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• W2022760091 abstract "We present a novel method for segmenting demonstrations, recognizing repeated skills, and generalizing complex tasks from unstructured demonstrations. This method combines many of the advantages of recent automatic segmentation methods for learning from demonstration into a single principled, integrated framework. Specifically, we use the Beta Process Autoregressive Hidden Markov Model and Dynamic Movement Primitives to learn and generalize a multi-step task on the PR2 mobile manipulator and to demonstrate the potential of our framework to learn a large library of skills over time." @default.
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• W2022760091 date "2012-10-01" @default.
• W2022760091 modified "2023-10-13" @default.
• W2022760091 title "Learning and generalization of complex tasks from unstructured demonstrations" @default.
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• W2022760091 doi "https://doi.org/10.1109/iros.2012.6386006" @default.
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