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@ARTICLE{Jankowski_RA-L_2022,
         author = {Jankowski, Julius and Racca, Mattia and Calinon, Sylvain},
       projects = {Idiap},
          month = jan,
          title = {From Key Positions to Optimal Basis Functions for Probabilistic Adaptive Control},
        journal = {IEEE Robotics and Automation Letters},
           year = {2022},
       abstract = {In the field of Learning from Demonstration (LfD), movement primitives learned from full trajectories provide mechanisms to generalize a demonstrated skill to unseen situations. Key position demonstrations, requiring the user to provide only a sequence of via-points rather than a complete trajectory, have been shown to be an appealing alternative. In this letter, we
investigate the synergy between learning adaptive movement primitives and key position demonstrations. We exploit a linear optimal control formulation to (1) recover the timing information of the skill missing from key position demonstrations, and to (2) infer low-effort movements on-the-fly. We evaluate the performance of the proposed approach in a user study where 16 novice users taught a 7-DoF robot manipulator, showing improved learning efficiency and trajectory smoothness. We further showcase the effectiveness of the approach for tasks that require precise demonstrations and on-the-fly movement adaptation.},
            pdf = {https://publications.idiap.ch/attachments/papers/2022/Jankowski_RA-L_2022.pdf}
}