Name
Abstract | ||
|---|---|---|
Accurately modeling local surface properties of objects is crucial to many robotic applications, from grasping to material recognition. Surface properties like friction are however difficult to estimate, as visual observation of the object does not convey enough information over these properties. In contrast, haptic exploration is time consuming as it only provides information relevant to the explored parts of the object. In this letter, we propose a joint visuo-haptic object model that enables the estimation of surface friction coefficient over an entire object by exploiting the correlation of visual and haptic information, together with a limited haptic exploration by a robotic arm. We demonstrate the validity of the proposed method by showing its ability to estimate varying friction coefficients on a range of real multi-material objects. Furthermore, we illustrate how the estimated friction coefficients can improve grasping success rate by guiding a grasp planner toward high friction areas. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/LRA.2021.3062585 | IEEE ROBOTICS AND AUTOMATION LETTERS |
Keywords | DocType | Volume |
Perception for grasping and manipulation, probabilistic inference, sensor fusion | Journal | 6 |
Issue | ISSN | Citations |
2 | 2377-3766 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tran Nguyen Le | 1 | 1 | 1.38 |
| Francesco Verdoja | 2 | 5 | 4.79 |
| Fares J. Abu-Dakka | 3 | 51 | 8.47 |
| V. Kyrki | 4 | 652 | 61.79 |