Self-organisation of Language Instruction for Robot Action

Most current approaches for robot control do not make use of language and ignore neural learning. However, our robot control approach uses language instruction and draws from the concepts of regional distributed modularity, mirror neuron theory and neural assemblies. We describe a self-organising model that clusters action verbs into different locations of the output layer dependent on the body part they are associated with. In doing so we build on our previous work by using actual sensor readings from the MIRA robot that incorporate semantic features of the action verbs. Furthermore, we outline a hierarchical computational model for a neurally inspired self-organising robot action control system using language for instruction.

@InProceedings{EWW03, 
 	 author =  {Elshaw, Mark I. and Wermter, Stefan and Watt, Peter},  
 	 title = {Self-organisation of Language Instruction for Robot Action}, 
 	 booktitle = {Proceedings of the International Joint Conference on Neural Networks},
 	 number = {},
 	 volume = {},
 	 pages = {22--27},
 	 year = {2003},
 	 month = {Jul},
 	 publisher = {IEEE},
 	 doi = {}, 
 }