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Robots have finally escaped from industrial workplaces and made their way into our homes, offices and public spaces. In order to realize the dream of robot assistants performing human tasks together with humans in a seamless fashion, we need to provide them with the fundamental capability of understanding complex and unstructured environments. In this talk, we will provide an overview of our recent work on semantic spatial understanding and exploiting semantic knowledge for generating more informed and efficient robot behavior in human environments. We will start by presenting our spatial knowledge modeling framework and continue with methods of acquiring semantic world descriptions, abstracting and reasoning about object locations, topology and segments of space. Finally, we will show that semantic knowledge can indeed improve performance of a robot on the task of large-scale object search and make the robot's behavior much more intuitive and human-like.
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