<mods:mods version="3.3" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-3.xsd" xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><mods:titleInfo><mods:title>Autonomous learning and reproduction of complex
sequences: a multimodal architecture for
bootstraping imitation games</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">Pierre</mods:namePart><mods:namePart type="family">Andry</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart type="given">Philippe</mods:namePart><mods:namePart type="family">Gaussier</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart type="given">Jacqueline</mods:namePart><mods:namePart type="family">Nadel</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>This paper introduces a control architecture
for the learning of complex sequence of gestures
applied to autonomous robots. The architecture
is designed to exploit the robot internal
sensory-motor dynamics generated by
visual, proprioceptive, and predictive informations
in order to provide intuitive behaviors
in the purpose of natural interactions
with humans.</mods:abstract><mods:classification authority="lcc">Machine Learning</mods:classification><mods:classification authority="lcc">Neural Nets</mods:classification><mods:classification authority="lcc">Robotics</mods:classification><mods:originInfo><mods:dateIssued encoding="iso8061">2005</mods:dateIssued></mods:originInfo><mods:originInfo><mods:publisher>Lund University Cognitive Studies</mods:publisher></mods:originInfo><mods:genre>Conference Paper</mods:genre></mods:mods>