Presentado al International Conference on Robotics and Automation celebrado en USA del 14 al 18 de mayo de 2012. ; Grasping highly deformable objects, like textiles, is an emerging area of research that involves both perception and manipulation abilities. As new techniques appear, it becomes essential to design strategies to compare them. However, this is not an easy task, since the large state-space of textile objects explodes when coupled with the variability of grippers, robotic hands and robot arms performing the manipulation task. This high variability makes it very difficult to design experiments to evaluate the performance of a system in a repeatable way and compare it to others. We propose a framework to allow the comparison of different grasping methods for textile objects. Instead of measuring each component separately, we therefore propose a methodology to explicitly measure the vision-manipulation correlation by taking into account the throughput of the actions. Perceptions of deformable objects should be grouped into different clusters, and the different grasping actions available should be tested for each perception type to obtain the action-perception success ratio. This characterization potentially allows to compare very different systems in terms of specialized actions, perceptions or widely useful actions, along with the cost of performing each action. We will also show that this categorization is useful in manipulation planning of deformable objects. ; This work was supported by the Spanish Ministry of Science and Innovation under projects PAU+ DPI2011-27510, by the EU project INTELLACT 247947 FP7-269959 and by the Catalan Research Commission through SGR-00155. A. Ramisa worked under the JAE-DOC grant from the CSIC and the FSE. ; Peer Reviewed
Characterization of textile grasping experiments
2012-01-01
Conference paper
Electronic Resource
English
DDC: | 629 |
DRIVER STATE GRASPING DEVICE, DRIVER STATE GRASPING SYSTEM, AND DRIVER STATE GRASPING METHOD
European Patent Office | 2019
|