Robotics: Perception & Planning

Description

This textbook provides a comprehensive introduction to the core algorithms and paradigms of robotics, particularly machine perception and planning. It covers foundational topics such as machine learning, deep learning, path planning, reinforcement learning, mapping, object detection, stereo vision, sensor fusion, SLAM, and multi-robot systems. Designed for both undergraduate and graduate students, the book is intended for readers seeking a deep, principled understanding of robotics. The central teaching philosophy is to justify each algorithm from first principles—explaining not just how an algorithm works, but why it works and under what conditions it is optimal. Rather than treating methods as black boxes, the book begins with the foundational goals of robotics and derives each algorithm as a rational solution to those goals. This approach empowers students to identify hidden assumptions or gaps in the justifications and to recognize when an algorithm may be suboptimal. From there, they are equipped to select a more appropriate alternative, adapt the existing method, or develop an improved one. In this way, the book goes beyond teaching established techniques—it cultivates the analytical mindset essential for advancing the field. Throughout the text, intuitive explanations, visualizations, and concrete examples reinforce key ideas and promote lasting insight. The goal is to equip students with the conceptual and technical tools needed to analyze, implement, and improve robotics algorithms with both confidence and rigor. Review: Great Explanations - Great book! Explains concepts very well. I feel like I understand ever decision behind each algorithm, not just the basic steps Review: Great Guide for Perception and Planning - I have 20 yrs of IT experience but I’ve had minimal exposure to robotics besides ML. I read this book to learn about machine perception and planning because I’m considering taking college robotics courses to upskill. The book is structured like a textbook but it was clearly written and easy to understand. It covers a ton of content. I really like that the book was technical with equations, algorithms and real world scenarios - not just boring theory. The authors explain the prerequisites in the preface: one university class (or equivalent) in statistics, multi variable calc, linear algebra, computer programming and robotics. I never took robotics or multi variable calc so I struggled in some sections in this book where those were necessary. Overall - excellent book, well written, lots of information.