  Artificial visual attention systems aim to support technical systems in
visual tasks by applying the concepts of selective attention observed in humans
and other animals. Such systems are typically evaluated against ground truth
obtained from human gaze-data or manually annotated test images. When applied
to robotics, the systems are required to be adaptable to the target system.
Here, we describe a flexible environment based on a robotic middleware layer
allowing the development and testing of attention-guided vision systems. In
such a framework, the systems can be tested with input from various sources,
different attention algorithms at the core, and diverse subsequent tasks.
