|Title||An active learning approach for assessing robot grasp reliability|
|Publication Type||Conference Paper|
|Year of Publication||2004|
|Authors||Morales, A, Chinellato, E, Fagg, AH, del Pobil, AP|
|Conference Name||Intelligent Robots and Systems, 2004. (IROS 2004). Proceedings. 2004 IEEE/RSJ International Conference on|
|Keywords||active learning approach, Costs, Grasping, Haptic interfaces, Intelligent robots, Laboratories, learning (artificial intelligence), manipulators, motor control, Motor drives, online estimation, Reliability, reliability assessment capabilities, robot grasp reliability, Robot sensing systems, Torso, Training data, Uncertainty, visually-guided grasp selection|
Learning techniques in robotic grasping applications have usually been concerned with the way a hand approaches to an object, or with improving the motor control of manipulation actions. We present an active learning approach devised to face the problem of visually-guided grasp selection. We want to choose the best hand configuration for grasping a particular object using only visual information. Experimental data from real grasping actions is used, and the experience gathering process is driven by an on-line estimation of the reliability assessment capabilities of the system. The goal is to improve the selection skills of the grasping system, minimizing at the same time the cost and duration of the learning process.