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 [BibTeX] [Marc21]
From Key Positions to Optimal Basis Functions for Probabilistic Adaptive Control
Type of publication: Journal paper
Citation: Jankowski_RA-L_2022
Publication status: Accepted
Journal: IEEE Robotics and Automation Letters
Year: 2022
Month: January
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.
Projects Idiap
Authors Jankowski, Julius
Racca, Mattia
Calinon, Sylvain
Added by: [UNK]
Total mark: 0
  • Jankowski_RA-L_2022.pdf