Evolved Motor Primitives and Sequences in a Hierarchical Recurrent Neural Network

This study describes how complex goal-directed behavior can evolve in a hierarchically organized recurrent neural network controlling a simulated Khepera robot. Different types of dynamic structures self-organize in the lower and higher levels of a network for the purpose of achieving complex navigation tasks. The parametric bifurcation structures that appear in the lower level explain the mechanism of how behavior primitives are switched in a top-down way. In the higher level, a topologically ordered mapping of initial cell activation states to motor-primitive sequences self-organizes by utilizing the initial sensitivity characteristics of nonlinear dynamical systems. A further experiment tests the evolved controller’s adaptability to changes in its environment. The biological plausibility of the model’s essential principles is discussed.

• Type: Conference
• Year: 2004
• Where: GECCO
• Authors: Rainer W. Paine, Jun Tani
• Keywords: Complex Goal-directed Behavior, GECCO 2004, Parametric Bifurcation Structures, Simulated Khepera Robot