Cognitive Control and Adaptive Attentional Regulations for Robotic Task Execution

We propose a robotic cognitive control framework that exploits supervisory attention and contention scheduling for flexible and adaptive orchestration of structured tasks. Specifically, in the proposed system, top-down and bottom-up attentional processes are exploited to modulate the execution of hierarchical robotic behaviors conciliating goal-oriented and reactive behaviors. In this context, we propose a learning method that allows us to suitably adapt task-based attentional regulations during the execution of structured activities.