Model-based imitation learning by probabilistic trajectory matching

@article{Englert2013ModelbasedIL,
  title={Model-based imitation learning by probabilistic trajectory matching},
  author={Peter Englert and Alexandros Paraschos and Jan Peters and Marc Peter Deisenroth},
  journal={2013 IEEE International Conference on Robotics and Automation},
  year={2013},
  pages={1922-1927}
}
One of the most elegant ways of teaching new skills to robots is to provide demonstrations of a task and let the robot imitate this behavior. Such imitation learning is a non-trivial task: Different anatomies of robot and teacher, and reduced robustness towards changes in the control task are two major difficulties in imitation learning. We present an imitation-learning approach to efficiently learn a task from expert demonstrations. Instead of finding policies indirectly, either via state… CONTINUE READING
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