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.

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

This thesis introduces new methods for enabling the effective use of highly dexterous robotic hands, interfacing with the upcoming generation of neurally controlled hand prostheses, and designing a new class of simple yet effective grasping devices based on underactuation and mechanical adaptation. These methods share a common goal: reducing the complexity that has traditionally been associated, at both computational and mechanical levels, with robotic grasping in unstructured environments. A key prerequisite for robot operation in human settings is versatility, which, in terms of autonomous grasping, translates into the ability to reliably acquire and interact with a wide range of objects. In an attempt to match the abilities of the most versatile end-effector known, the human hand, many anthropomorphic robotic models have been proposed, with the number of degrees of freedom starting to approach that of their human counterpart. However, these models have proven difficult to use in practice, as the high dimensionality of the posture space means that finding adequate grasps for a target object is often an intractable problem. In this thesis, we propose using low-dimensional posture subspaces for dexterous or