Improving Superquadric Modeling and Grasping with Prior on Object Shapes
| Autor: | Giulia Vezzani, Giulia Pasquale, Lorenzo Natale, Ugo Pattacini |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Speedup Computer science business.industry GRASP 02 engineering and technology 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Robot Object model 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Classifier (UML) Humanoid robot iCub |
| Zdroj: | ICRA |
| DOI: | 10.1109/icra.2018.8463161 |
| Popis: | This paper proposes an object modeling and grasping pipeline for humanoid robots. This work improves our previous approach based on superquadric functions. In particular, we speed up and refine the modeling process by using prior information on the object shape provided by an object classifier. We use our previous method for the computation of grasping pose to obtain pose candidates for both the robot hands and, then, we automatically choose the best candidate for grasping the object according to a given quality index. The performance of our pipeline has been assessed on a real robotic system, the iCub humanoid robot. The robot can grasp 18 objects of the YCB and iCub World datasets considerably different in terms of shape and dimensions with a high success rate. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|