Resumé
of good grasps for grasp planning in robotics research. However, the existence of a generic simulation context that enables generation of high quality grasps that can be used in several different contexts such as bin-picking or picking objects from a table, has to our knowledge not been discussed in the literature. In this paper we investigate how well the quality of grasps simulated in a commonly used ”generic” context transfer to a specific context where the object is placed on a table. We generate a large database of grasp hypothesis for several objects, which we then evaluate in different dynamic simulation contexts eg. free float (no gravity, no obstacles), standing on table and lying on table. We present a comparison on the intersection of the grasp outcome space across the different contexts and quantitatively show that to generate reliable grasp databases, it is often required to use context specific simulation.
| Originalsprog | Engelsk |
|---|---|
| Titel | Proceedings of the RAAD 2013 |
| Antal sider | 9 |
| Publikationsdato | 11. aug. 2013 |
| Status | Udgivet - 11. aug. 2013 |
| Begivenhed | 22nd International Workshop on Robotics in Alpe-Adria-Danube Region - Portorož, Slovenien Varighed: 11. sep. 2013 → 13. sep. 2013 |
Workshop
| Workshop | 22nd International Workshop on Robotics in Alpe-Adria-Danube Region |
|---|---|
| Land | Slovenien |
| By | Portorož |
| Periode | 11/09/2013 → 13/09/2013 |
Fingeraftryk
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On Transferability of grasp-affordances in data-driven grasping. / Rytz, Jimmy Alison; Ellekilde, Lars-Peter; Kraft, Dirk; Petersen, Henrik Gordon; Krüger, Norbert.
Proceedings of the RAAD 2013. 2013.Publikation: Bidrag til bog/antologi/rapport/konference-proceeding › Konferencebidrag i proceedings › Forskning › peer review
TY - GEN
T1 - On Transferability of grasp-affordances in data-driven grasping
AU - Rytz, Jimmy Alison
AU - Ellekilde, Lars-Peter
AU - Kraft, Dirk
AU - Petersen, Henrik Gordon
AU - Krüger, Norbert
PY - 2013/8/11
Y1 - 2013/8/11
N2 - It has become a common practice to use simulation to generate large databasesof good grasps for grasp planning in robotics research. However, the existence of a generic simulation context that enables generation of high quality grasps that can be used in several different contexts such as bin-picking or picking objects from a table, has to our knowledge not been discussed in the literature. In this paper we investigate how well the quality of grasps simulated in a commonly used ”generic” context transfer to a specific context where the object is placed on a table. We generate a large database of grasp hypothesis for several objects, which we then evaluate in different dynamic simulation contexts eg. free float (no gravity, no obstacles), standing on table and lying on table. We present a comparison on the intersection of the grasp outcome space across the different contexts and quantitatively show that to generate reliable grasp databases, it is often required to use context specific simulation.
AB - It has become a common practice to use simulation to generate large databasesof good grasps for grasp planning in robotics research. However, the existence of a generic simulation context that enables generation of high quality grasps that can be used in several different contexts such as bin-picking or picking objects from a table, has to our knowledge not been discussed in the literature. In this paper we investigate how well the quality of grasps simulated in a commonly used ”generic” context transfer to a specific context where the object is placed on a table. We generate a large database of grasp hypothesis for several objects, which we then evaluate in different dynamic simulation contexts eg. free float (no gravity, no obstacles), standing on table and lying on table. We present a comparison on the intersection of the grasp outcome space across the different contexts and quantitatively show that to generate reliable grasp databases, it is often required to use context specific simulation.
KW - Robotic Grasping
KW - Grasp-affordances
KW - Dynamic Simulation
KW - Data-driven grasping
M3 - Article in proceedings
BT - Proceedings of the RAAD 2013
ER -