Visual guided grasping and generalization 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. In this case we present a generalization method of the learning parameters based on a tree distance model for the medial axis transformations. 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.