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Generative models are widely used for unsupervised learning with various applications, including data compression and signal restoration. Training methods for such systems focus on the generality of the network given limited amount of training data. A less researched type of techniques concerns generation of only a single type of input. This is useful for applications such as constraint handling,...
Spatio-temporal anomaly detection by unsupervised learning have applications in a wide range of practical settings. In this paper we present a surveillance system for industrial robots using a monocular camera. We propose a new unsupervised learning method to train a deep feature extractor from unlabeled images. Without any data augmentation, the algorithm co-learns the network parameters on different...
The goal of this work is to teach a robot to regrasp an object using knowledge obtained from human demonstration. This paper presents a task model that represents a human regrasping movement. The task model is based on the topological information and comprised of four task primitives. Human regrasping movement is recognised and represented as a sequence of these task primitives by the proposed recognition...
This paper introduces a novel method to teach a robot to regrasp an object based on the Programming by Demonstration paradigm. In this paradigm, a robot observes a human performing a regrasping task via various sensors to recognise crucial information in order to reproduce the task using its own hand. The main contribution is in the proposal of a representation technique that can analyse a human regrasping...
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