Name
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LRA.2022.3146614 | IEEE ROBOTICS AND AUTOMATION LETTERS |
Keywords | DocType | Volume |
Learning from demonstration, imitation learning, machine learning for robot control | Journal | 7 |
Issue | ISSN | Citations |
2 | 2377-3766 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Julius Jankowski | 1 | 0 | 0.34 |
| Mattia Racca | 2 | 0 | 0.34 |
| Sylvain Calinon | 3 | 1897 | 117.63 |