Learning of Grasp Adaptation through Experience and Tactile Sensing
Paper i proceeding, 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

Uncertainty

Impedance

Support vector machines

Grasping

Estimation

Författare

Miao Li

Ecole Polytech Federate Lausanne EPFL

Yasemin Bekiroglu

Chalmers, Signaler och system, System- och reglerteknik, Reglerteknik

Danica Kragic

Kungliga Tekniska Högskolan (KTH)

Aude Billard

Ecole Polytech Federate 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, ,

Ämneskategorier

Robotteknik och automation

Reglerteknik

Datorseende och robotik (autonoma system)

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2020-09-02