The human hand and its dexterity in grasping and manipulating objects are some of the hallmarks of the human species. For years, anatomic and biomechanical studies have deepened the understanding of the human hand’s functioning and, in parallel, the robotics community has been working on the design of robotic hands capable of manipulating objects with a performance similar to that of the human hand. However, although many researchers have partially studied various aspects, to date there has been no comprehensive characterization of the human hand’s function for grasping and manipulation of everyday life objects. This monograph explores the hypothesis that the confluence of both scientific fields, the biomechanical study of the human hand and the analysis of robotic manipulation of objects, would greatly benefit and advance both disciplines through simulation. Therefore, in this book, the current knowledge of robotics and biomechanics guides the design and implementation of a simulation framework focused on manipulation interactions that allows the study of the grasp through simulation. As a result, a valuable framework for the study of the grasp, with relevant applications in several fields such as robotics, biomechanics, ergonomics, rehabilitation and medicine, has been made available to these communities.

Grasping in Robotics contains original contributions in the field of grasping in robotics with a broad multidisciplinary approach. This gives the possibility of addressing all the major issues related to robotized grasping, including milestones in grasping through the centuries, mechanical design issues, control issues, modelling achievements and issues, formulations and software for simulation purposes, sensors and vision integration, applications in industrial field and non-conventional applications (including service robotics and agriculture). The contributors to this book are experts in their own diverse and wide ranging fields. This multidisciplinary approach can help make Grasping in Robotics of interest to a very wide audience. In particular, it can be a useful reference book for researchers, students and users in the wide field of grasping in robotics from many different disciplines including mechanical design, hardware design, control design, user interfaces, modelling, simulation, sensors and humanoid robotics. It could even be adopted as a reference textbook in specific PhD courses.

This book constitutes the refereed proceedings of the First Robotic Grasping and Manipulation Challenge, RGMC 2016, held at IROS 2016, Daejeon, South Korea, in October 2016.The 13 revised full papers presented were carefully reviewed and are describing the rules, results, competitor systems and future directions of the inaugural competition. The competition was designed to allow researchers focused on the application of robot systems to compare the performance of hand designs as well as autonomous grasping and manipulation solutions across a common set of tasks. The competition was comprised of three tracks that included hand-in-hand grasping, fully autonomous grasping, and simulation.

Humans learn to be dexterous by interacting with a wide variety of objects in different contexts. Given the description of an object's physical attributes, humans can determine a proper strategy and grasp an object. This paper proposes an approach to determine grasping strategy for a 10 degree-of-freedom anthropomorphic robotic hand simply based on natural-language descriptions of an object. A probabilistic learning-based approach is proposed to help a robotic hand learn suitable grasp poses starting from the natural language description of the object. The solution involves a three-step learning model. In the first step, the information parsed from an object's natural-language descriptions are used to identify/recognize the object by making use of a novel nearestneighbor distance metric. In the second step, the probability distribution of grasp types for the given object is learned using a deep neural net which takes in object features as input. The labels for this grasp learning model is supplied from human grasping trials. The discrete, two-dimensional grasp type/size vector is mapped back to the ten-dimensional robot joint-angles configuration space using linear inverse-kinematics models. The grasping strategy generated by the proposed approach is evaluated both by simulation study and execution of the grasps on an AR10 robotic hand. Index Terms--robotic grasping, human grasp primitives, natural language processing, object features extraction, neural networks classification.

Human Inspired Dexterity in Robotic Manipulation provides up-to-date research and information on how to imitate humans and realize robotic manipulation. Approaches from both software and hardware viewpoints are shown, with sections discussing, and highlighting, case studies that demonstrate how human manipulation techniques or skills can be transferred to robotic manipulation. From the hardware viewpoint, the book discusses important human hand structures that are key for robotic hand design and how they should be embedded for dexterous manipulation. This book is ideal for the research communities in robotics, mechatronics and automation. Investigates current research direction in robotic manipulation Shows how human manipulation techniques and skills can be transferred to robotic manipulation Identifies key human hand structures for robotic hand design and how they should be embedded in the robotic hand for dexterous manipulation

This book provides a fundamental knowledge of robotic grasping and fixturing (RGF) manipulation. For RGF manipulation to become a science rather than an art, the content of the book is uniquely designed for a thorough understanding of the RGF from the multifingered robot hand grasp, basic fixture design principle, and evaluating and planning of robotic grasping/fixturing, and focuses on the modeling and applications of the RGF. Compared with existing publications, this volume concentrates more on abstract formulation, i.e. mathematical modeling of robotic grasping and fixturing. Thus, it will be a good reference text for academic researchers, manufacturing and industrial engineers and a textbook for engineering graduate students.The book provides readers an overall picture and scientific basis of RGF, the comprehensive information and mathematic models of developing and applying RGF in industry, and presents long term valuable information which is essential and can be used by technical professions as a good reference.

“The Human Hand as an Inspiration for Robot Hand Development” presents an edited collection of authoritative contributions in the area of robot hands. The results described in the volume are expected to lead to more robust, dependable, and inexpensive distributed systems such as those endowed with complex and advanced sensing, actuation, computation, and communication capabilities. The twenty-four chapters discuss the field of robotic grasping and manipulation viewed in light of the human hand’s capabilities and push the state-of-the-art in robot hand design and control. Topics discussed include human hand biomechanics, neural control, sensory feedback and perception, and robotic grasp and manipulation. This book will be useful for researchers from diverse areas such as robotics, biomechanics, neuroscience, and anthropologists.