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ICRA
2002
IEEE

Visual Guided Grasping of Aggregates using Self-Valuing Learning

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Visual Guided Grasping of Aggregates using Self-Valuing Learning
We present a self-valuing learning technique which is capable of learning how to grasp unfamiliar objects and generalize the learned abilities. The learning system consists of two learners which distinguish between local and global grasping criteria. The local criteria are not object specific while the global criteria cover physical properties of each object. The system is self-valuing, i.e. it rates its actions by evaluating sensory information and the usage of image processing techniques. An experimental setup consisting of a PUMA-260 manipulator, equipped with a hand-camera and a force/torque sensor, was used to test this scheme. The system has shown the ability to grasp a wide range of objects and to apply previously learned knowledge to new objects.
Bernd Rössler, Jianwei Zhang, Alois Knoll
Added 15 Jul 2010
Updated 15 Jul 2010
Type Conference
Year 2002
Where ICRA
Authors Bernd Rössler, Jianwei Zhang, Alois Knoll
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