The human hand is unrivaled in its ability to grasp and manipulate objects, but we still do not understand all of its complexities. One benefit it has over traditional robot hands is the fact that our fingers conform to a grasped object's shape, giving rise to larger contact areas and the ability to apply larger frictional forces. In this paper, we demonstrate how we have extended our simulation and analysis system with finite element modeling to allow us to evaluate these complex contact types. We also propose a new contact model that better accounts for the deformations and show how grasp quality is affected. This work is part of a larger project to understand the benefits the human hand has in grasping.

Finding appropriate stable grasps for a hand (either robotic or human) on an arbitrary object has proved to be a challenging and difficult problem. The space of grasping parameters coupled with the degrees-of-freedom and geometry of the object to be grasped creates a high-dimensional, non-smooth manifold. Traditional search methods applied to this manifold are typically not powerful enough to find appropriate stable grasping solutions, let alone optimal grasps. We address this issue in this paper, which attempts to find optimal grasps of objects using a grasping simulator. Our unique approach to the problem involves a combination of numerical methods to recover parts of the grasp quality surface with any robotic hand, and contemporary machine learning methods to interpolate that surface, in order to find the optimal grasp.

Robotic simulation systems allow researchers, engineers, and students to test control algorithms in a safe environment, but until recently these systems only simulated the dynamics of the mechanism itself and could not simulate (complex) contacts with other bodies in the environment. However, if the robot's task involves grasping an object, accurate simulation of contact and friction forces is a necessity. Recently developed methods formulate the constrains as a linear complementarity problem, allowing a solution to be computed using proven algorithms, but for anyone implementing such a system, several additional considerations must be taken into account. In this paper we present the implementation of the dynamics module of our freely available grasping simulator and present an example grasping task.

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.

Describes a synergistic integration of a grasping simulator and a real-time visual tracking system, that work in concert to (1) find an object's pose, (2) plan grasps and movement trajectories, and (3) visually monitor task execution. Starting with a CAD model of an object to be grasped, the system can find the object's pose through vision which then synchronizes the state of the robot workcell with an online, model-based grasp planning and visualization system we have developed called GraspIt. GraspIt can then plan a stable grasp for the object, and direct the robotic hand system to perform the grasp. It can also generate trajectories for the movement of the grasped object, which are used by the visual control system to monitor the task and compare the actual grasp and trajectory with the planned ones. We present experimental results using typical grasping tasks.

Previous grasp quality research is mainly theoretical, and has assumed that contact types and positions are given, in order to preserve the generality of the proposed quality measures. The example results provided by these works either ignore hand geometry and kinematics entirely or involve only the simplest of grippers. We present a unique grasp analysis system that, when given a 3D object, hand, and pose for the hand, can accurately determine the types of contacts that will occur between the links of the hand and the object, and compute two measures of quality for the grasp. Using models of two articulated robotic hands, we analyze several grasps of a polyhedral model of a telephone handset, and we use a novel technique to visualize the 6D space used in these computations. In addition, we demonstrate the possibility of using this system for synthesizing high quality grasps by performing a search over a subset of possible hand configurations.

Most robotic hands are either sensorless or lack the ability to accurately and robustly report position and force information relating to contact. This paper describes a robotic hand system that uses a limited set of native-joint position and force sensing along with custom-designed tactile sensors and real-time vision modules to accurately compute finger contacts and applied forces for grasping tasks. Three experiments are described: integration of real-time visual trackers in conjunction with internal strain gauge sensing to correctly localize and compute finger forces, determination of contact points on the inner and outer links of a finger through tactile sensing and visual sensing, and determination of vertical displacement by tactile sensing for a grasping task.