Name
Abstract | ||
|---|---|---|
In the last years, several regression algorithms have been proposed to learn accurate mechanical models of robots. Comparisons are proposed at the conceptual level or through the use of recorded databases, but they deliver limited conclusions with respect to the real performance of these algorithms in their true context of use, i.e. online learning on the real robot interacting with its environment, within a feedback control loop. In this paper, we provide an empirical study of three state-of-the-art regression methods through online learning on the iCub robot holding a tool. We show that they can effectively learn a visuo-motor kinematic model for a simple visual servoing task in a very limited time (few minutes), without making any a priori hypothesis on the geometry of the robot and its tool. Furthermore, we can draw from the results some stronger conclusions about the comparison of the algorithms than previous studies based on databases. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IROS.2012.6385674 | IROS |
Keywords | Field | DocType |
feedback,learning (artificial intelligence),manipulator dynamics,robot vision,visual servoing,autonomous online learning,feedback control loop,iCub,mechanical models,regression algorithms,robots,velocity kinematics,visual servoing,visuo-motor kinematic model | Robot learning,Computer vision,Robot control,iCub,Kinematics,Computer science,A priori and a posteriori,Robot kinematics,Control engineering,Artificial intelligence,Visual servoing,Robot | Conference |
ISSN | Citations | PageRank |
2153-0858 | 2 | 0.36 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alain Droniou | 1 | 61 | 3.71 |
| Serena Ivaldi | 2 | 163 | 19.72 |
| vincent padois | 3 | 168 | 15.26 |
| Olivier Sigaud | 4 | 539 | 53.35 |