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Optimisation of gaze movement for multitasking using rewards

Domestic tasks such as grasping or navigation for robotic systems can be supported by vision. However, the environment provides a vast amount of visual information and concentrating on the information related to the task being undertaken is an important job. Active vision is an approach that provides such a filtering mechanism by using camera movements to bring relevant information into the focus of attention. However timing of gaze shifts (i.e. when to look where) is crucial for cognitive tasks to proceed simultaneously (multitasking). We developed a framework that learns task dependent management of gaze control. We adopted a systems approach where individual visual processes were formalised as modules such as a colour saliency module or object recognition module. Modules may generate motor commands for gaze shifts to acquire visual information relevant to their operation. The system learns how to use its modules (i.e. when to give motor control access to which module) for a task in a reward-based concept. The framework was used in a reaching-while-interacting scenario using the humanoid iCub in a simulation environment.