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Kohei Nakajima, Ingo Fischer.
Reservoir Computing: theory, physical implementations, and applications.
Springer. 2021

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This book is the first comprehensive book about reservoir computing (RC). RC is a powerful and broadly applicable computational framework based on recurrent neural networks. Its advantages lie in small training data set requirements, fast training, inherent memory and high flexibility for various hardware implementations. It originated from computational neuroscience and machine learning but has, in recent years, spread dramatically, and has been introduced into a wide variety of fields, including complex systems science, physics, material science, biological science, quantum machine learning, optical communication systems, and robotics. Reviewing the current state of the art and providing a concise guide to the field, this book introduces readers to its basic concepts, theory, techniques, physical implementations and applications.

The book is sub-structured into two major parts: theory and physical implementations. Both parts consist of a compilation of chapters, authored by leading experts in their respective fields. The first part is devoted to theoretical developments of RC, extending the framework from the conventional recurrent neural network context to a more general dynamical systems context. With this broadened perspective, RC is not restricted to the area of machine learning but is being connected to a much wider class of systems. The second part of the book focuses on the utilization of physical dynamical systems as reservoirs, a framework referred to as physical reservoir computing. A variety of physical systems and substrates have already been suggested and used for the implementation of reservoir computing. Among these physical systems which cover a wide range of spatial and temporal scales, are mechanical and optical systems, nanomaterials, spintronics, and quantum many body systems.

This book offers a valuable resource for researchers (Ph.D. students and experts alike) and practitioners working in the field of machine learning, artificial intelligence, robotics, neuromorphic computing, complex systems, and physics.

Koichi Suzumori, Kenjiro Fukuda, Ryuma Niiyama, Kohei Nakajima.
The Science of Soft Robots: design, materials and information processing.
Springer. 2023

The goal of this textbook is to equip readers with as structured knowledge of soft robotics as possible. Seeking to overcome the limitations of conventional robots by making them more flexible, gentle and adaptable, soft robotics has become one of the most active fields over the last decade. Soft robotics is also highly interdisciplinary, bringing together robotics, computer science, material science, biology, etc.

After the introduction, the content is divided into three parts: Design of Soft Robots; Soft Materials; and Autonomous Soft Robots. Part I addresses soft mechanisms, biological mechanisms, and soft manipulation & locomotion. In Part II, the basics of polymer, biological materials, flexible & stretchable sensors, and soft actuators are discussed from a materials science standpoint. In turn, Part III focuses on modeling & control of continuum bodies, material intelligence, and information processing using soft body dynamics. In addition, the latest research results and cutting-edge research are highlighted throughout the book.

Written by a team of researchers from highly diverse fields, the work offers a valuable textbook or technical guide for all students, engineers and researchers who are interested in soft robotics.