Learning of Grasp Adaptation through Experience and Tactile Sensing
Paper in proceedings, 2014

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.

Training

Stability analysis

Estimation

Uncertainty

Grasping

Support vector machines

Impedance

Author

Miao Li

Swiss Federal Institute of Technology in Lausanne (EPFL)

Yasemin Bekiroglu

Chalmers, Signals and Systems, Systems and control, Automatic Control

Danica Kragic

Royal Institute of Technology (KTH)

Aude Billard

Swiss Federal Institute of Technology in Lausanne (EPFL)

IEEE/RSJ International Conference on Intelligent Robots and Systems

2153-0858 (ISSN) 2153-0866 (eISSN)

IEEE/RSJ International Conference on Intelligent Robots and Systems
Chicago, ,

Subject Categories

Robotics

Control Engineering

Computer Vision and Robotics (Autonomous Systems)

More information

Latest update

9/29/2020