Name
Abstract | ||
|---|---|---|
This letter proposes a dynamic system approach to learn point-to-point motions while keeping the stability of the dynamic system. The proposed approach is grounded on a Learning from Demonstration (LfD) method based on a neural network, which gets a better reproduction performance while guaranteeing the generalization ability. The proposed approach has been experimentally validated on the LASA dataset and by the "pick-and-place" task of Franke Emika robot, and experimental results demonstrate that: (1) compared with the state-of-the-art results, the trajectory generated by the proposed approach achieves higher accuracy (approximately 24.79%) in terms of the similarity with respect to the demonstration; (2) the proposed approach can handle high dimensional data and learn from one or more demonstrations; (3) the proposed approach can guarantee the performance regardless of the variation of starting points even in the case of high dimensional complex motions. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LRA.2022.3140677 | IEEE ROBOTICS AND AUTOMATION LETTERS |
Keywords | DocType | Volume |
Point-to-point tasks, neural network, dynamic system, generalization performance, high dimensional data | Journal | 7 |
Issue | ISSN | Citations |
2 | 2377-3766 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Zhang | 1 | 22 | 15.84 |
| Long Cheng | 2 | 1492 | 73.97 |
| Houcheng Li | 3 | 0 | 0.34 |
| Ran Cao | 4 | 0 | 0.68 |