Methods by which robots can learn control laws that enable real-time reactivity using dynamical systems; with applications and exercises. This book presents a wealth of machine learning techniques to make the control of robots more flexible and safe when interacting with humans. It introduces a set of control laws that enable reactivity using dynamical systems, a widely used method for solving motion-planning problems in robotics. These control approaches can replan in milliseconds to adapt to new environmental constraints and offer safe and compliant control of forces in contact. The techniques offer theoretical advantages, including convergence to a goal, non-penetration of obstacles, and passivity. The coverage of learning begins with low-level control parameters and progresses to higher-level competencies composed of combinations of skills. Learning for Adaptive and Reactive Robot Control is designed for graduate-level courses in robotics, with chapters that proceed from fundamentals to more advanced content. Techniques covered include learning from demonstration, optimization, and reinforcement learning, and using dynamical systems in learning control laws, trajectory planning, and methods for compliant and force control . Features for teaching in each chapter: applications, which range from arm manipulators to whole-body control of humanoid robots; pencil-and-paper and programming exercises; lecture videos, slides, and MATLAB code examples available on the author’s website . an eTextbook platform website offering protected material[EPS2] for instructors including solutions.

A modern look at state estimation, targeted at students and practitioners of robotics, with emphasis on three-dimensional applications.

Multimodal Perception and Secure State Estimation for Robotic Mobility Platforms Enables readers to understand important new trends in multimodal perception for mobile robotics This book provides a novel perspective on secure state estimation and multimodal perception for robotic mobility platforms such as autonomous vehicles. It thoroughly evaluates filter-based secure dynamic pose estimation approaches for autonomous vehicles over multiple attack signals and shows that they outperform conventional Kalman filtered results. As a modern learning resource, it contains extensive simulative and experimental results that have been successfully implemented on various models and real platforms. To aid in reader comprehension, detailed and illustrative examples on algorithm implementation and performance evaluation are also presented. Written by four qualified authors in the field, sample topics covered in the book include: Secure state estimation that focuses on system robustness under cyber-attacks Multi-sensor fusion that helps improve system performance based on the complementary characteristics of different sensors A geometric pose estimation framework to incorporate measurements and constraints into a unified fusion scheme, which has been validated using public and self-collected data How to achieve real-time road-constrained and heading-assisted pose estimation This book will appeal to graduate-level students and professionals in the fields of ground vehicle pose estimation and perception who are looking for modern and updated insight into key concepts related to the field of robotic mobility platforms.

We are starting to enter a post-COVID-19 life. While this pandemic has made everyone’s life challenging, it also expedited the transition of our everyday lives into a new form, often called “The New Normal.” Although many people often use the terminology, perhaps we still do not have consensus about what it is and what it should be like. However, one thing that is clear namely that robotics and artificial intelligence technologies are playing a critical role in this transition phase of our everyday lives. We saw the emergence of last-mile delivery robots on the street, AI-embedded service robots in restaurants, uninhabited shops, non-face-to-face medical services, conferences and talks in metaverses, and AI-based online education programs. This book is an edition that aims at serving researchers and practitioners in related fields with a timely dissemination of the recent progress in the areas of robotics and artificial intelligence. This book is based on a collection of papers presented at the 10th International Conference on Robot Intelligence Technology and Applications (RiTA), held at Griffith University in the Gold Coast, Queensland, Australia. The conference was held in a hybrid format on December 7–9, 2022, with the main theme “Artificial, Agile, Acute Robot Intelligence.” For better readability, the total of 41 papers are grouped into five chapters: Chapter I: Motion Planning and Control; Chapter II: Vision and Image Processing; Chapter III: Unmanned Aerial Vehicles and Autonomous Vehicles; Chapter IV: Learning and Classification; and Chapter V: Environmental and Societal Robotic Applications. The articles were accepted through a rigorous peer-review process and presented at the RiTA 2022 conference. Also, they were updated, and final versions of the manuscripts were produced after in-depth discussions during the conference. We would like to thank all the authors and editors for contributing to this edition.