Robotics

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.