Learning and generalization of complex tasks from unstructured demonstrations

@article{Niekum2012LearningAG,
  title={Learning and generalization of complex tasks from unstructured demonstrations},
  author={Scott Niekum and Sarah Osentoski and George Konidaris and Andrew G. Barto},
  journal={2012 IEEE/RSJ International Conference on Intelligent Robots and Systems},
  year={2012},
  pages={5239-5246}
}
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… CONTINUE READING
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