Abstract
Object-level impedance control is of great importance for object-centric tasks, such as robust grasping and dexterous manipulation. Despite the recent progress on this topic, how to specify the desired object impedance for a given task remains an open issue. In this paper, we decompose the object's impedance into two complementary components-The impedance for stable grasping and impedance for object manipulation. Then, we present a method to learn the desired object's manipulation impedance (stiffness) using data obtained from human demonstration. The approach is validated in two tasks, for robust grasping of a wine glass and for inserting a bulb, using the 16 degrees of freedom Allegro Hand mounted with the SynTouch tactile sensors.
| Original language | English |
|---|---|
| Article number | 6907861 |
| Pages (from-to) | 6784-6791 |
| Number of pages | 8 |
| Journal | Proceedings - IEEE International Conference on Robotics and Automation |
| DOIs | |
| Publication status | Published - Sep 22 2014 |
| Event | 2014 IEEE International Conference on Robotics and Automation, ICRA 2014 - Hong Kong, China Duration: May 31 2014 → Jun 7 2014 |
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All Science Journal Classification (ASJC) codes
- Software
- Control and Systems Engineering
- Artificial Intelligence
- Electrical and Electronic Engineering
Cite this
Learning object-level impedance control for robust grasping and dexterous manipulation. / Li, Miao; Yin, Hang; Tahara, Kenji; Billard, Aude.
In: Proceedings - IEEE International Conference on Robotics and Automation, 22.09.2014, p. 6784-6791.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - Learning object-level impedance control for robust grasping and dexterous manipulation
AU - Li, Miao
AU - Yin, Hang
AU - Tahara, Kenji
AU - Billard, Aude
PY - 2014/9/22
Y1 - 2014/9/22
N2 - Object-level impedance control is of great importance for object-centric tasks, such as robust grasping and dexterous manipulation. Despite the recent progress on this topic, how to specify the desired object impedance for a given task remains an open issue. In this paper, we decompose the object's impedance into two complementary components-The impedance for stable grasping and impedance for object manipulation. Then, we present a method to learn the desired object's manipulation impedance (stiffness) using data obtained from human demonstration. The approach is validated in two tasks, for robust grasping of a wine glass and for inserting a bulb, using the 16 degrees of freedom Allegro Hand mounted with the SynTouch tactile sensors.
AB - Object-level impedance control is of great importance for object-centric tasks, such as robust grasping and dexterous manipulation. Despite the recent progress on this topic, how to specify the desired object impedance for a given task remains an open issue. In this paper, we decompose the object's impedance into two complementary components-The impedance for stable grasping and impedance for object manipulation. Then, we present a method to learn the desired object's manipulation impedance (stiffness) using data obtained from human demonstration. The approach is validated in two tasks, for robust grasping of a wine glass and for inserting a bulb, using the 16 degrees of freedom Allegro Hand mounted with the SynTouch tactile sensors.
UR - http://www.scopus.com/inward/record.url?scp=84929208215&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84929208215&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2014.6907861
DO - 10.1109/ICRA.2014.6907861
M3 - Conference article
AN - SCOPUS:84929208215
SP - 6784
EP - 6791
JO - Proceedings - IEEE International Conference on Robotics and Automation
JF - Proceedings - IEEE International Conference on Robotics and Automation
SN - 1050-4729
M1 - 6907861
ER -