Finger design automation for industrial robot grippers: A review <s> Task-oriented Metrics: <s> We discuss the problem of optimal grasping to an object by a multifingered robot hand. We axiomatize using screw theory and elementary differential geometry the concept of a grasp and characterize its stability. Three quality measures for evaluating a grasp are then proposed. The last quality measure is task oriented and needs the development of a procedure for modeling tasks as ellipsoids in the wrench space of the object. Numerical computations of these quality measures and the selection of an optimal grasp are addressed in detail. Several examples are given using these quality measures to show that they are consistent with human grasping experience. <s> BIB001 </s> Finger design automation for industrial robot grippers: A review <s> Task-oriented Metrics: <s> The authors address the problem of planning optimal grasps. Two general optimality criteria that consider the total finger force and the maximum finger force are introduced and discussed. Their formalization using various metrics on a space of generalized forces is detailed. The geometric interpretation of the two criteria leads to an efficient planning algorithm. An example of its use in a robotic environment equipped with two-jaw and three-jaw is described. > <s> BIB002 </s> Finger design automation for industrial robot grippers: A review <s> Task-oriented Metrics: <s> For the evaluation of grasp quality, different measures have been proposed that are based on wrench spaces. Almost all of them have drawbacks that derive from the non-uniformity of the wrench space, composed of force and torque dimensions. Moreover, many of these approaches are computationally expensive. We address the problem of choosing a proper task wrench space to overcome the problems of the non-uniform wrench space and show how to integrate it in a well-known, high precision and extremely fast computable grasp quality measure. <s> BIB003 </s> Finger design automation for industrial robot grippers: A review <s> Task-oriented Metrics: <s> This paper proposes a task-oriented grasp quality metric based on distribution of task disturbance, which could be used to search for a grasp that covers the most significant part of the disturbance distribution. Rather than using a uniformly distributed task wrench space, this paper models a manipulation task with a non-parametric statistical distribution model built from the disturbance data captured during the task demonstrations. The grasp resulting from maximizing the proposed grasp quality criterion is prone to increasing the coverage of most frequent disturbances. To reduce the computational complexity of the search in a high-dimensional robotic hand configuration space, as well as to avoid the correspondence problem, the candidate grasps are computed from a reduced configuration space that is confined by a set of given thumb placements and thumb directions. The proposed approach has been tested both in simulation and on a real robotic system. In simulation, the approach was validated with a Barrett hand and a Shadow hand in several manipulation tasks. Experiments on a physical robotic platform verified the consistency between the proposed grasp metric and the success rate. <s> BIB004
The quality measures presented so far are task-independent, in which evenly distributed wrenches in all directions are assumed. In many assembly tasks, a task-oriented grasp quality measure can be utilized to find grasps that are most suitable to fulfill that specific task's requirements. A task can be defined as a set of wrenches that should be exerted on the workpiece to accomplish a given objective or a set of expected manipulation disturbance wrenches that the object should resist. These wrenches form the polytope formed by these wrenches is called task wrench space (TWS). BIB001 present the TWS as a 6D ellipsoid. This representation helps to find grasps that are more suitable for that specific task. Fig. 8 schematically illustrates the difference between the general ball metric BIB002 and the task-oriented ellipsoid BIB001 ) for a given grasp wrenches (w1 -w 4 ) and task wrenches (tw 1 -tw 6 ). As the general ball metric does not take the task wrenches into account, it considers the exampled grasp set as a low quality grasp while the ellipsoid task-oriented metric presents the grasp as a better grasp for the task. A popular task-oriented grasp metric is to choose an appropriate TWS and then measure how well it can be inscribed in a grasp wrench space (GWS) Fig. 8 -Bidimensional example of (a) classic general quality metric (b) task-oriented quality metric BIB001  BIB003 . Among the studies done in this area, just a few have considered the task information in the grasp synthesis and grasp analysis due to difficulty of modeling the task. In reality, high resolution sensors are required to measure the TWS by help of human demonstrations BIB004 which is not applicable in industries yet. Pros: These metrics accurately measure the grasp quality when information regarding robot task is available. Cons: The proposed metrics require many manual inputs that inconvenience finger design automation.