A method is presented for stably grasping 2 dimensional polygonal objects with a dextrous hand when object models are not available. Basic constraints on object vertex angles are found for feasible grasping with two fingers. Local tactile information can be used to determine the finger motion that will reach feasible grasping locations. With an appropriate choice of finger stiffness, a hand can automatically grasp these objects with two fingers. The bounded slip of a part in a hand is shown to be valuable for adapting the fingers and object to a stable situation. Examples are given to show the ability of this grasping method to accommodate disturbance forces and to perform simple part reorientations and regrasping operations.

Manipulation using dextrous robot hands has been an exciting yet frustrating research topic for the last several years. While significant progress has occurred in the design, construction, and low level control of robotic hands, researchers are up against fundamental problems in developing algorithms for real-time computations in multi-sensory processing and motor control. The aim of this book is to explore parallels in sensorimotor integration in dextrous robot and human hands, addressing the basic question of how the next generation of dextrous hands should evolve. By bringing together experimental psychologists, kinesiologists, computer scientists, electrical engineers, and mechanical engineers, the book covers topics that range from human hand usage in prehension and exploration, to the design and use of robotic sensors and multi-fingered hands, and to control and computational architectures for dextrous hand usage. While the ultimate goal of capturing human hand versatility remains elusive, this book makes an important contribution to the design and control of future dextrous robot hands through a simple underlying message: a topic as complex as dextrous manipulation would best be addressed by collaborative, interdisciplinary research, combining high level and low level views, drawing parallels between human studies and analytic approaches, and integrating sensory data with motor commands. As seen in this text, success has been made through the establishment of such collaborative efforts. The future will hold up to expectations only as researchers become aware of advances in parallel fields and as a common vocabulary emerges from integrated perceptions about manipulation.

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.

When a person picks up a metal part and clamps it in the chuck of a lathe, he begins with his arm, proceeds with his wrist and finishes with his fingers. The arm brings the part near the chuck. The wrist positions the part, giving it the proper orientation to slide in. After the part is inserted, the wrist and fingers make tiny corrections to ensure that it is correctly seated. Today's robot attempting the same operations is at a grave disadvantage if it has to make all motions with the arm. The following work investigates the use of robotic wrists and hands to help industrial robots perform the fine motions needed in a metal working cell. Chapters 1 and 2 are an introduction to the field and a review of previous investigations on related subjects. Little work has been done on grasping and fine manipulation with a robot hand or wrist, but the related subjects of robot arm dynamics and control have an extensive literature.

This book introduces a novel model-based dexterous manipulation framework, which, thanks to its precision and versatility, significantly advances the capabilities of robotic hands compared to the previous state of the art. This is achieved by combining a novel grasp state estimation algorithm, the first to integrate information from tactile sensing, proprioception and vision, with an impedance-based in-hand object controller, which enables leading manipulation capabilities, including finger gaiting. The developed concept is implemented on one of the most advanced robotic manipulators, the DLR humanoid robot David, and evaluated in a range of challenging real-world manipulation scenarios and tasks. This book greatly benefits researchers in the field of robotics that study robotic hands and dexterous manipulation topics, as well as developers and engineers working on industrial automation applications involving grippers and robotic manipulators.

The simple task of grasping objects has been studied for centuries by scientists, therapists and engineers who have tried to understand and duplicate the versatility of the human hand. Using an interdisciplinary approach and new framework for looking at prehension, the authors uncover the subleties of the amazing interaction between the hand and the brain. They draw from such diverse fields as experimental psychology, kinesiology, robotics, neural networks, artificial intelligence, neuropsychology and rehabilitation. A triangle strategy is presented, starting from conceptual models that suggest both experimental and computational models. Chapters describe the multiple postures established by the hand, phases in the dynamic process of reaching for, grasping and manipulating various objects, and the constraints acting on such activity. Appendices provide the complete anatomy of the upper limb, the basics of computational modelling, and the fundamentals of prosthetic and dextrous robot hands. The ultimate goal of this book is to develop a common vocabularly for multidisciplinary researchers who strive to understand a system as complex as the hand under the control of the human brain.