Robot Learning

From Concept to Application
Erscheinungsjahr 2027
ISBN: 978-981-9209-93-4
Verlag: Springer Verlag, Singapore

Buch, Englisch, 331 Seiten, Format (B × H): 155 mm x 235 mm

For decades, traditional robotics relied heavily on meticulously hand-engineered control systems. Robots were programmed to perform specific tasks in highly structured environments. However, the past decade has witnessed a paradigm shift fuelled by advancements in artificial intelligence, particularly in areas like deep learning and reinforcement learning. We have moved from programming robots to learning robots. This book is the definitive guide for researchers, engineers, and graduate students who want to design robots that see, think, and act with unprecedented autonomy.

The book opens with a rigorous yet accessible foundation in robot kinematics, dynamics, and learning paradigms. It journeys through four major robotic platforms:

• Wheeled Robots – robust traversability representation, multi-sensor perception, end-to-end decision-making, and real-world autonomous navigation.

• Legged Robots – bio-inspired learning, trajectory generator-based hierarchical learning, multi-task learning, and learning whole-body motion control.

• Robot Arms – learning grasping, learning grasp-stacking, learning replenishment, learning welding, and learning language-guided manipulation skills.

• Aerial Robots – aerial perception, UAV target tracking, and drone patrolling.

Each chapter blends theory with detailed case studies. Whether you are an academic researcher building the next generation of robots, a developer deploying autonomous robots, or an engineer turning a prototype into a product, this book offers the theory, algorithms, and practical case studies you need to bring robot learning to bear on real robotic systems.

Zielgruppe

Research

Autoren/Hrsg.

Zhang, Wei

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

"Chapter 1:Foundations of Robot Learning".- "Chapter 2:Robot Learning for Ground Robots".- "Chapter 3:Robot Learning for Legged Robots".- "Chapter 4:Robot Learning for Manipulation".- "Chapter 5:Robot Learning for Aerial Vehicles".- "Chapter 6:Conclusions and Future Trends".

Über Autor(innen)

Wei Zhang, Professor at Shandong University; IEEE Senior Member. His principal research interests lie in the domains of artificial intelligence and robotics, with a particular emphasis on developing algorithms and frameworks that enable intelligent perception, decision-making, and learning in autonomous systems. In recent years, he has served as Principal Investigator on several Major/Key Programs of the National Natural Science Foundation of China (NSFC), National Key Research and Development Program of China, and the Major Science and Technology Innovation Project of Shandong Province. He has published landmark papers in top-tier journals and conferences such as TPAMI, TRO, IJCV, NeurIPS, CVPR, ICCV, IJCAI, AAAI, CoRL, RSS and ICRA. To date, he holds over 60 granted invention patents across China, the United States, and Japan, and has received more than 10 academic awards including Best Paper Awards and other scientific prizes.