Data-Driven Grasp Synthesis - A Survey <s> I. INTRODUCTION <s> This article presents a survey of the existing computational algorithms meant for achieving four important properties in autonomous multifingered robotic hands. The four properties are: dexterity, equilibrium, stability, and dynamic behavior The multifingered robotic hands must be controlled so as to possess these properties and hence be able to autonomously perform complex tasks in a way similar to human hands. Existing algorithms to achieve dexterity primarily involve solving an unconstrained linear programming problem where an objective function can be chosen to represent one or more of the currently known dexterity measures. Algorithms to achieve equilibrium also constitute solving a linear program ming problem wherein the positivity, friction, and joint torque constraints of all fingers are accounted for while optimizing the internal grasping forces. Stability algorithms aim at achiev ing positive definite grasp impedance matrices by solving for the required fingertip impedances. This problem reduces to a nonlinear programming problem. Dynamic behavior algorithms determine fingertip impedances, which, when achieved, lead to a desired dynamic behavior. This problem, too, becomes a linear programming problem. If a robotic hand has to acquire any or all of these proper ties, the corresponding algorithms should become integral parts of the hand control system. These algorithms are collectively referred to in this article as robot grasp synthesis algorithms. <s> BIB001 </s> Data-Driven Grasp Synthesis - A Survey <s> I. INTRODUCTION <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> BIB002 </s> Data-Driven Grasp Synthesis - A Survey <s> I. INTRODUCTION <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> BIB003 </s> Data-Driven Grasp Synthesis - A Survey <s> I. INTRODUCTION <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> BIB004 </s> Data-Driven Grasp Synthesis - A Survey <s> I. INTRODUCTION <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> BIB005
Given an object, grasp synthesis refers to the problem of finding a grasp configuration that satisfies a set of criteria relevant for the grasping task. Finding a suitable grasp among the infinite set of candidates is a challenging problem and has been addressed frequently in the robotics community, resulting in an abundance of approaches. In the recent review of Sahbani et al. BIB005 , the authors divide the methodologies into analytic and empirical. Following Shimoga BIB001 , analytic refers to methods that construct forceclosure grasps with a multi-fingered robotic hand that are dexterous, in equilibrium, stable and exhibit a certain dynamic behaviour. Grasp synthesis is then usually formulated as a constrained optimization problem over criteria that measure one or several of these four properties. In this case, a grasp is typically defined by the grasp map that transforms the forces exerted at a set of contact points to object wrenches  . The criteria are based on geometric, kinematic or dynamic formulations. Analytic formulations towards grasp synthesis have also been reviewed by Bicchi and Kumar  . Empirical or data-driven approaches rely on sampling grasp candidates for an object and ranking them according to a specific metric. This process is usually based on some existing grasp experience that can be a heuristic or is generated in simulation or on a real robot. Kamon et al. BIB002 refer to this J. Bohg is with the Autonomous Motion Department at the MPI for Intelligent Systems, Tübingen, Germany, e-mail: jbohg@tuebingen.mpg.de. A. Morales is with the Robotic Intelligence Lab at UJI, Castelló, Spain, e-mail: Antonio.Morales@uji.es. T. Asfour is with the KIT, Karlsruhe, Germany, e-mail: asfour@kit.edu. D. Kragic is with the Computational Vision and Active Perception Lab at the KTH Stockholm, Sweden, e-mail: danik@csc.kth.se. as the comparative and Shimoga BIB001 as the knowledge-based approach. Here, a grasp is commonly parameterized by BIB004 BIB003 : • the grasping point on the object with which the tool center point (TCP) should be aligned, • the approach vector which describes the 3D angle that the robot hand approaches the grasping point with, • the wrist orientation of the robotic hand and • an initial finger configuration Data-driven approaches differ in how the set of grasp candidates is sampled, how the grasp quality is estimated and how good grasps are represented for future use. Some methods measure grasp quality based on analytic formulations, but more commonly they encode e.g. human demonstrations, perceptual information or semantics.