Data-Driven Grasp Synthesis - A Survey <s> A. Offline Generation of a Grasp Experience Database <s> 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 a set of grasp starting positions and pregrasp shapes that can then be tested on the object model. Each grasp is tested and evaluated within our grasping simulator "GraspIt!", and the best grasps are presented to the user. The simulator can also plan grasps in a complex environment involving obstacles and the reachability constraints of a robot arm. <s> BIB001 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Offline Generation of a Grasp Experience Database <s> Many methods for generating and analyzing grasps have been developed in the recent years. They gave insight and comprehension of grasping with robot hands but many of them are rather complicated to implement and of high computational complexity. In this paper we study if the basic quality criterion for grasps, the force-closure property, is in principle easy or difficult to reach. We show that it is not necessary to generate optimal grasps, due to a certain quality measure, for real robot grasping tasks where an average quality grasp is acceptable. We present statistical data that confirm our opinion that a randomized grasp generation algorithm is fast and suitable for the planning of robot grasping tasks. <s> BIB002 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Offline Generation of a Grasp Experience Database <s> One of the challenges in developing real-world autonomous robots is the need for integrating and rigorously testing high-level scripting, motion planning, perception, and control algorithms. For this purpose, we introduce an open-source cross-platform software architecture called OpenRAVE, the Open Robotics and Animation Virtual Environment. OpenRAVE is targeted for real-world autonomous robot applications, and includes a seamless integration of 3-D simulation, visualization, planning, scripting and control. A plugin architecture allows users to easily write custom controllers or extend functionality. With OpenRAVE plugins, any planning algorithm, robot controller, or sensing subsystem can be distributed and dynamically loaded at run-time, which frees developers from struggling with monolithic code-bases. Users of OpenRAVE can concentrate on the development of planning and scripting aspects of a problem without having to explicitly manage the details of robot kinematics and dynamics, collision detection, world updates, and robot control. The OpenRAVE architecture provides a flexible interface that can be used in conjunction with other popular robotics packages such as Player and ROS because it is focused on autonomous motion planning and high-level scripting rather than low-level control and message protocols. OpenRAVE also supports a powerful network scripting environment which makes it simple to control and monitor robots and change execution flow during run-time. One of the key advantages of open component architectures is that they enable the robotics research community to easily share and compare algorithms. <s> BIB003 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Offline Generation of a Grasp Experience Database <s> In this paper we present a framework for grasp planning with a humanoid robot arm and a five-fingered hand. The aim is to provide the humanoid robot with the ability of grasping objects that appear in a kitchen environment. Our approach is based on the use of an object model database that contains the description of all the objects that can appear in the robot workspace. This database is completed with two modules that make use of this object representation: An exhaustive offline grasp analysis system and a real-time stereo vision system. The offline grasp analysis system determines the best grasp for the objects by employing a simulation system, together with CAD models of the objects and the five-fingered hand. The results of this analysis are added to the object database using a description suited to the requirements of the grasp execution modules. A stereo camera system is used for a real-time object localization using a combination of appearance-based and model-based methods. The different components are integrated in a controller architecture to achieve manipulation task goals for the humanoid robot. <s> BIB004 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Offline Generation of a Grasp Experience Database <s> Planning realizable and stable grasps on 3D objects is crucial for many robotics applications, but grasp planners often ignore the relative sizes of the robotic hand and the object being grasped or do not account for physical joint and positioning limitations. We present a grasp planner that can consider the full range of parameters of a real hand and an arbitrary object, including physical and material properties as well as environmental obstacles and forces, and produce an output grasp that can be immediately executed. We do this by decomposing a 3D model into a superquadric 'decomposition tree' which we use to prune the intractably large space of possible grasps into a subspace that is likely to contain many good grasps. This subspace can be sampled and evaluated in GraspIt!, our 3D grasping simulator, to find a set of highly stable grasps, all of which are physically realizable. We show grasp results on various models using a Barrett hand. <s> BIB005 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Offline Generation of a Grasp Experience Database <s> This paper combines grasp analysis and manipulation planning techniques to perform fast grasp planning in complex scenes. In much previous work on grasping, the object being grasped is assumed to be the only object in the environment. Hence the grasp quality metrics and grasping strategies developed do not perform well when the object is close to obstacles and many good grasps are infeasible. We introduce a framework for finding valid grasps in cluttered environments that combines a grasp quality metric for the object with information about the local environment around the object and information about the robot's kinematics. We encode these factors in a grasp-scoring function which we use to rank a precomputed set of grasps in terms of their appropriateness for a given scene. We show that this ranking is essential for efficient grasp selection and present experiments in simulation and on the HRP2 robot. <s> BIB006 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Offline Generation of a Grasp Experience Database <s> In this paper, we address the problem of automatic grasp generation for robotic hands where experience and shape primitives are used in synergy so to provide a basis not only for grasp generation but also for a grasp evaluation process when the exact pose of the object is not available. One of the main challenges in automatic grasping is the choice of the object approach vector, which is dependent both on the object shape and pose as well as the grasp type. Using the proposed method, the approach vector is chosen not only based on the sensory input but also on experience that some approach vectors will provide useful tactile information that finally results in stable grasps. A methodology for developing and evaluating grasp controllers is presented where the focus lies on obtaining stable grasps under imperfect vision. The method is used in a teleoperation or a programming by demonstration setting where a human demonstrates to a robot how to grasp an object. The system first recognizes the object and grasp type which can then be used by the robot to perform the same action using a mapped version of the human grasping posture. <s> BIB007 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Offline Generation of a Grasp Experience Database <s> Grasping is a central issue of various robot applications, especially when unknown objects have to be manipulated by the system. In earlier work, we have shown the efficiency of 3D object shape approximation by box primitives for the purpose of grasping. A point cloud was approximated by box primitives [1]. In this paper, we present a continuation of these ideas and focus on the box representation itself. On the number of grasp hypotheses from box face normals, we apply heuristic selection integrating task, orientation and shape issues. Finally, an off-line trained neural network is applied to chose a final best hypothesis as the final grasp. We motivate how boxes as one of the simplest representations can be applied in a more sophisticated manner to generate task-dependent grasps. <s> BIB008 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Offline Generation of a Grasp Experience Database <s> Autonomous grasping of household objects is one of the major skills that an intelligent service robot necessarily has to provide in order to interact with the environment. In this paper, we propose a grasping strategy for known objects, comprising an off-line, box-based grasp generation technique on 3D shape representations. The complete system is able to robustly detect an object and estimate its pose, flexibly generate grasp hypotheses from the assigned model and perform such hypotheses using visual servoing. We will present experiments implemented on the humanoid platform ARMAR-III. <s> BIB009 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Offline Generation of a Grasp Experience Database <s> In this paper we focus on the concept of low-dimensional posture subspaces for artificial hands. We begin by discussing the applicability of a hand configuration subspace to the problem of automated grasp synthesis; our results show that low-dimensional optimization can be instrumental in deriving effective pre-grasp shapes for a number of complex robotic hands. We then show that the computational advantages of using a reduced dimensionality framework enable it to serve as an interface between the human and automated components of an interactive grasping system. We present an on-line grasp planner that allows a human operator to perform dexterous grasping tasks using an artificial hand. In order to achieve the computational rates required for effective user interaction, grasp planning is performed in a hand posture subspace of highly reduced dimensionality. The system also uses real-time input provided by the operator, further simplifying the search for stable grasps to the point where solutions can be found at interactive rates. We demonstrate our approach on a number of different hand models and target objects, in both real and virtual environments. <s> BIB010 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Offline Generation of a Grasp Experience Database <s> We present a method for automatic grasp generation based on object shape primitives in a Programming by Demonstration framework. The system first recognizes the grasp performed by a demonstrator as well as the object it is applied on and then generates a suitable grasping strategy on the robot. We start by presenting how to model and learn grasps and map them to robot hands. We continue by performing dynamic simulation of the grasp execution with a focus on grasping objects whose pose is not perfectly known. <s> BIB011 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Offline Generation of a Grasp Experience Database <s> This paper addresses the issue of learning and representing object grasp affordances, i.e. object-gripper relative configurations that lead to successful grasps. The purpose of grasp affordances is to organize and store the whole knowledge that an agent has about the grasping of an object, in order to facilitate reasoning on grasping solutions and their achievability. The affordance representation consists in a continuous probability density function defined on the 6D gripper pose space - 3D position and orientation -, within an object-relative reference frame. Grasp affordances are initially learned from various sources, e.g. from imitation or from visual cues, leading to grasp hypothesis densities. Grasp densities are attached to a learned 3D visual object model, and pose estimation of the visual model allows a robotic agent to execute samples from a grasp hypothesis density under various object poses. Grasp outcomes are used to learn grasp empirical densities, i.e. grasps that have been confirmed through experience. We show the result of learning grasp hypothesis densities from both imitation and visual cues, and present grasp empirical densities learned from physical experience by a robot. <s> BIB012 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Offline Generation of a Grasp Experience Database <s> We study the problem of human to robot grasp mapping as a basic building block of a learning by imitation system. The human hand posture, including both the grasp type and hand orientation, is first classified based on a single image and mapped to a specific robot hand. A metric for the evaluation based on the notion of virtual fingers is proposed. The first part of the experimental evaluation, performed in simulation, shows how the differences in the embodiment between human and robotic hand affect the grasp strategy. The second part, performed with a robotic system, demonstrates the feasibility of the proposed methodology in realistic applications. <s> BIB013 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Offline Generation of a Grasp Experience Database <s> In this paper, we present a system for vision-based grasp recognition, mapping and execution on a humanoid robot to provide an intuitive and natural communication channel between humans and humanoids. This channel enables a human user to teach a robot how to grasp an object. The system comprises three components: human upper body motion capture system which provides the approaching direction towards an object, hand pose estimation and grasp recognition system, which provides the grasp type performed by the human as well as a grasp mapping and execution system for grasp reproduction on a humanoid robot with five-fingered hands. All three components are real-time and markerless. Once an object is reached, the hand posture is estimated, including hand orientation and grasp type. For the execution on a robot, hand posture and approach movement are mapped and optimized according to the kinematic limitations of the robot. Experimental results are performed on the humanoid robot ARMAR-IIIb. <s> BIB014 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Offline Generation of a Grasp Experience Database <s> Typical tasks of future service robots involve grasping and manipulating a large variety of objects differing in size and shape. Generating stable grasps on 3D objects is considered to be a hard problem, since many parameters such as hand kinematics, object geometry, material properties and forces have to be taken into account. This results in a high-dimensional space of possible grasps that cannot be searched exhaustively. We believe that the key to find stable grasps in an efficient manner is to use a special representation of the object geometry that can be easily analyzed. In this paper, we present a novel grasp planning method that evaluates local symmetry properties of objects to generate only candidate grasps that are likely to be of good quality. We achieve this by computing the medial axis which represents a 3D object as a union of balls. We analyze the symmetry information contained in the medial axis and use a set of heuristics to generate geometrically and kinematically reasonable candidate grasps. These candidate grasps are tested for force-closure. We present the algorithm and show experimental results on various object models using an anthropomorphic hand of a humanoid robot in simulation. <s> BIB015 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Offline Generation of a Grasp Experience Database <s> Grasping an object is a task that inherently needs to be treated in a hybrid fashion. The system must decide both where and how to grasp the object. While selecting where to grasp requires learning about the object as a whole, the execution only needs to reactively adapt to the context close to the grasp's location. We propose a hierarchical controller that reflects the structure of these two sub-problems, and attempts to learn solutions that work for both. A hybrid architecture is employed by the controller to make use of various machine learning methods that can cope with the large amount of uncertainty inherent to the task. The controller's upper level selects where to grasp the object using a reinforcement learner, while the lower level comprises an imitation learner and a vision-based reactive controller to determine appropriate grasping motions. The resulting system is able to quickly learn good grasps of a novel object in an unstructured environment, by executing smooth reaching motions and preshaping the hand depending on the object's geometry. The system was evaluated both in simulation and on a real robot. <s> BIB016 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Offline Generation of a Grasp Experience Database <s> We present a method for learning object grasp affordance models in 3D from experience, and demonstrate its applicability through extensive testing and evaluation on a realistic and largely autonomous platform. Grasp affordance refers here to relative object-gripper configurations that yield stable grasps. These affordances are represented probabilistically with grasp densities, which correspond to continuous density functions defined on the space of 6D gripper poses. A grasp density characterizes an object's grasp affordance; densities are linked to visual stimuli through registration with a visual model of the object they characterize. We explore a batch-oriented, experience-based learning paradigm where grasps sampled randomly from a density are performed, and an importance-sampling algorithm learns a refined density from the outcomes of these experiences. The first such learning cycle is bootstrapped with a grasp density formed from visual cues. We show that the robot effectively applies its experience by downweighting poor grasp solutions, which results in increased success rates at subsequent learning cycles. We also present success rates in a practical scenario where a robot needs to repeatedly grasp an object lying in an arbitrary pose, where each pose imposes a specific reaching constraint, and thus forces the robot to make use of the entire grasp density to select the most promising achievable grasp. <s> BIB017 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Offline Generation of a Grasp Experience Database <s> Many supporting activities that future service robots might perform in people's homes depend on the capability to grasp and manipulate arbitrary objects. Easily accomplished by humans, but very difficult to achieve for robots, grasping involves dealing with a high-dimensional space of parameters which include hand kinematics, object geometry, material properties and forces. We believe that the way a robot grasps an object should be motivated by the object's geometry and that the search space for stable grasps can be dramatically reduced if the underlying object representation reflects symmetry properties of the object that contain valuable information for grasp planning. In this paper, we introduce the grid of medial spheres, a volumetric object representation based on the medial axis transform. The grid of medial spheres represents arbitrarily shaped objects with arbitrary levels of detail and contains symmetry information that can be easily exploited by a grasp planning algorithm. We present the data structure as well as a grasp planning algorithm that exploits it and provide experimental results on various object models using two robot hands in simulation. <s> BIB018 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Offline Generation of a Grasp Experience Database <s> Grasp planning based on perceived sensor data of an object can be performed in different ways, depending on the chosen semantic interpretation of the sensed data. For example, if the object can be recognized and a complete 3D model is available, a different planning tool can be selected compared to the situation in which only the raw sensed data, such as a single point cloud, is available. Instead of choosing between these options, we present a framework that combines them, aiming to find consensus on how the object should be grasped by using the information from each object representation according to their confidence levels. We show that this method is robust to common errors in perception, such as incorrect object recognition, while also taking into account potential grasp execution errors due to imperfect robot calibration. We illustrate this method on the PR2 robot by grasping objects common in human environments. <s> BIB019 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Offline Generation of a Grasp Experience Database <s> Personal robots can only become widespread if they are capable of safely operating among humans. In uncertain and highly dynamic environments such as human households, robots need to be able to instantly adapt their behavior to unforseen events. In this paper, we propose a general framework to achieve very contact-reactive motions for robotic grasping and manipulation. Associating stereotypical movements to particular tasks enables our system to use previous sensor experiences as a predictive model for subsequent task executions. We use dynamical systems, named Dynamic Movement Primitives (DMPs), to learn goal-directed behaviors from demonstration. We exploit their dynamic properties by coupling them with the measured and predicted sensor traces. This feedback loop allows for online adaptation of the movement plan. Our system can create a rich set of possible motions that account for external perturbations and perception uncertainty to generate truly robust behaviors. As an example, we present an application to grasping with the WAM robot arm. <s> BIB020 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Offline Generation of a Grasp Experience Database <s> Applying model-free reinforcement learning to manipulation remains challenging for several reasons. First, manipulation involves physical contact, which causes discontinuous cost functions. Second, in manipulation, the end-point of the movement must be chosen carefully, as it represents a grasp which must be adapted to the pose and shape of the object. Finally, there is uncertainty in the object pose, and even the most carefully planned movement may fail if the object is not at the expected position. To address these challenges we 1) present a simplified, computationally more efficient version of our model-free reinforcement learning algorithm PI2; 2) extend PI2 so that it simultaneously learns shape parameters and goal parameters of motion primitives; 3) use shape and goal learning to acquire motion primitives that are robust to object pose uncertainty. We evaluate these contributions on a manipulation platform consisting of a 7-DOF arm with a 4-DOF hand. <s> BIB021 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Offline Generation of a Grasp Experience Database <s> This paper presents an approach for computing power grasps for hands with kinematic structure similar to the human hand, which allows the implementation of strategies inspired in human grasping actions. The proposed method first samples the object surface to look for the best spots for creating an opposing grasp with two or three fingers, and then aligns the other fingers to match the local curvature of the object surface. Different grasp strategies are considered, depending on the relative size of the object with respect to the hand, and on the location of potential obstacles in the environment. Several application examples are provided with two different hand models. <s> BIB022 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Offline Generation of a Grasp Experience Database <s> We present a novel and simple experimental method called physical human interactive guidance to study human-planned grasping. Instead of studying how the human uses his/her own biological hand or how a human teleoperates a robot hand in a grasping task, the method involves a human interacting physically with a robot arm and hand, carefully moving and guiding the robot into the grasping pose, while the robot's configuration is recorded. Analysis of the grasps from this simple method has produced two interesting results. First, the grasps produced by this method perform better than grasps generated through a state-of-the-art automated grasp planner. Second, this method when combined with a detailed statistical analysis using a variety of grasp measures (physics-based heuristics considered critical for a good grasp) offered insights into how the human grasping method is similar or different from automated grasping synthesis techniques. Specifically, data from the physical human interactive guidance method showed that the human-planned grasping method provides grasps that are similar to grasps from a state-of-the-art automated grasp planner, but differed in one key aspect. The robot wrists were aligned with the object's principal axes in the human-planned grasps (termed low skewness in this paper), while the automated grasps used arbitrary wrist orientation. Preliminary tests show that grasps with low skewness were significantly more robust than grasps with high skewness (77-93%). We conclude with a detailed discussion of how the physical human interactive guidance method relates to existing methods to extract the human principles for physical interaction. <s> BIB023 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Offline Generation of a Grasp Experience Database <s> Grasp quality metrics which analyze the contact wrench space are commonly used to synthesize and analyze preplanned grasps. Preplanned grasping approaches rely on the robustness of stored solutions. Analyzing the robustness of such solutions for large databases of preplanned grasps is a limiting factor for the applicability of data driven approaches to grasping. In this work, we will focus on the stability of the widely used grasp wrench space epsilon quality metric over a large range of poses in simulation. We examine a large number of grasps from the Columbia Grasp Database for the Barrett hand. We find that in most cases the grasp with the most robust force closure with respect to pose error for a particular object is not the grasp with the highest epsilon quality. We demonstrate that grasps can be reranked by an estimate of the stability of their epsilon quality. We find that the grasps ranked best by this method are successful more often in physical experiments than grasps ranked best by the epsilon quality. <s> BIB024 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Offline Generation of a Grasp Experience Database <s> Humans excel in manipulation tasks, a basic skill for our survival and a key feature in our manmade world of artefacts and devices. In this work, we study how humans manipulate simple daily objects, and construct a probabilistic representation model for the tasks and objects useful for autonomous grasping and manipulation by robotic hands. Human demonstrations of predefined object manipulation tasks are recorded from both the human hand and object points of view. The multimodal data acquisition system records human gaze, hand and fingers 6D pose, finger flexure, tactile forces distributed on the inside of the hand, colour images and stereo depth map, and also object 6D pose and object tactile forces using instrumented objects. From the acquired data, relevant features are detected concerning motion patterns, tactile forces and hand-object states. This will enable modelling a class of tasks from sets of repeated demonstrations of the same task, so that a generalised probabilistic representation is derived to be used for task planning in artificial systems. An object centred probabilistic volumetric model is proposed to fuse the multimodal data and map contact regions, gaze, and tactile forces during stable grasps. This model is refined by segmenting the volume into components approximated by superquadrics, and overlaying the contact points used taking into account the task context. Results show that the features extracted are sufficient to distinguish key patterns that characterise each stage of the manipulation tasks, ranging from simple object displacement, where the same grasp is employed during manipulation (homogeneous manipulation) to more complex interactions such as object reorientation, fine positioning, and sequential in-hand rotation (dexterous manipulation). The framework presented retains the relevant data from human demonstrations, concerning both the manipulation and object characteristics, to be used by future grasp planning in artificial systems performing autonomous grasping. <s> BIB025
First, we look at approaches for generating the experience database. Figs. 3 and 5 summarize the typical functional flowchart of these type of approaches. Each box represents a processing step. Please note, that these figures are abstractions that summarize the implementations of a number of papers. Figure 3 : Typical functional flow-chart for a system with offline generation of a grasp database. In the offline phase, every object model is processed to generate grasp candidates. Their quality is evaluated for ranking. Finally, the list of grasp hypotheses is stored with the corresponding object model. In the online phase, the scene is segmented to search and recognize object models. If the process succeeds, the associated grasp hypotheses are retrieved and unreachable ones are discarded. Most of the following approaches can be summarized with this flowchart. Some of them only implement the offline part. BIB005 BIB006 BIB001 BIB018 BIB022 BIB009  BIB023 BIB002 BIB019 BIB010 BIB004 BIB011 Most reviewed papers focus on a single module. This is also true for similar figures appearing in Sections IV and III. 1) 3D Mesh Models and Contact-Level Grasping: Many approaches in this category assume that a 3D mesh of the object is available. The challenge is then to automatically generate a set of good grasp hypotheses. This involves sampling the infinite space of possible hand configurations and ranking the resulting candidate grasps according to some quality metric. The major part of the approaches discussed in the following use force closure grasps and rank them according to the previously discussed -metric. They differ mostly in the way the grasp candidates are sampled. Fig. 3 shows a flowchart of which specifically the upper part (Offline) visualizes the data flow for the following approaches. Some of them approximate the object's shape with a constellation of primitives such as spheres, cones, cylinders and boxes as in Miller et al. BIB001 , Hübner and Kragic BIB008 and Przybylski et al. BIB018 or superquadrics (SQ) as in Goldfeder et al. BIB005 . These shape primitives are then used to limit the amount of candidate grasps and thus prune the search tree for finding the most stable set of grasp hypotheses. Examples for these approaches are shown in Fig. 4a-4c and Fig. 4e . Borst et al. BIB002 reduce the number of candidate grasps by randomly generating a number of them dependent on the object surface and filter them with a simple heuristic. The authors show that this approach works well if the goal is not to find an optimal grasp but instead a fairly good grasp that works Figure 4 : Generation of grasp candidates through object shape approximation with primitives or through sampling. 4a) Primitive Shape Decomposition BIB001 . 4b) Box Decomposition BIB008 . 4c) SQ Decomposition BIB005 . 4d) Randomly sampled grasp hypotheses. BIB002 . 4e) Green: Centers of a union of spheres. Red: Centers at a slice through the model BIB015 BIB018 . 4f) Grasp candidate sampled based on surface normals and bounding box BIB003 . well for " everyday tasks". Diankov  proposes to sample grasp candidates dependent on the objects bounding box in conjunction with surface normals. Grasp parameters that are varied are the distance between the palm of the hand and the grasp point as well as the wrist orientation. This scheme is implemented in OpenRave BIB003 . The authors find that usually a relatively small amount of 30% from all grasp samples is in force closure. Examples for these sampling approaches are shown in Fig. 4d and 4f. Roa et al. BIB022 present an approach towards synthesizing power grasps that is not based on evaluating the force-closure property. Slices through the object model and perpendicular to the axes of the bounding box are sampled. The ones that best resemble a circle are chosen for synthesizing a grasp. All these approaches are developed and evaluated in simulation. As claimed by e.g. Diankov  , the biggest criticism towards ranking grasps based on force closure and the -metric is that relatively fragile grasps might be selected. A common approach to filter these, is to add noise to the grasp parameters and keep only those grasps in which a certain percentage of the neighboring candidates also yield force closure. Weisz and Allen BIB024 followed a similar approach that focuses in particular on the ability of the -metric to predict grasp stability under object pose uncertainty. For a set of object models, the authors used GraspIt!  to generate a set of grasp candidates in force closure. For each object, pose uncertainty is simulated by perturbing it in three degrees of freedom. Each grasp candidate The robot observes a human grasping a known object. Two perceptual processed are followed in parallel. On the left, the object is recognized. On the right, the demonstrated grasp configuration is extracted or recognized. Finally, object models and grasps are stored together. This process could replace or complement the offline phase described in Fig. 3 . The following approaches follow this approach: BIB012 BIB025 BIB023 BIB013 BIB016 BIB020 BIB021 . was then re-evaluated according to the probability of attaining a force closure grasp. The authors found that their proposed metric performs superior especially on large objects. Also Berenson et al. BIB006 consider the -metric as only one factor in selecting an appropriate grasp. The second factor in computing the final score of a grasp is the whole scene geometry. Balasubramanian et al. BIB023 question classical grasp metrics in principle. The authors systematically tested a number of grasps in the real world that were stable according to classical grasp metrics. These grasps under-performed significantly when compared to grasps planned by humans through kinesthetic teaching on the same objects. The authors found that humans optimize a skewness metric, i.e., the divergence of alignment between hand and principal object axes. 2) Learning from Humans: A different way to generate grasp hypotheses is to observe how humans grasp an object. This is usually done offline following the flow-chart in Fig. 5 . This process produces an experience database that is exploited online in a similar fashion as depicted in Fig. 3 . Ciocarlie and Allen BIB010 exploit results from neuroscience that showed that human hand control takes place in a space of much lower dimension than the hand's degrees of freedom. This finding was applied to directly reduce the configuration space of a robotic hand to find pre-grasp postures. From these so called eigengrasps the system searches for stable grasps. Detry et al. BIB012 model the object as a constellation of local multi-modal contour descriptors. Four elementary grasping actions are associated to specific constellations of these features resulting in an abundance of grasp candidates. They are modeled as a non-parametric density function in the space of 6D gripper poses, referred to as a bootstrap density. Human grasp examples are used to build an object specific empirical grasp density from which grasp hypotheses can be sampled. This is visualized in Fig. 8f and 8g. Kroemer et al. BIB016 represent the object with the same features as used by Detry et al. BIB012 . How to grasp specific objects is learned through a combination of a high-level reinforcement learner and a low level reactive grasp controller. The learning process is bootstrapped through imitation learning in which a demonstrated reaching trajectory is converted into an initial policy. Similar initialization of an object specific grasping policy is used in Pastor et al. BIB020 and Stulp et al. BIB021 . Faria et al. BIB025 use a multi-modal data acquisition tool to record humans grasping specific objects. Objects are represented as probabilistic volumetric maps that integrate multimodal information such as contact regions, fixation points and tactile forces. This information is then used to segment these maps into object parts and determine whether they are graspable or not. However, no results are provided on how a robot can use this information to synthesize a grasp. Romero et al. BIB013 present a system for observing humans visually while they interact with an object. A grasp type and pose is recognized and mapped to different robotic hands in a fixed scheme. For validation of the approach in the simulator, 3D object models are used. This approach has been demonstrated on a humanoid robot by Do et al. BIB014 . The object is not explicitly modeled. Instead, it is assumed that human and robot act on the same object in the same pose. In the method presented by Ekvall and Kragic BIB007 , a human demonstrator wearing a magnetic tracking device is observed while manipulating a specific object. The grasp type is recognized and mapped through a fixed schema to a set of robotic hands. Given the grasp type and the hand, the best approach vector is selected from an offline trained experience database. Unlike Detry et al. BIB012 and Romero et al. BIB013 , the approach vector used by the demonstrator is not adopted. Ekvall and Kragic BIB007 assume that the object pose is known. Experiments are conducted with a simulated pose error. No physical experiments have been demonstrated. Examples for the above mentioned ways to teach a robot grasping by demonstration are shown in Fig. 6 . 3) Learning through Trial and Error: Instead of adopting a fixed set of grasp candidates for a known object, the following approaches try to refine them by trial and error. In this case, there is no separation between offline learning and online exploitation as can be seen in Fig. 7 . Kroemer et al. BIB016 , Stulp et al. BIB021 apply reinforcement learning o improve an initial human demonstration. Kroemer et al. BIB016 uses a low-level reactive controller to perform the grasp that informs the high level controller with reward information. Stulp et al. BIB021 increase the robustness of their non-reactive grasping strategy by learning shape and goal parameters of the motion primitives that are used to model a full grasping action. Through this approach, the robot learns reaching trajectories and grasps that are robust against object pose uncertainties. Also Detry et al. BIB017 , adopt an approach in which the robot learns to grasp specific objects by experience. Instead of human examples as in BIB012 , successful grasping trials are used to build the objectspecific empirical grasp density. This non-parametric density can then be used to sample grasp hypotheses.