Data-Driven Grasp Synthesis - A Survey <s> B. Classification <s> Abstract Accumulating neuropsychological, electrophysiological and behavioural evidence suggests that the neural substrates of visual perception may be quite distinct from those underlying the visual control of actions. In other words, the set of object descriptions that permit identification and recognition may be computed independently of the set of descriptions that allow an observer to shape the hand appropriately to pick up an object. We propose that the ventral stream of projections from the striate cortex to the inferotemporal cortex plays the major role in the perceptual identification of objects, while the dorsal stream projecting from the striate cortex to the posterior parietal region mediates the required sensorimotor transformations for visually guided actions directed at such objects. <s> BIB001 </s> Data-Driven Grasp Synthesis - A Survey <s> B. Classification <s> Nature Rev. Neurosci. 6, 726–736 (2005) The correct image for panel 7d is shown below. <s> BIB002 </s> Data-Driven Grasp Synthesis - A Survey <s> B. Classification <s> Research from macaque neurophysiology and human neuropsychology has implicated the parietal cortex in the sensory control of action. Functional neuroimaging has been very valuable in localizing and characterizing specific regions of the human brain involved in visuomotor actions involving different effectors, such as the eyes, head, arms and hands. Here, we review the areas discovered by human neuroimaging, including the putative functional equivalents of the following macaque regions: parietal eye fields (PEF), ventral intraparietal (VIP) area, parietal reach region (PRR) and the anterior intraparietal (AIP) area. We discuss the challenges of studying realistic movements in the imaging environment, the lateralization of visuomotor function, caveats involved in proposing interspecies homologies and the limitations and future directions for neuroimaging studies of visuomotor control. © 2005 Elsevier Ltd. All rights reserved. <s> BIB003 </s> Data-Driven Grasp Synthesis - A Survey <s> B. Classification <s> This overview presents computational algorithms for generating 3D object grasps with autonomous multi-fingered robotic hands. Robotic grasping has been an active research subject for decades, and a great deal of effort has been spent on grasp synthesis algorithms. Existing papers focus on reviewing the mechanics of grasping and the finger-object contact interactions Bicchi and Kumar (2000) [12] or robot hand design and their control Al-Gallaf et al. (1993) [70]. Robot grasp synthesis algorithms have been reviewed in Shimoga (1996) [71], but since then an important progress has been made toward applying learning techniques to the grasping problem. This overview focuses on analytical as well as empirical grasp synthesis approaches. <s> BIB004
Sahbani et al. BIB004 divide the data-driven methods based on whether they employ object features or observation of humans during grasping. We believe that this falls short of capturing the diversity of these approaches especially in terms of the ability to transfer grasp experience between similar objects and the role of perception in this process. In this survey, we propose to group data-driven grasp synthesis approaches based on what they assume to know a priori about the query object: • Known Objects: These approaches assume that the query object has been encountered before and that grasps have already been generated for it. Commonly, the robot has access to a database containing geometric object models that are associated with a number of good grasp. This database is usually built offline and in the following will be referred to as an experience database. Once the object has been recognized, the goal is to estimate its pose and retrieve a suitable grasp. • Familiar Objects: Instead of exact identity, the approaches in this group assume that the query object is similar to previously encountered ones. New objects can be familiar on different levels. Low-level similarity can be defined in terms of shape, color or texture. High-level similarity can be defined based on object category. These approaches assume that new objects similar to old ones can be grasped in a similar way. The challenge is to Figure 1 : We identified a number of aspects that influence how the final set of grasp hypotheses is generated for an object. The most important one is the assumed prior object knowledge as discussed in Section I-B. Numerous different object representations are proposed in the literature that are relying on features of different modalities such as 2D or 3D vision or tactile sensors. Either local object parts or the object as a whole are linked to specific grasp configurations. Grasp synthesis can either be analytic or data-driven. The latter is further detailed in Fig. 2 . Very few approaches explicitly address the task or embodiment of the robot. find an object representation and a similarity metric that allows to transfer grasp experience. • Unknown Objects: Approaches in this group do not assume to have access to object models or any sort of grasp experience. They focus on identifying structure or features in sensory data for generating and ranking grasp candidates. These are usually based on local or global features of the object as perceived by the sensor. We find the above classification suitable for surveying the data-driven approaches since the assumed prior object knowledge determines the necessary perceptual processing and associated object representations for generating and ranking grasp candidates. For known objects, the problems of recognition and pose estimation have to be addressed. The object is usually represented by a complete geometric 3D object model. For familiar objects, an object representation has to be found that is suitable for comparing them to already encountered object in terms of graspability. For unknown objects, heuristics have to be developed for directly linking structure in the sensory data to candidate grasps. Only a minority of the approaches discussed in this survey cannot be clearly classified to belong to one of these three groups. Most of the included papers use sensor data from the scene to perform data-driven grasp synthesis and are part of a real robotic system that can execute grasps. Finally, this classification is well in line with the research in the field of neuroscience, specifically, from the theory of the dorsal and ventral stream in human visual processing BIB001 . The dorsal pathway processes immediate action-relevant features while the ventral pathway extracts context-and scene-relevant information and is related to object recognition. The visual processing in the ventral and dorsal pathways can be related to the grouping of grasp synthesis for familiar/known and unknown objects, respectively. The details of such links are out of the scope of this paper. Extensive and detailed reviews on the neuroscience of grasping are offered in BIB002 BIB003  .