Learning of Grasp Adaptation through Experience and Tactile Sensing

To perform robust grasping, a multi-fingered robotic hand should be able to adapt its grasping configuration, i.e., how the object is grasped, to maintain the stability of the grasp. Such a change of grasp configuration is called grasp adaptation and it depends on the controller, the employed sensory feedback and the type of uncertainties inherit to the problem. This paper proposes a grasp adaptation strategy to deal with uncertainties about physical properties of objects, such as the object weight and the friction at the contact points. Based on an object-level impedance controller, a grasp stability estimator is first learned in the object frame. Once a grasp is predicted to be unstable by the stability estimator, a grasp adaptation strategy is triggered according to the similarity between the new grasp and the training examples. Experimental results demonstrate that our method improves the grasping performance on novel objects with different physical properties from those used for training.


Published in:
2014 Ieee/Rsj International Conference On Intelligent Robots And Systems (Iros 2014), 3339-3346
Presented at:
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, IL, SEP 14-18, 2014
Year:
2014
Publisher:
New York, Ieee
ISSN:
2153-0858
ISBN:
978-1-4799-6934-0
Laboratories:




 Record created 2015-04-13, last modified 2018-09-13


Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)