to be exclusive to human adult, from low-level imitations or "mimicries" observed on babies or primates. Closely, classical researches suppose that an imitative artificial system must be able to build a model of the demonstrator's geometry, in order to reproduce finely the movements on each joints. Conversely, we will advocate that if imitation is viewed as a part of a developmental course, then (1) an artificial developing system does not need to build any internal model of the other, to perform real-time and low-level imitations of human movements despite the related correspondence problem between man and robot and, (2) a simple sensory-motor loop could be at the basis of multiples heterogeneous imitative behaviors often explained in the literature by different models.
publisher: Lund University Cognitive Studies
contributor: Prince, Christopher G.
contributor: Berthouze, Luc
contributor: Kozima, Hideki
contributor: Bullock, Daniel
contributor: Stojanov, Georgi
contributor: Balkenius, Christian
date: 2003
type: Conference Poster
type: PeerReviewed
format: application/pdf
identifier: http://cogprints.org/3343/1/Andry.pdf
identifier:   Andry, Pierre and Gaussier, Philippe and Nadel, Jacqueline and Courant, Michele  (2003) A Developmental Approach for low-level Imitations.  [Conference Poster]     
relation: http://cogprints.org/3343/