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

On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas

Computational Modeling, Visualization, and Control of 2-D and 3-D Grasping under Rolling Contacts

This chapter presents a computational methodology for modeling 2-dimensional grasping of a 2-D object by a pair of multi-joint robot fingers under rolling contact constraints. Rolling contact constraints are expressed in a geometric interpretation of motion expressed with the aid of arclength parameters of the fingertips and object contours with an arbitrary geometry. Motions of grasping and object manipulation are expressed by orbits that are a solution to the Euler-Lagrange equation of motion of the fingers/object system together with a set of first-order differential equations that update arclength parameters. This methodology is then extended to mathematical modeling of 3-dimensional grasping of an object with an arbitrary shape. Based upon the mathematical model of 2-D grasping, a computational scheme for construction of numerical simulators of motion under rolling contacts with an arbitrary geometry is presented, together with preliminary simulation results. The chapter is composed of the following three parts. Part 1 Modeling and Control of 2-D Grasping under Rolling Contacts between Arbitrary Smooth Contours Authors: S. Arimoto and M. Yoshida Part 2 Simulation of 2-D Grasping under Physical Interaction of Rolling between Arbitrary Smooth Contour Curves Authors: M. Yoshida and S. Arimoto Part 3 Modeling of 3-D Grasping under Rolling Contacts between Arbitrary Smooth Surfaces Authors: S. Arimoto, M. Sekimoto, and M. Yoshid

GelSlim: A High-Resolution, Compact, Robust, and Calibrated Tactile-sensing Finger

This work describes the development of a high-resolution tactile-sensing finger for robot grasping. This finger, inspired by previous GelSight sensing techniques, features an integration that is slimmer, more robust, and with more homogeneous output than previous vision-based tactile sensors. To achieve a compact integration, we redesign the optical path from illumination source to camera by combining light guides and an arrangement of mirror reflections. We parameterize the optical path with geometric design variables and describe the tradeoffs between the finger thickness, the depth of field of the camera, and the size of the tactile sensing area. The sensor sustains the wear from continuous use -- and abuse -- in grasping tasks by combining tougher materials for the compliant soft gel, a textured fabric skin, a structurally rigid body, and a calibration process that maintains homogeneous illumination and contrast of the tactile images during use. Finally, we evaluate the sensor's durability along four metrics that track the signal quality during more than 3000 grasping experiments.Comment: RA-L Pre-print. 8 page

A Linear Actuator/Spring Steel-Driven Glove for Assisting Individuals with Activities of Daily Living

Over three million people in the U.S. suffer from forearm and hand disabilities. This can result from aging, neurological disorders (e.g., stroke), chronic disease (e.g., arthritis), and injuries. Injuries to hands comprise one-third of all work-related injuries worldwide. This can lead to difficulties with activities of daily living (ADL), where one needs to grasp, lift, and release objects in the household. There is a rise in demand for assistive orthoses and gloves that can allow many people to regain their grasping/releasing ability and, thereby, their independence. The main contribution of this thesis is developing an assistive glove with the actuating mechanism comprised of linear actuators and strips of spring steel to enable bidirectional motion of users\u27 fingers during ADL. The target group of people to use this proposed actuation system was chosen to those who had only diminished hand grasping capabilities. There are already many different gloves in the market. Each one uses different methods of actuation and force transmission, as well as different control methods. These gloves were analyzed by looking at their actuation mechanisms, control systems, and the benefits and downfalls of each one. Vigorous testing was conducted to choose the most effective components for the actuating mechanism. Then, an assistive glove was fabricated which included a control system box that could be easily worn on the forearm of the user. Tests were conducted on the glove to test its effectiveness when the user’s hand was completely passive using four to six participants. Motion capture, force, and electromyography (EMG) data were collected and from those, range of finger motion, maximum grasping capabilities, maximum force generation, and muscle activity were analyzed. The glove was shown to actuate the fingers enough to grasp objects with different sizes ranging in diameter from 40mm to 80mm, with maximum possible weight able to be picked up being around 1000g for the larger sizes. The glove could generate 4N-5N to the index and middle fingers and 10N to the thumb. EMG analysis showed that using the glove to pick up heavy objects caused a decrease in muscle activity of up to 80%. From this analysis, it was shown that the glove has potential to assist with ADL and would provide greater independence for those with diminished hand grasping abilities