Functional 3D Object Classification Using Simulation of Embodied Agent

This paper presents a cognitive-motivated approach for classification of 3D objects according to the functional paradigm. We hypothesize that classification can be achieved through simulation of actions meant to verify whether a candidate object fulfills a certain functionality. This paper presents ABSV: Agent Based Simulated Vision, a novel approach that tries to imitate the way humans perform certain classification tasks. ABSV can determine the category of a candidate object by verifying certain functional properties that the object should possess. Unlike conventional functional approaches, it uses virtual environment to simulate the interaction between the object and various examination agents to expose those functionalities. To demonstrate our approach we have implemented it for the recognition of several object categories. We achieved promising classification results using both complete CAD models and real 3D scanned data generated from a single view point. We believe that the concepts introduced in ABSV will influence significantly the design of robot classification systems.