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)