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

Ground Robotic Hand Applications for the Space Program study (GRASP)

This document reports on a NASA-STDP effort to address research interests of the NASA Kennedy Space Center (KSC) through a study entitled, Ground Robotic-Hand Applications for the Space Program (GRASP). The primary objective of the GRASP study was to identify beneficial applications of specialized end-effectors and robotic hand devices for automating any ground operations which are performed at the Kennedy Space Center. Thus, operations for expendable vehicles, the Space Shuttle and its components, and all payloads were included in the study. Typical benefits of automating operations, or augmenting human operators performing physical tasks, include: reduced costs; enhanced safety and reliability; and reduced processing turnaround time

CASSL: Curriculum Accelerated Self-Supervised Learning

Recent self-supervised learning approaches focus on using a few thousand data points to learn policies for high-level, low-dimensional action spaces. However, scaling this framework for high-dimensional control require either scaling up the data collection efforts or using a clever sampling strategy for training. We present a novel approach - Curriculum Accelerated Self-Supervised Learning (CASSL) - to train policies that map visual information to high-level, higher- dimensional action spaces. CASSL orders the sampling of training data based on control dimensions: the learning and sampling are focused on few control parameters before other parameters. The right curriculum for learning is suggested by variance-based global sensitivity analysis of the control space. We apply our CASSL framework to learning how to grasp using an adaptive, underactuated multi-fingered gripper, a challenging system to control. Our experimental results indicate that CASSL provides significant improvement and generalization compared to baseline methods such as staged curriculum learning (8% increase) and complete end-to-end learning with random exploration (14% improvement) tested on a set of novel objects

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

Fast on-line determination of quasi-optimal grasp configurations based on off-line analysis and parametrization of the wrist position

Determining the optimum configuration of a mechanical hand in order to grasp an object is a computational hard work, mainly due to the large number of degrees of freedom (fingers and wrist), the existence of a large number of solutions, and the constraints imposed by the object or the task to be done. This paper presents an approach to solve this problem under certain conditions, which is then particularized for an anthropomorphic mechanical hand involving 22 degrees of freedom. The configurations of the hand to optimally grasp rectangular parallelepipeds of different sizes with a planar grasp (contact points on the same plane) are determined off-line from the results the wrist position is analyzed and stored as approximated functions of the height and width of the grasped object. This information is used for a fast on-line computation of the hand configuration in a real grasp operation, given the rectangular parallelepiped bounding-box of the part of the object that will be “inside” the hand after the grasp. The approach has been implemented for the mechanical hand MA-I and the paper includes numerical examples

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

Automated Configuration of Gripper Fingers from a Construction Kit for Robotic Applications

Gripper finger design is a complex process that requires a lot of experience, time, and effort. For this reason, automating this design process is an important area of research that has the potential to improve the efficiency and effectiveness of robotic systems. The current approaches are aimed at the automated design of monolithic gripper fingers, which have to be manufactured additively or by machining. This paper describes a novel approach for the automated design of gripper fingers. The motivation for this work stems from the increasing demand for flexible, adaptable handling systems in various industries in response to the increasing individualization of products as well as the increasing volatility in the markets. Based on the CAD data of the handling objects, the most suitable configuration of gripper fingers can be determined from the existing modules of a construction kit for the respective handling object, which can significantly reduce the provisioning time for new gripper fingers. It can be shown that gripper fingers can be effectively configured for a variety of objects and two different grippers, increasing flexibility in industrial handling processes