Textbook in PDF format Robotic assembly is a fundamental technique in a wide range of manufacturing processes. While the classical rule-based robotic assembly methods have reached an elevated level of maturity, they have limitations when it comes to handling complex tasks with a large variety of components, which significantly restricts their applications. Fortunately, in the past decade, Deep Learning and Artificial Intelligence have undergone transformative breakthroughs, showcasing remarkable success across various domains such as computer vision and natural language processing (NLP). These data-driven approaches have demonstrated great generalizability and have paved the way for significant advancements in robotic assembly by improving the robot capability of perception, planning and control to meet the requirement of increasing complexity and variety of assembly tasks. This book is devoted to presenting the latest advances in robotic intelligent assembly strategies, with a focus on pig-in-hole tasks. The book covers a range of perspectives related to robotic PiH assembly strategies, including perception, model-based control, and learning-based control. The authors have put great effort into organizing previous research works in a systematic manner, offering readers a comprehensive understanding of the field. The first part of the book introduces the background and drawbacks of existing approaches. The second part delves into improving the accuracy and computational efficiency of learning-based multi-view stereo and narrowing the simulation-to-reality domain gap for depth sensors. These techniques improve the robot’s ability to perceive the geometry information of the assembly environment. The third part describes the model-based strategies. And the fourth part presents state-of-the-art learning-based strategies for general PiH tasks. These approaches enhance the generalizability of robot assembly by utilizing learning-based algorithms. By leveraging the power of Deep Learning, robots can adapt to new assembly scenarios and handle a wider range of components efficiently. The authors also explore techniques for accelerating the training process of existing learning algorithms, enabling faster deployment and improved performance. This monograph is intended for undergraduate and postgraduate students interested in robotic intelligent assembly, researchers studying robotic intelligent assembly algorithms, and electronic, mechanical, and computer engineers engaged in industrial robot-assisted assembly