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This paper presents an online algorithm for early detection of anomalies in robot execution, where the anomalies occur in a particular region of the robot's state space. Assuming that a model of normal execution is given, the algorithm detects regions of space where data significantly deviate from normal. It achieves this by focusing optimization over a fixed-parameter family of shapes to find the one among them that is most likely anomalous, and then using this region to decide whether execution is anomalous. Experiments using synthetic and real robot data support the effectiveness of the approach.
Mendoza et al. (Thu,) studied this question.
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