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Human-robot interaction is becoming an increasingly important research area. In this paper, we present our work on designing a human-robot system with adjustable autonomy and describe not only the prototype interface but also the corresponding robot behaviors. In our approach, we grant the human meta-level control over the level of robot autonomy, but we(More)
Within the field of behavior-based robotics, a useful and popular technique for robot control is that of utilitarian voting, where behaviors representing objectives assign utilities to candidate actions. Utilitarian voting allows for distributed, modular control but is not well suited to systems with very high resolution or several, dependent degrees of(More)
We present a multiple instance learning (MIL) algorithm that learns ellipsoidal decision boundaries with arbitrary covari-ance. In contrast to the fixed-length feature vectors of traditional classification problems, MIL operates on unordered bags of instances. Commonly, each instance is a feature vector , and a bag is considered positive if any one of its(More)
—We present a method for predicting action outcomes in unstructured environments with variable numbers of participants and hidden relationships between them. For example, when pouring flour from a cup into a mixing bowl, important relations must exist between the cup and the bowl. The action P our(x, y) might depend on the precondition Above(x, y). How well(More)
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