  Nowadays, robots are dominating the manufacturing, entertainment and
healthcare industries. Robot vision aims to equip robots with the ability to
discover information, understand it and interact with the environment. These
capabilities require an agent to effectively understand object affordances and
functionalities in complex visual domains. In this literature survey, we first
focus on Visual affordances and summarize the state of the art as well as open
problems and research gaps. Specifically, we discuss sub-problems such as
affordance detection, categorization, segmentation and high-level reasoning.
Furthermore, we cover functional scene understanding and the prevalent
functional descriptors used in the literature. The survey also provides
necessary background to the problem, sheds light on its significance and
highlights the existing challenges for affordance and functionality learning.
