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@INPROCEEDINGS{Havoutis_SSRR2016_2016,
         author = {Havoutis, I. and Calinon, Sylvain},
       keywords = {assistive robotics, Learning by demonstration, shared control, teleoperation},
       projects = {Idiap},
          month = oct,
          title = {Learning assistive teleoperation behaviors from demonstration},
      booktitle = {Proc. IEEE International Symposium on Safety, Security and Rescue Robotics},
           year = {2016},
          pages = {258-263},
       abstract = {Emergency response in hostile environments often
involves remotely operated vehicles (ROVs) that are teleoperated
as interaction with the environment is typically required. Many
ROV tasks are common to such scenarios and are often recurrent.
We show how a probabilistic approach can be used to learn a
task behavior model from data. Such a model can then be used to
assist an operator performing the same task in future missions.
We show how this approach can capture behaviors (constraints)
that are present in the training data, and how this model can
be combined with the operator’s input online. We present an
illustrative planar example and elaborate with a non-Destructive
testing (NDT) scanning task on a teleoperation mock-up using a
two-armed Baxter robot. We demonstrate how our approach can
learn from examples task specific behaviors and automatically
control the overall system, combining the operator’s input and the
learned model online, in an assistive teleoperation manner. This
can potentially reduce the time and effort required to perform
teleoperation tasks that are commonplace to ROV missions in
the context of security, maintenance and rescue robotics.},
            pdf = {https://publications.idiap.ch/attachments/papers/2016/Havoutis_SSRR2016_2016.pdf}
}