For Instructors
Principles of Robot Autonomy is designed to be taught. Whether you are running a full semester-long robotics course or folding a few chapters into an existing class, this page gathers everything you need to build your own course on top of the book.
Build your course
The book grew out of a decade of teaching at Stanford and the chapters map directly onto these courses, which you can use as reference syllabi, pacing guides, and sources of additional class materials:
Lecture slides
The book's chapters are accompanied by lecture slides in PDF and PPTX formats. The slides are organized by course and lecture, and you can download them for use in your own classes.
Exams, homeworks & solutions
To preserve their value as assessments, exams, homework sets, and their solutions are shared privately with instructors rather than posted publicly. If you are teaching a course and would like access, please feel free to contact us.
Adopting the book
We look forward to seeing Principles of Robot Autonomy in your course! If you are adopting the book, please reach out and let us know. We would love to hear about your experience, and we can also help publicize your course on this website.
Acknowledgments
Our beliefs about robot autonomy, and the way we present the material in the book and in these slides, have been largely informed by other excellent courses, textbooks, and lecture materials from across the community. We are deeply grateful to their authors and instructors. If you are building a course, the resources below are wonderful companions to this book.
Courses & lecture materials
Textbooks & notes
- D. E. Kirk, Optimal Control Theory: An Introduction
- F. Borrelli, A. Bemporad, M. Morari, Predictive Control for Linear and Hybrid Systems
- J. B. Rawlings, D. Q. Mayne, M. M. Diehl, Model Predictive Control: Theory, Computation, and Design
- R. S. Sutton, A. G. Barto, Reinforcement Learning: An Introduction
- J.-J. E. Slotine, W. Li, Applied Nonlinear Control
- S. M. LaValle, Planning Algorithms
- R. Siegwart, I. R. Nourbakhsh, D. Scaramuzza, Introduction to Autonomous Mobile Robots
- D. A. Forsyth, J. Ponce, Computer Vision: A Modern Approach
- R. Hartley, A. Zisserman, Multiple View Geometry in Computer Vision
- R. M. Murray, Optimization-Based Control (lecture notes)
Individual lectures also draw on many excellent surveys and research papers, credited on the slides where they appear.