TY - CONF T1 - Experimental prediction of the performance of grasp tasks from visual features T2 - Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on Y1 - 2003 A1 - Antonio Morales A1 - Eris Chinellato A1 - Fagg, A.H. A1 - Angel P. del Pobil KW - adaptive behavior KW - Barrett hand KW - dexterous manipulators KW - estimation rule KW - feature extraction KW - Geometry KW - grasp configuration KW - Grasping KW - hand kinematics KW - humanoid robot KW - Humans KW - Image reconstruction KW - Intelligent robots KW - Kinematics KW - Laboratories KW - manipulator kinematics KW - object image KW - performance prediction KW - prediction theory KW - Reliability KW - Robot sensing systems KW - robot vision KW - Robustness KW - Service robots KW - three finger grasps KW - unmodeled objects KW - visual features KW - visually guided grasping AB -

This paper deals with visually guided grasping of unmodeled objects for robots which exhibit an adaptive behavior based on their previous experiences. Nine features are proposed to characterize three-finger grasps. They are computed from the object image and the kinematics of the hand. Real experiments on a humanoid robot with a Barrett hand are carried out to provide experimental data. This data is employed by a classification strategy, based on the k-nearest neighbour estimation rule, to predict the reliability of a grasp configuration in terms of five different performance classes. Prediction results suggest the methodology is adequate.

JF - Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on ER -