  A key capability required by service robots operating in real-world, dynamic
environments is that of Visual Intelligence, i.e., the ability to use their
vision system, reasoning components and background knowledge to make sense of
their environment. In this paper, we analyze the epistemic requirements for
Visual Intelligence, both in a top-down fashion, using existing frameworks for
human-like Visual Intelligence in the literature, and from the bottom up, based
on the errors emerging from object recognition trials in a real-world robotic
scenario. Finally, we use these requirements to evaluate current knowledge
bases for Service Robotics and to identify gaps in the support they provide for
Visual Intelligence. These gaps provide the basis of a research agenda for
developing more effective knowledge representations for Visual Intelligence.
