Tactile Sensing, Skill Learning and Robotic Dexterous Manipulation focuses on cross-disciplinary lines of research and groundbreaking research ideas in three research lines: tactile sensing, skill learning and dexterous control. The book introduces recent work about human dexterous skill representation and learning, along with discussions of tactile sensing and its applications on unknown objects' property recognition and reconstruction. Sections also introduce the adaptive control schema and its learning by imitation and exploration. Other chapters describe the fundamental part of relevant research, paying attention to the connection among different fields and showing the state-of-the-art in related branches. The book summarizes the different approaches and discusses the pros and cons of each. Chapters not only describe the research but also include basic knowledge that can help readers understand the proposed work, making it an excellent resource for researchers and professionals who work in the robotics industry, haptics and in machine learning. - Provides a review of tactile perception and the latest advances in the use of robotic dexterous manipulation - Presents the most detailed work on synthesizing intelligent tactile perception, skill learning and adaptive control - Introduces recent work on human's dexterous skill representation and learning and the adaptive control schema and its learning by imitation and exploration - Reveals and illustrates how robots can improve dexterity by modern tactile sensing, interactive perception, learning and adaptive control approaches

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.

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

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.

Dexterous and autonomous manipulation is a key technology for the personal and service robots of the future. Advances in Bimanual Manipulation edited by Bruno Siciliano provides the robotics community with the most noticeable results of the four-year European project DEXMART (DEXterous and autonomous dual-arm hand robotic manipulation with sMART sensory-motor skills: A bridge from natural to artificial cognition). The volume covers a host of highly important topics in the field, concerned with modelling and learning of human manipulation skills, algorithms for task planning, human-robot interaction, and grasping, as well as hardware design of dexterous anthropomorphic hands. The results described in this five-chapter collection are believed to pave the way towards the development of robotic systems endowed with dexterous and human-aware dual-arm/hand manipulation skills for objects, operating with a high degree of autonomy in unstructured real-world environments.

Used for gestures of communication, environmental exploration, and the grasping and manipulating of objects, the hand has a vital role in our lives. The hand's anatomical structure and neural control are among the most complex and detailed of human motor systems.Hand and Brain is a comprehensive overview of the hand's sensorimotor control. It discusses mediating variables in perception and prehension, the coordination of muscles with the central nervous system, the nature of movement control and hand positioning, hand-arm coordination in reaching and grasping, and the sensory function of the hand.In the last decade the rapid growth of neuroscience has been paralleled by a surge of interest in hand function. This reflects the fact that many of the fundamental issues facing neuroscientists today--including the problem of relating physiology to behavior--are central to the study of sensorimotor control of the hand. This book takes a broad interdisciplinary perspective on the control of hand movements that includes neurophysiology, neuroanatomy, psychology and neuropsychology, and biomechanics.The authors, who have all made significant scientific contributions in their own right, have sought to introduce their chosen topics in a manner that the undergraduate reader will be able to follow without sacrificing detailed and up-to-date coverage ofthe major developments.Uses an interdisciplinary approach including behavioral and neurophysiological dataDescribes a variety of experimental methodologiesTreats neural computations necessary for the control of movementCovers implications of biomechanics for control, sensory mechanisms, and perceptual processing (haptics)Includes manipulative hand function as well as reachingOverviews each group of chapters using link sectionsContains an integrated index and a glosssaryThe five sections cover:Mediating variables in perception and prehensionThe coordination of muscles with the central nervous systemThe nature of movement control and hand positioningHand-arm coordination in reaching and graspingThe sensory function of the hand

The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.