CSE 571: AI-Robotics
Winter 2026
Tue & Thu 10:00-11:20 @ CSE2 271
Instructor:
Dieter Fox
Office hours: 11:30 - 12:30 PM Tue
Gates 204
fox@cs.washington.edu
Co-Instructor:
Jiafei Duan
Office hours: 4:00 - 5:00 PM Fri
duanj1@cs.washington.edu
Teaching Assistants:
- Helen Wang
Office hours: 12:30 - 1:30 PM Tue | Zoom
yiruwang@cs.washington.edu
- Marius Memmel
Office hours: TBD
memmelma@cs.washington.edu
Discussion:
Canvas:
https://canvas.uw.edu/courses/1861955
Please access Zoom class lectures and recordings via Canvas.
Discussion board:
https://edstem.org/us/courses/90290
Use this board to discuss the content of the course.
Feel free to discuss homeworks, projects, and any confusion over topics discussed in class. It is also acceptable to ask for clarifications about the statement of homework problems, but not about their solutions.
Textbook:
There is no required textbook for the class. Many of the lectures and
homework assignments will have associated papers and chapters from:
Probabilistic Robotics,
S. Thrun, W. Burgard, and D. Fox., MIT Press, Cambridge, MA, September 2005.
Homeworks:
There will be 2 homeworks with programming components done in Python.
- Assignment #1, released Thursday, January 8th, due Thursday, February 5th, 11:59PM
- Assignment #2, released Thursday, February 12th, due Thursday, March 5th, 11:59PM
Homework late policy: You can accumulate 6 late days without incurring
a penalty. Each day beyond that will incur a 20% penalty on that
assignment. No late days on the 2nd assignment.
While we
encourage students to discuss homeworks, each student must write up their own solution. It's fine to use a source for generic algorithms (with attribution), but it is not allowed to copy solutions to the problems. Additionally, students
may not post their code online. If we determine that a student posted their code online, they will get an automatic 50% reduction on the entire assignment (math + code) and if they copy code for the problems from another student or from online, they will get an automatic 0% for the entire assignment (and possibly reported to the college).
Projects:
There will be 2 projects done in
teams of 3 people and will account for 55% of your grade.
Project 1: Released
Thursday, January 15th, due
Thursday, February 5th, 11:59PM
Project 1 will be a guided project focused on probabilistic robotics concepts covered in Section 1.
Project 2: Open-ended research project
- Proposal due: Thursday, February 12th, 11:59PM
- Report due: Thursday, March 12th, 11:59PM
- Presentations (Poster + Demo): Finals Week
Project 2 can investigate any question related to robotics. We encourage ideas from your own research. Make sure your problem is well-defined with clear objectives.
Projects will be presented in a poster session at the end of the quarter.
Grading:
You will submit your assignments (both writeups and code) to canvas. You have all been enrolled into the class. Please check your email.
- Homeworks 40% (20% each)
- Project 1 (20%)
- Project 2 (35%)
- Proposal: 5%
- Report: 10%
- Presentation: 20%
- Attendance (5%)
- Participation (Bonus up to 5%)
Anonymous Feedback:
Please send your feedback via
https://feedback.cs.washington.edu/
Course Outline
Lectures
| Date |
Topic |
Slides |
Reading (textbook/papers) |
Homeworks & Projects |
|
Section 1: Probabilistic Robotics |
|
|
|
| Tue 06-Jan |
Introduction and Course Overview |
Intro |
|
|
| Thu 08-Jan |
Bayes Filters |
|
Book Chapters 1, 2 |
HW1 Released |
| Tue 13-Jan |
Motion and Sensor Models |
|
Book Chapter 5, 6 |
|
| Thu 15-Jan |
Particle Filters |
|
Book Chapter 6, 8, Pose-RBPF |
Project 1 Released |
| Tue 20-Jan |
Guest lecture |
|
|
|
| Thu 22-Jan |
Kalman Filters |
|
Book Chapter 3 |
|
| Tue 27-Jan |
Mapping |
|
Chapter 9 and 10, OctoMap |
|
| Thu 29-Jan |
Path Planning |
|
|
|
| Tue 03-Feb |
Buffer to finish |
|
|
|
| Thu 05-Feb |
|
|
|
HW1 Due, Project 1 Due |
|
Section 2: Learning-based Robotics Manipulation |
|
|
|
| Tue 10-Feb |
Introduction to Manipulation |
|
|
|
| Thu 12-Feb |
Basics of Pick-and-Place: Spatial Algebra & Kinematics |
|
|
HW2 Released, Project 2 Proposals Due |
| Tue 17-Feb |
Motion Planning: Sampling-Based Methods & CuRobo |
|
|
|
| Thu 19-Feb |
Deep Perception for Manipulation (Guest Lecture) |
|
|
|
| Tue 24-Feb |
Basics of Imitation Learning (Behavior Cloning, Covariate Shift, DAgger) |
|
|
|
| Thu 26-Feb |
Basics of Reinforcement Learning (TD Learning, Q-Learning, Bellman Equations) |
|
|
|
| Tue 03-Mar |
Deep Imitation Learning & Reinforcement Learning (Guest Lecture) |
|
|
|
| Thu 05-Mar |
Modern Robot Learning Approaches |
|
|
HW2 Due |
| Tue 10-Mar |
Robotics Foundation Models |
|
|
|
| Thu 12-Mar |
Recap of Section 1 & Section 2 |
|
|
Project 2 Report Due |
| Finals Week |
|
|
|
Project 2 Presentations (Poster + Demo) |