Grasp densities are designed to be learned empirically. In principle, a robot attemps to grasp the object a large number of times using a wide variety of object-relative gripper poses. Each successful grasp constitutes a data point drawn from the underlying grasp density (1). In practice, for reasons of efficiency, attempted grasps should be chosen in an informed manner [1]. For resampling and ...

In this talk a greedy randomized adaptive search procedure (GRASP) is presented for computing approximate solutions to the NP-hard three-index assignment problem (AP3). A FORTRAN implementation of this GRASP is tested on several problem instances of the AP3. A parallelization strategy that combines GRASP and path relinking is also proposed and tested for the problem. Computational results indic...

BACKGROUND The Graded Repetitive Arm Supplementary Program (GRASP) is a hand and arm exercise programme designed to increase the intensity of exercise achieved in inpatient stroke rehabilitation. GRASP was shown to be effective in a randomised controlled trial in 2009 and has since experienced unusually rapid uptake into clinical practice. The aim of this study was to conduct a formative evalua...

In this article, we study the problem of selecting a grasp pose on the surface of an object to be manipulated by considering three post-grasp objectives. These objectives include (i) kinematic manipulation capability [1], [2], (ii) torque effort [3] and (iii) impact force in case of collision [4] during post-grasp manipulative actions. In these works [1]–[4], the main assumption is that a manip...

Copyright is held by the author/owner. CHI 2011, May 7-12 2011, Vancouver, BC, Canada. ACM 978-1-4503-0268-5/11/05 Abstract The way we grasp an object depends on several factors, for example the intended goal or the hand's anatomy. Therefore, a grasp can convey meaningful information about its context. Inferring these factors from a grasp allows us to enhance interaction with tools and artifact...

This paper discusses the computerization of an existent tool called GRASP. It helps teachers to design an instructional system. Currently, GRASP has the form of a grid. Teachers complete it to first clarify their instructional problem and then to design an instructional system. This tool covers the analysis and design phases of the ADDIE (Analyze, Design, Develop, Implement and Evaluate) model....

A greedy randomized adaptive search procedure (GRASP) is a metaheuristic for combinatorial optimization. It is a multi-start or iterative process, in which each GRASP iteration consists of two phases, a construction phase, in which a feasible solution is produced, and a local search phase, in which a local optimum in the neighborhood of the constructed solution is sought. Since 1989, numerous p...

A greedy randomized adaptive search procedure (GRASP) is a randomized heuristic that has been shown to quickly produce good quality solutions for a wide variety of combinatorial optimization problems. In this paper, we describe a GRASP for the quadratic assignment problem. We review basic concepts of GRASP: construction and local search algorithms. The implementation of GRASP for the quadratic ...

In general, automatic grasp synthesis can be thought of the task of finding the combination of hand posture (intrinsic degrees of freedom, or DOF’s) and position (extrinsic DOF’s) that produces a stable grasp, according to a given grasp quality metric. From this perspective, it can be approached as an optimization problem, seeking to maximize the value of the grasp quality Q expressed as a func...

The correct grasp of objects is a key aspect for the right fulfillment of a given task. Obtaining a good grasp requires algorithms to automatically determine proper contact points on the object as well as proper hand configurations, especially when dexterous manipulation is desired, and the quantification of a good grasp requires the definition of suitable grasp quality measures. This article r...