A beyond human knowledge and reach, robotics is strongly involved in tackling challenges of new emerging multidisciplinary fields. Together with humans, robots are busy exploring and working on the new generation of ideas and problems whose solution is otherwise impossible to find. The future is near when robots will sense, smell and touch people and their lives. Behind this practical aspect of human-robotics, there is a half a century spanned robotics research, which transformed robotics into a modern science. The Advances in Robotics and Virtual Reality is a compilation of emerging application areas of robotics. The book covers robotics role in medicine, space exploration and also explains the role of virtual reality as a non-destructive test bed which constitutes a premise of further advances towards new challenges in robotics. This book, edited by two famous scientists with the support of an outstanding team of fifteen authors, is a well suited reference for robotics researchers and scholars from related disciplines such as computer graphics, virtual simulation, surgery, biomechanics and neuroscience.

A fascinating book that covers in detail all of the most recent advances in Telerobotics. A must-read for scientists, researchers and students in teleoperation, it describes everything from methods and experimental results to applications and developments. Its three sections cover human system interfaces, control, and applications.

This book gathers the proceedings of the 3rd Latin American Congress on Automation and Robotics, held at Monterrey, Mexico, on November 17–19, 2021. This book presents recent advances in the modeling, design, control, and development of autonomous and robotic systems and explores current exciting applications and future challenges of these technologies. The scope of this book covers a wide range of research fields associated with automation and robotics encountered within engineering, scientific research, and practice. These topics are related to autonomous systems, industrial automation and robotics, modelling and systems identification, simulation procedures and experimental validations, control theory, artificial intelligence, computer vision, sensing and sensor fusion, multi-robot and multi-agent systems, field and service robotics, human robot interaction and interfaces, modelling of robotic systems, and the design of new robotic platforms.

This book brings together some recent advances and development in robotics. In 12 chapters, written by experts and researchers in respective fields, the book presents some up-to-date research ideas and findings in a wide range of robotics, including the design, modeling, control, learning, interaction, and navigation of robots. From an application perspective, the book covers UAVs, USVs, mobile robots, humanoid robots, graspers, and underwater robots. The unique text offers practical guidance to graduate students and researchers in research and applications in the field of robotics.

Recent Advances in Robot Learning contains seven papers on robot learning written by leading researchers in the field. As the selection of papers illustrates, the field of robot learning is both active and diverse. A variety of machine learning methods, ranging from inductive logic programming to reinforcement learning, is being applied to many subproblems in robot perception and control, often with objectives as diverse as parameter calibration and concept formulation. While no unified robot learning framework has yet emerged to cover the variety of problems and approaches described in these papers and other publications, a clear set of shared issues underlies many robot learning problems. Machine learning, when applied to robotics, is situated: it is embedded into a real-world system that tightly integrates perception, decision making and execution. Since robot learning involves decision making, there is an inherent active learning issue. Robotic domains are usually complex, yet the expense of using actual robotic hardware often prohibits the collection of large amounts of training data. Most robotic systems are real-time systems. Decisions must be made within critical or practical time constraints. These characteristics present challenges and constraints to the learning system. Since these characteristics are shared by other important real-world application domains, robotics is a highly attractive area for research on machine learning. On the other hand, machine learning is also highly attractive to robotics. There is a great variety of open problems in robotics that defy a static, hand-coded solution. Recent Advances in Robot Learning is an edited volume of peer-reviewed original research comprising seven invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 23, Numbers 2 and 3).

This book of Advances in Deep Learning, Artificial Intelligence and Robotics (proceedings of ICDLAIR 2020) is intended to be used as a reference by students and researchers who collect scientific and technical contributions with respect to models, tools, technologies and applications in the field of modern artificial intelligence and robotics. Deep Learning, AI and robotics represent key ingredients for the 4th Industrial Revolution. Their extensive application is dramatically changing products and services, with a large impact on labour, economy and society at all. The research and reports of new technologies and applications in DL, AI and robotics like biometric recognition systems, medical diagnosis, industries, telecommunications, AI petri nets model-based diagnosis, gaming, stock trading, intelligent aerospace systems, robot control and web intelligence aim to bridge the gap between these non-coherent disciplines of knowledge and fosters unified development in next-generation computational models for machine intelligence.

This book provides an overview of a series of advanced research lines in robotics as well as of design and development methodologies for intelligent robots and their intelligent components. It represents a selection of extended versions of the best papers presented at the Seventh IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IDAACS 2013 that were related to these topics. Its contents integrate state of the art computational intelligence based techniques for automatic robot control to novel distributed sensing and data integration methodologies that can be applied to intelligent robotics and automation systems. The objective of the text was to provide an overview of some of the problems in the field of robotic systems and intelligent automation and the approaches and techniques that relevant research groups within this area are employing to try to solve them. The contributions of the different authors have been grouped into four main sections: RobotsControl and IntelligenceSensingCollaborative automation The chapters have been structured to provide an easy to follow introduction to the topics that are addressed, including the most relevant references, so that anyone interested in this field can get started in the area.