A Multimodal Hierarchical Approach to Robot Learning by Imitation

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 hierarchical 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 behaviour. In response we produced a hierarchical 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.

@InProceedings{WEZW04, 
 	 author =  {Weber, Cornelius and Elshaw, Mark I. and Zochios, Alex and Wermter, Stefan},  
 	 title = {A Multimodal Hierarchical Approach to Robot Learning by Imitation}, 
 	 booktitle = {Fourth International Workshop on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems},
 	 number = {},
 	 volume = {},
 	 pages = {131--134},
 	 year = {2004},
 	 month = {},
 	 publisher = {Elsevier},
 	 doi = {}, 
 }