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A Multimodal Hierarchial Approach to Robot Learning by Imitation

Weber, Cornelius and Elshaw, Mark and Zochios, Alex and Wermter, Stefan (2004) A Multimodal Hierarchial Approach to Robot Learning by Imitation. [Conference Paper]

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Abstract

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.

Item Type:Conference Paper
Keywords:multimodal imitation learning, hierarchial neural architecture, mirror neuron system, robotic simulator, MirrorBot
Subjects:Computer Science > Machine Learning
Computer Science > Neural Nets
Computer Science > Robotics
ID Code:4148
Deposited By:Prince, Dr Christopher G.
Deposited On:14 Apr 2005
Last Modified:11 Mar 2011 08:55

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