Grasp and manipulation planning of slippery objects often relies on the 'lform'closure" grasp, which can he maintained regardless of the external force applied to the object. Despite its importance, a quantitative test ,for form closure valid for any number of contact points is not availuhle. The primary contribution of this paper is the introduction of such a test formulated as a lineor progra...

Leveraging human grasping skills to teach a robot to perform a manipulation task is appealing, but there are several limitations to this approach: time-inefficient data capture procedures, limited generalization of the data to other grasps and objects, and inability to use that data to learn more about how humans perform and evaluate grasps. This paper presents a data capture protocol that part...

Grasp planning and optimization have been hot topics in the last decade. Due to the complexity of generating good grasps in analytic form for arbitrary objects, most of the proposed approaches rely on sampling strategies: candidate grasps are generated according to some criteria and then ranked with some quality metric. Uniform sampling around heuristic pre-grasps [1], [2], simulated annealing ...

This paper presents a method for one-shot learning of dexterous grasps, and grasp generation for novel objects. A model of each grasp type is learned from a single kinesthetic demonstration, and several types are taught. These models are used to select and generate grasps for unfamiliar objects. Both the learning and generation stages use an incomplete point cloud from a depth camera – no prior...

Automatic grasp planning for robotic hands is a difficult problem because of the huge number of possible hand configurations. However, humans simplify the problem by choosing an appropriate prehensile posture appropriate for the object and task to be performed. By modeling an object as a set of shape primitives, such as spheres, cylinders, cones and boxes, we can use a set of rules to generate ...

This paper presents a method for one-shot learning of dexterous grasps, and grasp generation for novel objects. A model of each grasp type is learned from a single kinesthetic demonstration, and several types are taught. These models are used to select and generate grasps for unfamiliar objects. Both the learning and generation stages use an incomplete point cloud from a depth camera – no prior...