“ The affordances of the environment are what it offers the animal, what it provides or furnishes… It implies the complementarity of the animal and the environment.” – James J. Gibson, 1979
Every object in our world has its own and discrete characteristics that derived from its visual properties and physical attributes. These features are effective to recognize objects and to classify them into different categories. Thus, they are widely being used by vision recognition systems in the research area. However, these properties are unable to indicate how they can be used by a human. Visual and physical properties are not able to provide any clue about the set of potential actions that can be performed in a human – object interaction.
Affordances describe a possible set of actions that an environment allows to an actor [2]. Thus, dissimilar to the aforementioned object attributes that paint the picture of the object as an independent entity, affordances are capable to imply functional interactions of object parts with humans. In the context of robotic vision, taking into account object affordances is vitally important in order to create efficient autonomous robots that are able to interact with objects and to assist humans in daily tasks.
Affordances can be used in a big variety of applications. Among others, affordances are useful to anticipate and predict future actions because they represent the set of possible actions that may be performed by a human being. Additionally, by defining a set of possible and potential actions affordances provide beneficial clues not only for efficient activity recognition but also for functionality validation of objects. Last but not least affordances can be characterized as an intuition about objects’ value and significance leading to enhance scene understanding and traditional object recognition approaches.
In conclusion, affordances are powerful. They provide those details needed to make computer vision systems able to imitate humans’ object recognition system. Evermore, affordances provide a very effective and unique combination of features that seems to be able to enhance almost every computer vision system.
Illustration 1: Affordances Examples [1]
[1] Zhao, Xue & Cao, Yang & Kang, Yu. (2020). Object affordance detection with relationship-aware network. Neural Computing and Applications.
[2] Mohammed Hassanin, Salman Khan, and Murat Tahtali. 2021. Visual Affordance and Function Understanding: A Survey.
