Data-Driven Grasp Synthesis - A Survey

We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic

Visual Neuroscience of Robotic Grasping

Supporting Informatio

Planning dextrous robot hand grasps from range data, using preshapes and digit trajectories

Dextrous robot hands have many degrees of freedom. This enables the manipulation of objects between the digits of the dextrous hand but makes grasp planning substantially more complex than for parallel jaw grippers. Much of the work that addresses grasp planning for dextrous hands concentrates on the selection of contact sites to optimise stability criteria and ignores the kinematics of the hand. In more complete systems, the paradigm of preshaping has emerged as dominant. However, the criteria for the formation and placement of the preshapes have not been adequately examined, and the usefulness of the systems is therefore limited to grasping simple objects for which preshapes can be formed using coarse heuristics.In this thesis a grasp metric based on stability and kinematic feasibility is introduced. The preshaping paradigm is extended to include consideration of the trajectories that the digits take during closure from preshape to final grasp. The resulting grasp family is dependent upon task requirements and is designed for a set of "ideal" object-hand configurations. The grasp family couples the degrees of freedom of the dextrous hand in an anthropomorphic manner; the resulting reduction in freedom makes the grasp planning less complex. Grasp families are fitted to real objects by optimisation of the grasp metric; this corresponds to fitting the real object-hand configuration as close to the ideal as possible. First, the preshape aperture, which defines the positions of the fingertips in the preshape, is found by optimisation of an approximation to the grasp metric (which makes simplifying assumptions about the digit trajectories and hand kinematics). Second, the full preshape kinematics and digit closure trajectories are calculated to optimise the full grasp metric.Grasps are planned on object models built from laser striper range data from two viewpoints. A surface description of the object is used to prune the space of possible contact sites and to allow the accurate estimation of normals, which is required by the grasp metric to estimate the amount of friction required. A voxel description, built by ray-casting, is used to check for collisions between the object and the robot hand using an approximation to the Euclidean distance transform.Results are shown in simulation for a 3-digit hand model, designed to be like a simplified human hand in terms of its size and functionality. There are clear extensions of the method to any dextrous hand with a single thumb opposing multiple fingers and several different hand models that could be used are described. Grasps are planned on a wide variety of curved and polyhedral object

The neuroscience of vision-based grasping: a functional review for computational modeling and bio-inspired robotics

The topic of vision-based grasping is being widely studied using various techniques and with different goals in humans and in other primates. The fundamental related findings are reviewed in this paper, with the aim of providing researchers from different fields, including intelligent robotics and neural computation, a comprehensive but accessible view on the subject. A detailed description of the principal sensorimotor processes and the brain areas involved in them is provided following a functional perspective, in order to make this survey especially useful for computational modeling and bio-inspired robotic application

Affordance-based control of a variable-autonomy telerobot

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2012.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis. "September 2012."Includes bibliographical references (pages 37-38).Most robot platforms operate in one of two modes: full autonomy, usually in the lab; or low-level teleoperation, usually in the field. Full autonomy is currently realizable only in narrow domains of robotics-like mapping an environment. Tedious teleoperation/joystick control is typical in military applications, like complex manipulation and navigation with bomb-disposal robots. This thesis describes a robot "surrogate" with an intermediate and variable level of autonomy. The robot surrogate accomplishes manipulation tasks by taking guidance and planning suggestions from a human "supervisor." The surrogate does not engage in high-level reasoning, but only in intermediate-level planning and low-level control. The human supervisor supplies the high-level reasoning and some intermediate control-leaving execution details for the surrogate. The supervisor supplies world knowledge and planning suggestions by "drawing" on a 3D view of the world constructed from sensor data. The surrogate conveys its own model of the world to the supervisor, to enable mental-model sharing between supervisor and surrogate. The contributions of this thesis include: (1) A novel partitioning of the manipulation task load between supervisor and surrogate, which side-steps problems in autonomous robotics by replacing them with problems in interfaces, perception, planning, control, and human-robot trust; and (2) The algorithms and software designed and built for mental model-sharing and supervisor-assisted manipulation. Using this system, we are able to command the PR2 to manipulate simple objects incorporating either a single revolute or prismatic joint.by Michael Fleder.M. Eng