text: We present and evaluate an approach for human-in-the-loop specification of shape reconstruction with annotations for basic robot-object interactions. Our method is based on the idea of model annotation: the addition of simple cues to an underlying object model to specify shape and delineate a simple task. The goal is to explore reducing the complexity of CAD-like interfaces so that novice users can quickly recover an object's shape and describe a manipulation task that is then carried out by a robot. The object modeling and interaction annotation capabilities are tested with a user study and compared against results obtained using existing approaches. The approach has been analyzed using a variety of shape comparison, grasping, and manipulation metrics, and tested with the PR2 robot platform, where it was shown to be successful.
  @cite_61: 1. The prehensile movements of the hand as a whole are analysed from both an anatomical anda functional viewpoint. 2. It is shown that movements of the hand consist of two basic patterns of movements which are termed precision grip and power grip. 3. In precision grip the object is pinched between the flexor aspects of the fingers and that of the opposing thumb. 4. In power grip the object is held as in a clamp between the flexed fingers and the palm, counter pressure being applied by the thumb lying more or less in the plane of the palm. 5. These two patterns appear to cover the whole range of prehensile activity of the human hand.
  @cite_35: 
  @cite_84: Current analytical models of grasping and manipulation with robotic hands contain simplifications and assumptions that limit their application to manufacturing environments. To evaluate these models, a study was undertaken of the grasps used by machinists in a small batch manufacturing operation. Based on the study, a taxonomy of grasps was constructed. An expert system was also developed to clarify the issues involved in human grasp choice. Comparisons of the grasp taxonomy, the expert system, and grasp-quality measures derived from the analytic models reveal that the analytic measures are useful for describing grasps in manufacturing tasks despite the limitations in the models. In addition, the grasp taxonomy provides insights for the design of versatile robotic hands for manufacturing. >
  @cite_27: 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. >
  @cite_93: In this paper, we analyze and compare existing human grasp taxonomies and synthesize them into a single new taxonomy (dubbed “The GRASP Taxonomy” after the GRASP project funded by the European Commission). We consider only static and stable grasps performed by one hand. The goal is to extract the largest set of different grasps that were referenced in the literature and arrange them in a systematic way. The taxonomy provides a common terminology to define human hand configurations and is important in many domains such as human–computer interaction and tangible user interfaces where an understanding of the human is basis for a proper interface. Overall, 33 different grasp types are found and arranged into the GRASP taxonomy. Within the taxonomy, grasps are arranged according to 1) opposition type, 2) the virtual finger assignments, 3) type in terms of power, precision, or intermediate grasp, and 4) the position of the thumb. The resulting taxonomy incorporates all grasps found in the reviewed taxonomies that complied with the grasp definition. We also show that due to the nature of the classification, the 33 grasp types might be reduced to a set of 17 more general grasps if only the hand configuration is considered without the object shape size.
  @cite_111: 
  @cite_66: A robotic grasping simulator, called Graspit!, is presented as versatile tool for the grasping community. The focus of the grasp analysis has been on force-closure grasps, which are useful for pick-and-place type tasks. This work discusses the different types of world elements and the general robot definition, and presented the robot library. The paper also describes the user interface of Graspit! and present the collision detection and contact determination system. The grasp analysis and visualization method were also presented that allow a user to evaluate a grasp and compute optimal grasping forces. A brief overview of the dynamic simulation system was provided.
summary: Significant effort has been placed on obtaining a taxonomy of types of grasps and to judge their physical qualities @cite_84 @cite_93 @cite_111 . These studies draw inspiration from early analysis of human grasping @cite_61 or robot hands @cite_35 . One widely used approach for judging grasp quality is that of the metric developed by @cite_27 . It uses the grasp contact points to construct a convex hull over the set of wrenches that can be applied with the given grasp. This is called the Grasp Wrench Space (GWS). Two measurements that can be used are the volume of the computed convex hull and the radius of the maximum sphere contained in it, which we call the epsilon-distance, or @math -dist. These indicate the robustness of a grasp and the versatility of the wrenches applicable for the given grasp. One popular tool used in the literature to evaluate grasp quality and generate grasp candidates is GraspIt! @cite_66 . It uses the GWS volume and @math metrics to rank grasps that have been generated using a variety of grasp-planning techniques including the above mentioned .