Self-organisation of language instruction for robot action control

Abstract

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 described 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 sensor reading from the MIRA robot that incorporate semantic features of the actions verbs. Furthermore, we outline a hierarchical computational model for a neurally inspired self-organising robot action control system using language for instruction.