title: A Multimodal Hierarchial Approach to Robot Learning by Imitation
creator: Weber, Cornelius
creator: Elshaw, Mark
creator: Zochios, Alex
creator: Wermter, Stefan
subject: Machine Learning
subject: Neural Nets
subject: Robotics
description: In this paper we propose an approach to robot learning by imitation that uses the multimodal inputs of language, vision and motor. In our approach a student robot learns from a teacher robot how to perform three separate behaviours based on these inputs. We considered two neural architectures for performing this robot learning. First, a one-step hierarchial architecture trained with two different learning approaches either based on Kohonen's self-organising map or based on the Helmholtz machine turns out to be inefficient or not capable of performing differentiated behavior. In response we produced a hierarchial architecture that combines both learning approaches to overcome these problems. In doing so the proposed robot system models specific aspects of learning using concepts of the mirror neuron system (Rizzolatti and Arbib, 1998) with regards to demonstration learning.
publisher: Lund University Cognitive Studies
contributor: Berthouze, Luc
contributor: Kozima, Hideki
contributor: Prince, Christopher G.
contributor: Sandini, Giulio
contributor: Stojanov, Georgi
contributor: Metta, Giorgio
contributor: Balkenius, Christian
date: 2004
type: Conference Paper
type: PeerReviewed
format: application/pdf
identifier: http://cogprints.org/4148/1/weber.pdf
identifier:   Weber, Cornelius and Elshaw, Mark and Zochios, Alex and Wermter, Stefan  (2004) A Multimodal Hierarchial Approach to Robot Learning by Imitation.  [Conference Paper]     
relation: http://cogprints.org/4148/