Tactile Sensing Skill Learning And Robotic Dexterous Manipulation

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

Advanced robot systems require sensory information to enable them to make decisions and to carry out actions in a versatile, autonomous way. Humans make considerable use of information derived through touch, and an emerging domain of robot sensing is tactile sensing. This book considers various aspects of tactile sensing, from hardware design through to the use of tactile data in exploratory situations using a multi-fingered robot hand. In the first part of the book, the current state of progress of tactile sensing is surveyed, and it is found that the field is still in an early stage of development. Next, some fundamental issues in planar elasticity, concerning the interaction between tactile sensors and the environment, are presented. Having established how the basic data can be derived from the sensors, the issues of what form tactile sensors should take, and how they should be used, are considered. This is particularly important given the infancy of this field. The human tactile system is examined, and then biological touch and its implications for robotics is looked at. Some experiments in dextrous manipulation using a robot hand are described, which apply some of these results. The integration of tactile sensors into a complete system is also considered, and another, novel, approach for using touch sensing in a flexible assembly machine is described. Both basic material and new research results are provided in this book, thus catering to different levels of readers. The chapters by world experts in different aspects of the field are integrated well into one volume. The editor and authors have produced a thorough and in-depth survey of all work in robot tactile sensing, making the book essential reading for all researchers in this emergent field. Contents:Introduction to Tactile SensingTactile Sensor DesignsProcessing and Using Tactile Sensor Data (H R Nicholls)Planar Elasticity for Tactile Sensing (R S Fearing)Integrating Tactile Sensors — ESPRIT 278 (Z G Rzepczynski)Distributed Touch Sensing (H R Nicholls & N W Hardy)The Human Tactile System (L Moss-Salentijn)Lessons from the Study of Biological Touch for Robotic Tactile Sensing (S J Lederman & D T Pawluck)Lessons from the Study of Biological Touch for Robotic Haptic Sensing (S J Lederman et al.)Object Recognition Using Active Tactile Sensing (P K Allen)Experiments in Active Haptic Perception with the Utah-MIT Dextrous Hand (P K Allen et al.)Future Trends in Tactile Sensing (H R Nicholls)Appendix — Basic Linear Elasticity (R S Fearing) Readership: Computer scientists and engineers. keywords:Tactile Sensing;Tactile Sensor;Force Sensing;Force Sensor;Image Processing;Intelligent Robotics;Haptics;Robot Gripper;Robot Grasping;Touch Sensing;Touch Sensor “The book is well presented, with copious references to the literature, and will certainly be accepted as a standard work of reference in this area.” Robotica

This work introduces tactile sensing for those engaged in advanced, sensor-based robotics, with special reference to problems of addressing arrays of sensor elements. It describes tactile sensors to register contact, surface profile, thermal properties and other tactile sensing modes. The use of robot manipulators to provide mobility for tactile sensors, and techniques for applying tactile sensing in robotic manipulation and recognition tasks are also covered. The various applications of this technology are discussed, and robot hands and grips are detailed.

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

In the last decades robots are expected to be of increasing intelligence to deal with a large range of tasks. Especially, robots are supposed to be able to learn manipulation skills from humans. To this end, a number of learning algorithms and techniques have been developed and successfully implemented for various robotic tasks. Among these methods, learning from demonstrations (LfD) enables robots to effectively and efficiently acquire skills by learning from human demonstrators, such that a robot can be quickly programmed to perform a new task. This book introduces recent results on the development of advanced LfD-based learning and control approaches to improve the robot dexterous manipulation. First, there's an introduction to the simulation tools and robot platforms used in the authors' research. In order to enable a robot learning of human-like adaptive skills, the book explains how to transfer a human user’s arm variable stiffness to the robot, based on the online estimation from the muscle electromyography (EMG). Next, the motion and impedance profiles can be both modelled by dynamical movement primitives such that both of them can be planned and generalized for new tasks. Furthermore, the book introduces how to learn the correlation between signals collected from demonstration, i.e., motion trajectory, stiffness profile estimated from EMG and interaction force, using statistical models such as hidden semi-Markov model and Gaussian Mixture Regression. Several widely used human-robot interaction interfaces (such as motion capture-based teleoperation) are presented, which allow a human user to interact with a robot and transfer movements to it in both simulation and real-word environments. Finally, improved performance of robot manipulation resulted from neural network enhanced control strategies is presented. A large number of examples of simulation and experiments of daily life tasks are included in this book to facilitate better understanding of the readers.

Future robots are expected to work closely and interact safely with real-world objects and humans alike. Sense of touch is important in this context, as it helps estimate properties such as shape, texture, hardness, material type and many more; provides action related information, such as slip detection; and helps carrying out actions such as rolling an object between fingers without dropping it. This book presents an in-depth description of the solutions available for gathering tactile data, obtaining aforementioned tactile information from the data and effectively using the same in various robotic tasks. The efforts during last four decades or so have yielded a wide spectrum of tactile sensing technologies and engineered solutions for both intrinsic and extrinsic touch sensors. Nowadays, new materials and structures are being explored for obtaining robotic skin with physical features like bendable, conformable, and stretchable. Such features are important for covering various body parts of robots or 3D surfaces. Nonetheless, there exist many more hardware, software and application related issues that must be considered to make tactile sensing an effective component of future robotic platforms. This book presents an in-depth analysis of various system related issues and presents the trade-offs one may face while developing an effective tactile sensing system. For this purpose, human touch sensing has also been explored. The design hints coming out of the investigations into human sense of touch can be useful in improving the effectiveness of tactile sensory modality in robotics and other machines. Better integration of tactile sensors on a robot’s body is prerequisite for the effective utilization of tactile data. The concept of semiconductor devices based sensors is an interesting one, as it allows compact and fast tactile sensing systems with capabilities such as human-like spatio-temporal resolution. This book presents a comprehensive description of semiconductor devices based tactile sensing. In particular, novel Piezo Oxide Semiconductor Field Effect Transistor (POSFET) based approach for high resolution tactile sensing has been discussed in detail. Finally, the extension of semiconductors devices based sensors concept to large and flexile areas has been discussed for obtaining robotic or electronic skin. With its multidisciplinary scope, this book is suitable for graduate students and researchers coming from diverse areas such robotics (bio-robots, humanoids, rehabilitation etc.), applied materials, humans touch sensing, electronics, microsystems, and instrumentation. To better explain the concepts the text is supported by large number of figures.

The two volumes LNAI 11649 and 11650 constitute the refereed proceedings of the 20th Annual Conference "Towards Autonomous Robotics", TAROS 2019, held in London, UK, in July 2019. The 87 full papers and 12 short papers presented were carefully reviewed and selected from 101 submissions. The papers present and discuss significant findings and advances in autonomous robotics research and applications. They are organized in the following topical sections: robotic grippers and manipulation; soft robotics, sensing and mobile robots; robotic learning, mapping and planning; human-robot interaction; and robotic systems and applications.