This book takes a deep learning-centric approach to the evolving field of robotics, highlighting how artificial intelligence (AI) has dramatically reshaped the landscape of robotics. AI has fundamentally altered how robots perceive, understand, and interact with the world, opening the door to innovative solutions for complex, long-standing problems. This book equips you with the knowledge to reframe traditional robotics challenges as AI problems and provides a comprehensive guide for solving them using cutting-edge AI techniques, particularly in an era of large-scale foundation models.
The first part of the book introduces general-purpose robotics, detailing how robots are modeled and how the concept of physical intelligence is connected to the development of artificial general intelligence. It offers an in-depth overview of the current state of the field, the challenges that remain, and the exciting directions in which robotics is heading. The book then delves into the core robotics problems, breaking them down into AI problems and explaining how modern AI solutions can address them. A major focus is on robot perception and sensing, providing insights into how robots understand and interact with their environments. The book explores convolutional neural networks and vision transformers to tackle complex tasks such as segmentation, classification, and detection in both 2D and 3D spaces.
In addition, it covers the application of large language models and multimodal models in robotics, demonstrating how these technologies can be adapted to enhance reasoning and control in robots. The book also delves into simulation, localization, mapping, and navigation, reimagining these as deep learning challenges and discussing recent advancements in research. Furthermore, reinforcement learning and control are explored in the context of how robots learn through trial and error, self-play, and real-time interaction with their environment.
The second half of the book focuses on the practical applications of robotics in specialized domains. Here, you will apply the techniques from the first part to real-world use cases, gaining full-stack knowledge of building advanced robotic systems. Each chapter explores key application areas, such as self-driving vehicles, industrial manipulation, and humanoid robotics. For each domain, you’ll learn how to design robotic systems, understand the algorithms used in both research and industry, and weigh the trade-offs involved in performance, efficiency, and reliability.
The book concludes with a forward-looking discussion on the operational aspects of data-driven robotics, including infrastructure, safety, and maintenance. It also offers insights into the future trajectory of robotics and machine learning, exploring potential breakthroughs and new challenges on the horizon.
In summary, this book offers a thorough exploration of the intersection between machine learning and robotics, providing both theoretical insights and practical strategies to navigate the challenges of implementing modern AI techniques in robotics.
What You Will Learn
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Master machine learning applications in robotics, covering essential areas such as perception, control, localization, planning, and end-to-end learning
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Dive deep into system design and explore the algorithmic and hardware considerations necessary for building efficient AI-integrated robotic systems
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Gain hands-on experience with real-world robotics applications in self-driving vehicles, industrial manipulation, and humanoid robots, learning the design, algorithms, and trade-offs involved
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Understand how AI and machine learning can benefit current robotics research and impact organizations developing new robotic technologies
Who This Book Is For
This book is ideal for software and AI engineers eager to explore the integration of machine learning with robotics, seasoned robotics and mechanical engineers looking to stay ahead by incorporating modern AI techniques into their work, as well as investors, executives, and decision-makers seeking an in-depth understanding of this dynamic and rapidly evolving field. More information…
