(Colorado State University. Libraries, 2023) Tomar, Aniket, author; Krishnaswamy, Nikhil, advisor; Blanchard, Nathaniel, committee member; Clegg, Benjamin, committee member
Object affordance understanding is an important open problem in AI and robotics. Gibsonian affordances of an object are actions afforded due to its physical structure and can be directly perceived by agents. A telic affordance is an action that is conventionalized due to an object's typical use or purpose. This work explores the extent to which a 3D CNN analogue can infer grasp affordances from only 3D shape information. This experiment was designed as a grasp classification task for 3D meshes of common kitchen objects with labels derived from human annotations. 3D shape information was found to be insufficient for current models to learn telic affordances, even though they are successful at shape classification and Gibsonian affordance learning. This was investigated further by training a classifier to predict the telic grasps directly from the human annotations to a higher accuracy indicating that the information required for successful classification existed in the dataset but was not effectively utilized. Finally, the embedding spaces of the two classifiers were compared and found to have no significant correspondence between them. This work hypothesizes that this is due to the two models capturing fundamentally different distributions of affordances with respect to objects, one representing Gibsonian affordances or shape information, and the other, telic affordances
