Bielefeld University Faculty of Linguistics and Literature Faculty of Technology

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Abstract

Natural human-robot interaction (HRI) is a key feature of mobile robot companions collaborating with humans. To achieve natural HRI, multiple communication modalities like vision, speech, and gestures have to be utilized. Besides, capabilities to emulate cognitive processes, e.g., object learning and object recognition, are essential. In this work we present a new approach to interactive object learning enabling multi-view object representation. To overcome a robot's limitation of having only one view point, we make use of an iconic memory consisting of previously acquired images. As the relevant scene area is unknown during construction of the iconic memory, a representation in the form of mosaic images is applied. The relevant image patches describing an object referenced by the user are selected through an object attention mechanism. The resulting multi-view object representations improve the robustness and flexibility of our interactive approach for object learning.

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