In this paper, we survey the work in robotic grasping related areas that has been done over the last two decades, with a bias toward the development of the theoretical framework and analytical results in this area. In addition we assess the state of the art in this area and outline some of the important open problems.

Research in robotic grasping has flourished in the last 25 years. A recent survey by Bicchi [1] covered over 140 papers, and many more than that have been published. Stemming from our desire to implement some of the work in grasp analysis for particular hand designs, we created an interactive grasping simulator that can import a wide variety of hand and object models and can evaluate the grasps formed by these hands. This system, dubbed “GraspIt!,” has since expanded in scope to the point where we feel it could serve as a useful tool for other researchers in the field. To that end, we are making the system publicly available (GraspIt! is available for download for a variety of platforms from

In this paper, an attempt at summarizing the evolution and the state-of-the-art in the field of robot hands is made. In such exposition, a critical evaluation of what in the author's view are the leading ideas and emerging trends, is privileged with respect to exhaustiveness of citations. The survey is focused mainly on three types of functional requirements a machine hand can be assigned in an artificial system, namely, manipulative dexterity, grasp robustness, and human operability. A basic distinction is made between hands designed for mimicking the human anatomy and physiology, and hands designed to meet restricted, practical requirements. In the latter domain, arguments are presented in favor of a "minimalistic" attitude in the design of hands for practical applications, i.e., use the least number of actuators, the simplest set of sensors, etc., for a given task. To achieve this rather obvious engineering goal is a challenge to our community. The paper illustrates some of the ...

Three important problems in the study of grasping and manipulation by multifingered robotic hands are: (a) Given a grasp characterized by a set of contact points and the associated contact models, determine if the grasp has force closure; (b) If the grasp does not have force closure, determine if the ngers are able to apply a specified resultant wrench on the object; and (c) Compute "optimal" contact forces if the answer to problem (b) is affirmative. In this paper, based on an early result by Buss, Hashimoto and Moore, which transforms the nonlinear friction cone constraints into positive definiteness of certain symmetric matrices, we further cast the friction cone constraints into linear matrix inequalities (LMIs) and formulate all three of the problems stated above as a set of convex optimization problems involving LMIs. The latter problems have been extensively studied in optimization and control community and highly efficient algorithms with polynomial time complexity are now available for their solutions. We perform simulation studies to show the simplicity and efficiency of the LMI formulation to the three problems.

Abstract — Human grasps, especially whole-hand grasps, are difficult to animate because of the high number of degrees of freedom of the hand and the need for the hand to conform naturally to the object surface. Captured human motion data provides us with a rich source of examples of natural grasps. However, for each new object, we are faced with the problem of selecting the best grasp from the database and adapting it to that object. This paper presents a data-driven approach to grasp synthesis. We begin with a database of captured human grasps. To identify candidate grasps for a new object, we introduce a novel shape matching algorithm that matches hand shape to object shape by identifying collections of features having similar relative placements and surface normals. This step returns many grasp candidates, which are clustered and pruned by choosing the grasp best suited for the intended task. For pruning undesirable grasps, we develop an anatomically based grasp quality measure specific to the human hand. Examples of grasp synthesis are shown for a variety of objects not present in the original database. This algorithm should be useful both as an animator tool for posing the hand and for automatic grasp synthesis in virtual environments. Index Terms — Grasp synthesis, hands, shape matching, grasp quality.

Many nonlinear optimization problems can be cast as second-order cone programming problems. In this paper, we discuss a broad spectrum of such applications. For each application, we consider various formulations, some convex some not, and study which ones are amenable to solution using a general-purpose interior-point solver LOQO. We also compare with other commonly available nonlinear programming solvers and special-purpose codes for second-order cone programming.

Abstract — We propose a new and efficient approach to compute task oriented quality measures for dextrous grasps. Tasks can be specified as a single wrench to be applied, as a rough direction in form of a wrench cone, or as a complex wrench polytope. Based on the linear matrix inequality formalism to treat the friction cone constraints we formulate respective convex optimization problems, whose solutions give the maximal applicable wrench in the task direction together with the needed contact forces. Numerical experiments show that application to complex grasps with many contacts is possible. Index Terms — grasp quality measure, force optimization I.

Grasp quality measures are important for understanding how to plan for and maintain appropriate and secure grasps for pick and place operations and tool use. Most grasp quality measures assume certain symmetries about the mechanism or the task. For example, contact points may be considered to be independent and identical, or an ellipsoidal measure such as the force manipulability ellipsoid may be used. However, many tasks have strong asymmetries, where wrenches in certain directions dominate. Tendon driven hand designs may also have strong asymmetries, leading to differing abilities to apply contact forces in different directions. This paper begins to explore empirically the validity of some of the symmetry assumptions employed by common grasp quality metrics. We examine the human hand and the Shadow Robot Hand, and find that force abilities vary with finger choice and with location of the contact on the finger for both hands. However, while the human hand shows dramatic changes for different poses due to its asymmetric design, the Shadow hand, with a symmetric design shows much smaller changes and resembles the assumption of identical and independent contact points reasonably well. Thus, we suggest that the underlying design of the hand is a very important factor to consider for grasp quality metrics and for grasp planning and control. The specific grasp quality metric we study in this paper also brings together a variety of previous research. We outline a linear programming approach for computing a grasp quality metric that includes tendon force constraints and contact constraints and can handle any task described as a polytope in wrench space.

Abstract — Highly underactuated and passively adaptive robotic hands have shown great promise for robust performance in unstructured settings. In order to fully realize this potential, efficient tools are needed to analyze the execution of a grasp when using this class of devices. Along this line, this paper introduces a quasistatic analysis method for underactuated hands. First, we predict whether initial contacts between the fingers and the object are stable throughout the execution of a grasp, or the fingers will slip as the hand closes. Second, we compute the unbalanced forces applied to the object during the grasping process. Finally, once the grasp is complete, we analyze its stability as actuator forces are increased. These computations are performed in 3D, allow arbitrary kinematic structure of the fingers or geometry of the target object and take into account frictional constraints. We discuss applications of this method

Abstract — Passively adaptive and underactuated robotic hands have shown the potential to achieve reliable grasping in unstructured environments without expensive mechanisms or sensors. Instead of complex run-time algorithms, such hands use design-time analysis to improve performance for a wide range of tasks. Along these directions, we present an optimization framework for underactuated compliant hands. Our approach uses a pre-defined set of grasps in a quasistatic equilibrium formulation to compute the actuation mechanism design parameters that provide optimal performance. We apply our method to a class of tendon-actuated hands; for the simplified design of a two-fingered gripper, we show how a global optimum for the design optimization problem can be computed. We have implemented the results of this analysis in the construction of a gripper prototype, capable of a wide range of grasping tasks