Abstract
A biologically inspired mechanism for robot action selection, based on the vertebrate basal ganglia, has been previously presented (Prescott et al. 2006, Montes Gonzalez et al. 2000). In this model the task confronting the robot is decomposed into distinct behavioural modules that integrate information from multiple sensors and internal state to form ‘salience’ signals. These signals are provided as inputs to a computational model of the basal ganglia whose intrinsic processes cause the selection by disinhibition of a winning behaviour. This winner is then allowed access to the motor plant whilst losing behaviours are suppressed. In previous research we have focused on the development of this biomimetic selection architecture, and have therefore used behavioural modules that were hand-coded as algorithmic procedures. In the current article, we demonstrate the use of genetic algorithms and gradient–descent learning to automatically generate/tune some of the modules that generate the model behaviour.