![]() ![]() ![]() Our contributions to the state of the art are manifold. This information is fed into our intention recognition component where the most likely object and action pair that the user desires is determined. ![]() For scene understanding, the system is responsible for segmenting objects from captured RGB-D data, determining their positions and orientations in space, and acquiring their category labels. Accordingly, the system is partitioned into scene understanding and intention recognition modules. The aim of this system is to reduce the amount of human interactions necessary for communicating tasks to a robot. As a result, we present in this dissertation a novel scene-dependent human-robot collaborative system capable of recognizing and learning human intentions based on scene objects, the actions that can be performed on them, and human interaction history. In order for assistive robots to collaborate effectively with humans for completing everyday tasks, they must be endowed with the ability to effectively perceive scenes and more importantly, recognize human intentions. ![]()
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