Assistive robots are categorized as robots that share their area of work and interact with humans. Their main goals are to help, assist, and monitor humans, especially people with disabilities. To achieve these goals, it is necessary that these robots possess a series of characteristics, namely the abilities to perceive their environment from their sensors and act consequently, to interact with people in a multimodal manner, and to navigate and make decisions autonomously. This complexity demands computationally expensive algorithms to be performed in real time. The advent of high-end embedded processors has enabled several such algorithms to be processed concurrently and in real time. All these capabilities involve, to a greater or less extent, the use of machine learning techniques. In particular, in the last few years, new deep learning techniques have enabled a very important qualitative leap in different problems related to perception, navigation, and human understanding. In this Special Issue, several works are presented involving the use of machine learning techniques for assistive technologies, in particular for assistive robots.

Assistive robots are categorized as robots that share their area of work and interact with humans. Their main goals are to help, assist, and monitor humans, especially people with disabilities. To achieve these goals, it is necessary that these robots possess a series of characteristics, namely the abilities to perceive their environment from their sensors and act consequently, to interact with people in a multimodal manner, and to navigate and make decisions autonomously. This complexity demands computationally expensive algorithms to be performed in real time. The advent of high-end embedded processors has enabled several such algorithms to be processed concurrently and in real time. All these capabilities involve, to a greater or less extent, the use of machine learning techniques. In particular, in the last few years, new deep learning techniques have enabled a very important qualitative leap in different problems related to perception, navigation, and human understanding. In this Special Issue, several works are presented involving the use of machine learning techniques for assistive technologies, in particular for assistive robots.

This book contains a comprehensive overview of all current uses of robots in rehabilitation. The underlying principles in each application are provided. This is followed by a critical review of the technology available, of the utilization protocols, and of user studies, outcomes, and clinical evidence, if existing. Ethical and social implications of robot use are also discussed. The reader will have an in depth view of rehabilitation robots, from principles to practice.

This book deals with the growing challenges of using assistive robots in our everyday activities along with providing intelligent assistive services. The presented applications concern mainly healthcare and wellness such as helping elderly people, assisting dependent persons, habitat monitoring in smart environments, well-being, security, etc. These applications reveal also new challenges regarding control theory, mechanical design, mechatronics, portability, acceptability, scalability, security, etc.

Unveil the Frontiers of Robotic Innovation and Implementation In the realm of cutting-edge technology, robotics stands as a beacon of innovation with the potential to revolutionize industries and daily life. "Mastering Robotics" is your comprehensive guide to understanding and harnessing the power of robotics—a transformative field that spans science, engineering, and creativity. About the Book: As the boundaries of human achievement expand, robotics emerges as a dynamic field with diverse applications. "Mastering Robotics" offers a deep exploration of robotics technology—a cornerstone of modern automation and innovation. This book caters to both newcomers and experienced enthusiasts seeking to excel in robotics design, development, and deployment. Key Features: Robotics Fundamentals: Begin by understanding the core principles of robotics. Learn how robots function, their components, and how they interact with the world. Robotic Kinematics and Dynamics: Dive into the mechanics of robots. Explore kinematic chains, inverse kinematics, and the principles that govern robotic motion. Sensors and Perception: Grasp the art of integrating sensors into robots. Learn how robots perceive the world through sensors and understand their surroundings. Robot Programming: Explore the intricacies of programming robots. Understand how to write code to control robots' actions, movements, and responses. Robot Vision and Machine Learning: Delve into robotic vision and machine learning. Learn how robots process visual data and adapt their behavior using advanced algorithms. Robot Localization and Mapping: Grasp the significance of localization and mapping in robotics. Understand how robots navigate and build maps of their environments. Robotic Manipulation and Control: Explore techniques for robotic manipulation and control. Learn how robots interact with objects, perform tasks, and maintain stability. Real-World Applications: Gain insights into how robotics is applied across industries. From manufacturing to healthcare, discover the diverse applications of robotic technology. Why This Book Matters: In an era of technological advancement, mastering robotics offers a transformative advantage. "Mastering Robotics" empowers engineers, researchers, and technology enthusiasts to harness the potential of robotics, enabling them to innovate and create solutions that reshape industries and redefine human capabilities. Embark on a Journey of Innovation: In the landscape of cutting-edge technology, robotics holds the promise of reshaping our world. "Mastering Robotics" equips you with the knowledge needed to unlock the potential of robotics, enabling you to design, build, and deploy robotic systems that push the boundaries of human achievement. Whether you're a seasoned professional or a newcomer to robotics, this book will guide you in building a solid foundation for innovation and exploration. Your journey to mastering robotics starts here. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com

What is artificial intelligence (AI)? What is healthcare robotics? How can AI and healthcare robotics assist in contemporary medicine? Robotics and AI can offer society unimaginable benefits, such as enabling wheelchair users to walk again, performing surgery in a highly automated and minimally invasive way, and delivering care more efficiently. AI for Healthcare Robotics explains what healthcare robots are and how AI empowers them in achieving the goals of contemporary medicine.

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).