Learning object-level impedance control for robust grasping and dexterous manipulation

Miao Li, Hang Yin, Kenji Tahara, Aude Billard

Research output: Contribution to journalConference article

33 Citations (Scopus)

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 languageEnglish
Article number6907861
Pages (from-to)6784-6791
Number of pages8
JournalProceedings - IEEE International Conference on Robotics and Automation
DOIs
Publication statusPublished - Sep 22 2014
Event2014 IEEE International Conference on Robotics and Automation, ICRA 2014 - Hong Kong, China
Duration: May 31 2014Jun 7 2014

Fingerprint

Wine
Demonstrations
Stiffness
Glass
Sensors

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 journalConference article

@article{2a6fc535b7a34f90b2a710c5ac98fa9b,
title = "Learning object-level impedance control for robust grasping and dexterous manipulation",
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.",
author = "Miao Li and Hang Yin and Kenji Tahara and Aude Billard",
year = "2014",
month = "9",
day = "22",
doi = "10.1109/ICRA.2014.6907861",
language = "English",
pages = "6784--6791",
journal = "Proceedings - IEEE International Conference on Robotics and Automation",
issn = "1050-4729",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

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 -