Data-Driven Grasp Synthesis - A Survey <s> B. Grasp Synthesis by Comparison <s> A scheme for learning to grasp objects using visual information is presented. The learning problem is divided into two separate subproblems: choosing grasping points and predicting the quality of a given grasp. For each grasp we store location parameters that code the locations of the grasping points, quality parameters that are relevant features for the assessment of grasp quality, and the associated grade. The location parameters, using a special coding which is not object specific, are used to locate grasping points on new target objects. A function from the quality parameters to the grade is learned from examples. Grasp quality for novel situations can be generalized and estimated using the learned function. An experimental setup using an AdeptOne manipulator was developed to test this scheme. The system had demonstrated an ability to grasp a relatively wide variety of objects, and its performance had significantly improved with practice following a small number of trials. The knowledge learned for a set of objects was successfully generalized to new objects. <s> BIB001 </s> Data-Driven Grasp Synthesis - A Survey <s> B. Grasp Synthesis by Comparison <s> Manipulation skills are a key issue for a humanoid robot. Here, we are interested in a vision-based grasping system able to deal with previously unknown objects in real time and in an intelligent man- ner. Starting from a number of feasible candidate grasps, we focus on the problem of predicting their reliability using the knowledge acquired in previous grasping experiences. A set of visual features which take into account physical properties that can aect the stability and reliability of a grasp are dened. A humanoid robot obtains its grasping experience by repeating a large number of grasping actions on dieren t objects. An experimental protocol is established in order to classify grasps according to their reliability. Two prediction/classication strategies are dened which allow the robot to predict the outcome of a grasp only analizing its visual features. The results indicate that these strategies are adequate to predict the realibility of a grasp and to generalize to dieren t objects. <s> BIB002 </s> Data-Driven Grasp Synthesis - A Survey <s> B. Grasp Synthesis by Comparison <s> Humanoid robots must be capable of interacting with the world using their hands. A variety of humanlike robot hands have been constructed, but it remains difficult to control these hands in a dexterous way. One challenge is grasp synthesis, where we wish to place the hand and control its shape to successfully grasp a given object. In this paper, we present a datadriven approach to grasp synthesis that treats grasping as a shape matching problem. We begin with a database of grasp examples. Given a model of a new object to be grasped (the query), shape features of the object are compared to shape features of hand poses in these examples in order to identify candidate grasps. For effective retrieval, we develop a novel shape matching algorithm that can accommodate the sparse shape information associated with hand pose and that considers relative placements of contact points and normals, which are important for grasp function. We illustrate our approach with examples using a model of the human hand <s> BIB003 </s> Data-Driven Grasp Synthesis - A Survey <s> B. Grasp Synthesis by Comparison <s> This paper introduces a new approach to establish a good grasp for a novel object quickly. A comprehensive knowledge base for grasping is learned that takes into account the geometrical and physical knowledge of grasping. To automate the learning process as much as possible, learning happens in a virtual environment. We used the GraspIt! simulation environment with the Barrett hand for this work. As only approximate features of objects are used for training the grasping knowledge base (GKB), the knowledge gained is rather robust to object uncertainty. Based on the guidance of the GKB, a suitable grasp for a novel object can be found quickly. The newly gained grasping information of the new object can also be feedback to the GKB so that the knowledge base continues to improve as it is exposed to more grasping cases. The GKB serves as the ldquoexperiencerdquo of the robotic gripper to make grasping more and more skillful. We implemented the approach and tested it on This paper introduces a new approach to establish a good grasp for a novel object quickly. A comprehensive knowledge base for grasping is learned that takes into account the geometrical and physical knowledge of grasping. To automate the learning process as much as possible, learning happens in a virtual environment. We used the GraspIt! [T. Miller and P.K. Allen, 2000] simulation environment with the Barrett hand for this work. As only approximate features of objects are used for training the grasping knowledge base (GKB), the knowledge gained is rather robust to object uncertainty. Based on the guidance of the GKB, a suitable grasp for a novel object can be found quickly. The newly gained grasping information of the new object can also be feedback to the GKB so that the knowledge base continues to improve as it is exposed to more grasping cases. The GKB serves as the ldquoexperiencerdquo of the robotic gripper to make grasping more and more skillful. We implemented the approach and tested it on a wide variety of objects. The results show the effectiveness of this approach to achieve quick and good grasps of novel objects.a wide variety of objects. The results show the effectiveness of this approach to achieve quick and good grasps of novel objects. <s> BIB004 </s> Data-Driven Grasp Synthesis - A Survey <s> B. Grasp Synthesis by Comparison <s> This paper addresses the problem of grasp planning of unmodelled 3D objects with the aide of vision. The approach proposes the extension of fast 2D contour-based grasp planning algorithms, and the use of images of the target object from different points of view to recover critical 3D information like the size, location and the pose. A complete method is described, and experimental results are presented showing its feasibility. <s> BIB005 </s> Data-Driven Grasp Synthesis - A Survey <s> B. Grasp Synthesis by Comparison <s> Affordances encode relationships between actions, objects, and effects. They play an important role on basic cognitive capabilities such as prediction and planning. We address the problem of learning affordances through the interaction of a robot with the environment, a key step to understand the world properties and develop social skills. We present a general model for learning object affordances using Bayesian networks integrated within a general developmental architecture for social robots. Since learning is based on a probabilistic model, the approach is able to deal with uncertainty, redundancy, and irrelevant information. We demonstrate successful learning in the real world by having an humanoid robot interacting with objects. We illustrate the benefits of the acquired knowledge in imitation games. <s> BIB006 </s> Data-Driven Grasp Synthesis - A Survey <s> B. Grasp Synthesis by Comparison <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> BIB007 </s> Data-Driven Grasp Synthesis - A Survey <s> B. Grasp Synthesis by Comparison <s> In this paper, we present an efficient algorithm for 3D object recognition in presence of clutter and occlusions in noisy, sparse and unsegmented range data. The method uses a robust geometric descriptor, a hashing technique and an efficient RANSAC-like sampling strategy. We assume that each object is represented by a model consisting of a set of points with corresponding surface normals. Our method recognizes multiple model instances and estimates their position and orientation in the scene. The algorithm scales well with the number of models and its main procedure runs in linear time in the number of scene points. Moreover, the approach is conceptually simple and easy to implement. Tests on a variety of real data sets show that the proposed method performs well on noisy and cluttered scenes in which only small parts of the objects are visible. <s> BIB008 </s> Data-Driven Grasp Synthesis - A Survey <s> B. Grasp Synthesis by Comparison <s> This thesis introduces a new framework for data-driven grasping. We assume, with neuropsychological justification, that human grasping is fundamentally example based rather than rule based. This means that planning grasps for a novel object will usually reduce to identifying it as similar to known objects with known grasp affordances. Our framework is intended to allow robots to mimic this approach. ::: For robots to succeed in the real world, it is essential that they be able to grasp objects based on realistically available sensor data. However, most existing grasp planners assume that the robot has access to the full 3D geometry of all objects to be grasped, which is unscalable, or abandon 3D geometry entirely to plan grasps based on appearance, which is difficult to extend to dexterous hands. The core advantage of our data-driven framework is that it naturally allows grasps to be planned for partially sensed objects. We accomplish this by using the partial sensor data to find similar 3D models, which can be used as proxy geometries for grasp planning. ::: Along with the framework, we present a new set of shape descriptors suitable for matching partial sensor data to similar - but not identical - 3D models. This is in contrast to most previous descriptors for partial matching, which tend to rely on local feature correspondences that will often not exist in our problem setting. In a similar vein we also present new algorithms for aligning the pose and scale of partial sensor data to the best matching models, where no local correspondences may be assumed to exist. ::: Our grasp planner makes use of a grasp database, consisting of example grasps for a large number of 3D models. As no such database has previously existed, this thesis introduces the Columbia Grasp Database, a freely available collection of hundreds of thousands of grasps for thousands of 3D models using a variety of robotic hands. To construct this database we modified the Eigengrasp grasp planner, which uses a low dimensional control space to simplify the grasp search space. We also discuss some limitations of this planner and show how they can be addressed by model decomposition. ::: Our use of a database of 3D models annotated with precomputed grasps suggests the possibility of annotating the models with other forms of information as well. With this in mind, we show how to leverage noisy user data downloaded from the internet to suggest likely text tags for previously unlabeled 3D models. Although this work has not yet been applied to the problem of grasp planning, we demonstrate a content-based 3D model search engine that implements our automatic labeling algorithm. ::: The title of this thesis, “Data-Driven Grasping,” represents a vision of robotic grasping that is larger than our particular implementation. Instead, the major contribution of this thesis is bridging the worlds of robotics and 3D model similarity search. Content based search is an important area of research in its own right, but the combination of content based search and robotics is particularly exciting because it opens up the possibility of using the entire internet as a knowledge base for intelligent robots. <s> BIB009 </s> Data-Driven Grasp Synthesis - A Survey <s> B. Grasp Synthesis by Comparison <s> We design an example based planning framework to generate semantic grasps, stable grasps that are functionally suitable for specific object manipulation tasks. We propose to use partial object geometry, tactile contacts, and hand kinematic data as proxies to encode semantic constraints, which are task-related constraints. We introduce a semantic affordance map, which relates local geometry to a set of predefined semantic grasps that are appropriate to different tasks. Using this map, the pose of a robotic hand can be estimated so that the hand is adjusted to achieve the ideal approach direction required by a particular task. A grasp planner is then used to generate a set of final grasps which have appropriate stability, tactile contacts, and hand kinematics along this approach direction. We show experiments planning semantic grasps on everyday objects and executing these grasps with a physical robot. <s> BIB010 </s> Data-Driven Grasp Synthesis - A Survey <s> B. Grasp Synthesis by Comparison <s> We present a method for transferring grasps between objects of the same functional category. This transfer is intended to preserve the functionality of a grasp constructed for one of the objects, thus enabling the analogous action to be performed on a novel object for which no grasp has been specified. Manipulation knowledge is hence generalized from a single example to a class of objects with a significant amount of shape variability. The transfer is achieved through warping the surface geometry of the source object onto the target object, and along with it the contact points of a grasp. The warped contacts are locally replanned, if necessary, to ensure grasp stability, and a suitable grasp pose is computed. We present extensive results of experiments with a database of four-finger grasps, designed to systematically cover variations on grasping the mugs of the Princeton Shape Benchmark. <s> BIB011 </s> Data-Driven Grasp Synthesis - A Survey <s> B. Grasp Synthesis by Comparison <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> BIB012 </s> Data-Driven Grasp Synthesis - A Survey <s> B. Grasp Synthesis by Comparison <s> The paper starts by reviewing the challenges associated to grasp planning, and previous work on robot grasping. Our review emphasizes the importance of agents that generalize grasping strategies across objects, and that are able to transfer these strategies to novel objects. In the rest of the paper, we then devise a novel approach to the grasp transfer problem, where generalization is achieved by learning, from a set of grasp examples, a dictionary of object parts by which objects are often grasped. We detail the application of dimensionality reduction and unsupervised clustering algorithms to the end of identifying the size and shape of parts that often predict the application of a grasp. The learned dictionary allows our agent to grasp novel objects which share a part with previously seen objects, by matching the learned parts to the current view of the new object, and selecting the grasp associated to the best-fitting part. We present and discuss a proof-of-concept experiment in which a dictionary is learned from a set of synthetic grasp examples. While prior work in this area focused primarily on shape analysis (parts identified, e.g., through visual clustering, or salient structure analysis), the key aspect of this work is the emergence of parts from both object shape and grasp examples. As a result, parts intrinsically encode the intention of executing a grasp. <s> BIB013 </s> Data-Driven Grasp Synthesis - A Survey <s> B. Grasp Synthesis by Comparison <s> The ability to grasp unknown objects is an important skill for personal robots, which has been addressed by many present and past research projects, but still remains an open problem. A crucial aspect of grasping is choosing an appropriate grasp configuration, i.e. the 6d pose of the hand relative to the object and its finger configuration. Finding feasible grasp configurations for novel objects, however, is challenging because of the huge variety in shape and size of these objects. Moreover, possible configurations also depend on the specific kinematics of the robotic arm and hand in use. In this paper, we introduce a new grasp selection algorithm able to find object grasp poses based on previously demonstrated grasps. Assuming that objects with similar shapes can be grasped in a similar way, we associate to each demonstrated grasp a grasp template. The template is a local shape descriptor for a possible grasp pose and is constructed using 3d information from depth sensors. For each new object to grasp, the algorithm then finds the best grasp candidate in the library of templates. The grasp selection is also able to improve over time using the information of previous grasp attempts to adapt the ranking of the templates. We tested the algorithm on two different platforms, the Willow Garage PR2 and the Barrett WAM arm which have very different hands. Our results show that the algorithm is able to find good grasp configurations for a large set of objects from a relatively small set of demonstrations, and does indeed improve its performance over time. <s> BIB014 </s> Data-Driven Grasp Synthesis - A Survey <s> B. Grasp Synthesis by Comparison <s> The direct perception of actions allows a robot to predict the afforded actions of observed objects. In this paper, we present a non-parametric approach to representing the affordance-bearing subparts of objects. This representation forms the basis of a kernel function for computing the similarity between different subparts. Using this kernel function, together with motor primitive actions, the robot can learn the required mappings to perform direct action perception. The proposed approach was successfully implemented on a real robot, which could then quickly learn to generalize grasping and pouring actions to novel objects. <s> BIB015 </s> Data-Driven Grasp Synthesis - A Survey <s> B. Grasp Synthesis by Comparison <s> One of the basic skills for a robot autonomous grasping is to select the appropriate grasping point for an object. Several recent works have shown that it is possible to learn grasping points from different types of features extracted from a single image or from more complex 3D reconstructions. In the context of learning through experience, this is very convenient, since it does not require a full reconstruction of the object and implicitly incorporates kinematic constraints as the hand morphology. These learning strategies usually require a large set of labeled examples which can be expensive to obtain. In this paper, we address the problem of actively learning good grasping points to reduce the number of examples needed by the robot. The proposed algorithm computes the probability of successfully grasping an object at a given location represented by a feature vector. By autonomously exploring different feature values on different objects, the systems learn where to grasp each of the objects. The algorithm combines beta-binomial distributions and a non-parametric kernel approach to provide the full distribution for the probability of grasping. This information allows to perform an active exploration that efficiently learns good grasping points even among different objects. We tested our algorithm using a real humanoid robot that acquired the examples by experimenting directly on the objects and, therefore, it deals better with complex (anthropomorphic) hand-object interactions whose results are difficult to model, or predict. The results show a smooth generalization even in the presence of very few data as is often the case in learning through experience. <s> BIB016 </s> Data-Driven Grasp Synthesis - A Survey <s> B. Grasp Synthesis by Comparison <s> We present a real-world robotic agent that is capable of transferring grasping strategies across objects that share similar parts. The agent transfers grasps across objects by identifying, from examples provided by a teacher, parts by which objects are often grasped in a similar fashion. It then uses these parts to identify grasping points onto novel objects. We focus our report on the definition of a similarity measure that reflects whether the shapes of two parts resemble each other, and whether their associated grasps are applied near one another. We present an experiment in which our agent extracts five prototypical parts from thirty-two real-world grasp examples, and we demonstrate the applicability of the prototypical parts for grasping novel objects. <s> BIB017
The aforementioned approaches study what kind of features encode similarity of objects in terms of graspability and learn a discriminative function in the associated space. The methods we review next take an exemplar-based approach in which grasp hypotheses for a specific object are synthesized by finding the most similar object or object part in a database to which good grasps are already associated. 1) Synthetic Exemplars: Li and Pollard BIB003 treat the problem of finding a suitable grasp as a shape matching problem Figure 18 : Matching contact points between human hand and object BIB003 . between the human hand and the object. The approach starts off with a database of human grasp examples. From this database, a suitable grasp is retrieved when queried with a new object. Shape features of this object are matched against the shape of the inside of the available hand postures. An example is shown in Fig. 18 . Curtis and Xiao BIB004 build upon a knowledge base of 3D object types. These are represented by Gaussian distributions over very basic shape features, e.g., the aspect ratio of the object's bounding box, but also over physical features, e.g. material and weight. Furthermore, they are annotated with a set of representative pre-grasps. To infer a good grasp for a new object, its features are used to look up the most similar object type in the knowledge base. If a successful grasp has been synthesized in this way and it is similar enough to the object type, the mean and standard deviation of the object features are updated. Otherwise a new object type is formed in the knowledge base. While these two aforementioned approaches use low-level shape features to encode similarity between objects, Dang and Allen BIB010 present an approach towards semantic grasp planning. In this case, semantic refers to both, the object category and the task of a grasp, e.g. pour water, answer a call or hold and drill. The authors define a semantic affordance map on a prototypical object that links object features to an approach vector and to semantic grasp features (task label, joint angles and tactile sensor readings). For planning a taskspecific grasp on a novel object of the same category, the object features are used to retrieve the optimal approach direction and associated grasp features. The approach vector serves as a seed for synthesizing a grasp with the Eigengrasp planner BIB007 . The grasp features are used as a reference to which the synthesized grasp should be similar. Hillenbrand and Roa BIB011 frame the problem of transferring functional grasps between objects of the same category as pose alignment and shape warping. They assume that there is a source object given on which a set of functional grasps is defined. Pose clustering is used to align another object of the same category with it. This is followed by establishing correspondences between these two objects and subsequent transfer of the fingertip contact points from the source object to the target object. A feasible force-closed grasp is then replanned. The authors present experimental results in which 80% of grasps on the source object were also stable and reachable on the target object. These results are however limited to the category of cups containing six instances. All four approaches BIB003 BIB004 BIB010 BIB011 compute object features that rely on the availability of 3D object meshes. The question remains how these ideas could be transferred to the case where only partial sensory data is available to compute object features and similarity to already known objects. One idea would be to estimate full object shape from partial or multiple observations as proposed by the approaches in Sec. IV-A and use the resulting potentially noisy and uncertain meshes to transfer grasps. The above methods are also suitable to create experience databases offline that require only little labeling. In the case of category-based grasp transfer BIB010 BIB011 only one object per category would need to be associated with grasp hypotheses and all the other objects would only need a category label. No expensive grasp simulations for many grasp candidates would need to be executed as for the approaches in Section II-A1. Dang and Allen BIB010 followed this idea and demonstrated a few grasp trials on a real robot. 3D models of the query objects are available in an experience database that was built with the proposed category-based grasp transfer. The algorithm by Papazov and Burschka BIB008 was used to recognize objects in the scene and estimate their pose. Also Goldfeder and Allen BIB009 built their knowledge base only from synthetic data on which grasps are generated using the previously discussed Eigengrasp planner BIB007 . Different from the above approaches, observations made with real sensors from new objects are used to look up the most similar object and its pose in the knowledge base. This is done in two steps. In the first one, descriptors of the object views are matched against the synthetically generated ones from the database. Given the most similar object model, viewpoint constraints are exploited to find its pose and scale. Once this is found, the associated grasp hypotheses can be executed on the real object. Although experiments on a real platform are provided, it is not entirely clear how many trials have been performed on each object and how much the pose of an object was varied. As discussed earlier, the study conducted by Balasubramanian et al. BIB012 suggests that the employed grasp planner is not the optimal choice for synthesizing grasps that also work well in the real world. However, the methodology of retrieving similarly shaped objects from a grasp experience database is promising. Detry et al. BIB013 aim at generalizing grasps to novel objects by identifying parts to which a grasp has already been successfully applied. This look-up is rendered efficient by creating a lower-dimensional space in which object parts that are similarly shaped relative to the hand reference frame are close to each other. This space is shown in Fig. 19 . The authors show that similar grasp to object part configurations can be clustered in this space and form prototypical grasp-inducing parts. An extension of this approach is presented by Detry et al. BIB017 where the authors demonstrate how this approach can be used to synthesize grasps on novel objects by matching these prototypical parts to real sensor data. 2) Sensor-based Exemplars: The above mentioned approaches present promising ideas towards generalizing prior grasp experience to new objects. However, they are using 3D object models to construct the experience database. In this section, we review methods that generate a knowledge base by linking object representations from real sensor data to grasps that were executed on a robotic platform. Fig. 21 visualizes the flow of data that these approaches follow. Kamon et al. BIB001 propose one of the first approaches towards generalizing grasp experience to novel objects. Their aim is to learn a function f : Q → G that maps object-and graspcandidate-dependent quality parameters Q to a grade G of the grasp. An object is represented by its 2D silhouette, its center of mass and main axis. The grasp is represented by two parameters f 1 and f 2 from which in combination with the object features the fingertip positions can be computed. Learning is bootstrapped by the offline generation of a knowledge database containing grasp parameters along with their grade. This knowledge database is then updated while the robot gathers experience by grasping new objects. The system is restricted to planar grasps and visual processing of top-down views on objects. It is therefore questionable how robust this approach is to more cluttered environments and strong pose variations of the object. Morales et al. BIB002 use visual feedback to infer successful grasp configurations for a three-fingered hand. The authors take the hand kinematics into account when selecting a number of planar grasp hypotheses directly from 2D object contours. To predict which of these grasps is the most stable one, a knearest neighbour (KNN) approach is applied in connection with a grasp experience database. The experience database is built during a trial-and-error phase executed in the real world. Grasp hypotheses are ranked dependent on their outcome. Fig. 20 shows a successful and unsuccessful grasp configuration for one object. The approach is restricted to planar objects. Speth et al. BIB005 showed that their earlier 2D based approach BIB002 is also applicable when considering 3D objects. The camera is used to explore the object and retrieve crucial information like height, 3D position and pose. However, all this additional information is not applied in the inference and final selection of a suitable grasp configuration. The approaches presented by Herzog et al. BIB014 and Kroemer et al. BIB015 are also maintaining a database of grasp examples. They combine learning by trial and error on real world data with a part based representation of the object. There is no restriction of object shape. Each of them bootstrap the learning by providing the robot with a set of positive example grasps. However, their part representation and matching is very different. Herzog et al. BIB014 store a set of local templates of the parts of the object that have been in contact with the object No prior knowledge about objects is assumed. The scene is segmented to obtain object clusters and relevant features are extracted. A heuristic module produces grasp candidates from these features. These candidates are ranked using a previously learned model or based on comparison to previous examples. The resulting grasp hypotheses are filtered and one of them is finally executed. The performance of the execution is evaluated and the model or memory is updated with this new experience. The following approaches can be summarized by this flow chart: BIB015 BIB014 BIB016 BIB005 BIB001 BIB006 BIB002 during the human demonstration. Given a segmented object point cloud, its 3D convex hull is constructed. A template is a height map that is aligned with one polygon of this hull. Together with a grasp hypotheses, they serve as positive examples. If a local part of an object is similar to a template in the database, the associated grasp hypothesis is executed. Fig. 22 shows example query templates and the matched template from the database. In case of failure, the object part is added as a negative example to the old template. In this way, the similarity metric can weight in similarity to positive examples as well as dissimilarity to negative examples. The Figure 22 : Example query and matching templates BIB014 . proposed approach is evaluated on a large set of different objects and with different robots. Kroemer et al. BIB015 use a pouring task to demonstrate the generalization capabilities of the proposed approach to similar objects. An object part is represented as a set of points weighted according to an isotropic 3D Gaussian with a given standard deviation. Its mean is manually set to define a part that is relevant to the specific action. When shown a new object, the goal of the approach is to find the sub-part that is most likely to afford the demonstrated action. This probability is computed by kernel logistic regression whose result depends on the weighted similarity between the considered sub-part and the example sub-parts in the database. The weight vector is learned given the current set of examples. This set can be extended with new parts after each time an action has been executed and either succeeded or not. In this way, the weight vector can be updated to take the new experience into account. Herzog et al. BIB014 and Kroemer et al. BIB015 both do not adapt the similarity metric itself under which a new object part is compared to previously encountered examples. Instead the probability of success is estimated taken all the examples from the continuously growing knowledge base into account.