Semantic Representation of Objects and Function
نویسندگان
چکیده
Natural human activities involve using and manipulating objects, often several at a time, simultaneously and continuously. For example, changing lanes while driving requires using a steering wheel and pedals, all while observing the road and making sure that lanes are clear. Not only are we recognizing these objects, but we know what to do with them (i.e., we can perceive object affordances). We use our imagination and invoke mental simulations to construct variations of objects and actions to learn these affordances. Learning how to use objects is a highly desirable skill for robots, as well. Unfortunately, although robots are proficient at recognizing object features, they are less-skilled at recognizing what can be done with these objects. In this paper, we sketch a novel approach based on Dempster-Shafer (DS) theory [1] and uncertain logic for inferring object affordances using a mental simulation framework. As part of this effort, we are in the process of developing a computational model for affordance that can represent complicated activities and can account for the dynamic and continuous nature of real-world scenarios.
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