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. |
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Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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