Learning the Dynamic Process of Inhibition and Task Switching in Robotics Cognitive Control

Abstract

Modeling cognitive control is a major issue in robot control, and it is about deciding when a task cannot succeed and a new task need to be initiated. These decisions are induced by incoming stimuli alerting of events taking place while the robot is executing its duties. To learn cognitive control we address the human inspired mechanisms that govern cognitive control and that have been widely studied in neuroscience, namely, shifting and inhibition. Shifting and inhibition are, in fact, executive cognitive functions responding selectively to stimuli, so as to switch from one activity to a more compelling one or to inhibit inappropriate urges and preserve focus on the current task. In an autonomous system these cognitive skills are crucial to assess a well-regulated reactive behavior, which is of particular relevance in critical circumstances. In this paper we illustrate a new method developed for learning shifting and inhibition, based on Gaussian Processes, and using examples provided by skilled operators. We finally show that the learning method is promising and can be seen as a new view for modeling robot reactive and proactive behaviors.