CSE 571: AI-Robotics

Winter 2026

Tue & Thu 10:00-11:20 @ CSE2 271

Instructor:

Dieter Fox
Office hours: Tue/Thu 11:30 - 12:00 PM
Gates 204
fox@cs.washington.edu

Co-Instructor:

Jiafei Duan
Office hours: 4:00 - 5:00 PM Fri (starts week of Feb 10)
duanj1@cs.washington.edu

Teaching Assistants:

  • Helen Wang
    Office hours: 1-2 pm Tuesday | Zoom
    yiruwang@cs.washington.edu
  • Marius Memmel
    Office hours: 1:00 PM Thu
    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.

Modern Robotics: Mechanics, Planning, and Control, K. M. Lynch and F. C. Park, Cambridge University Press, 2017.


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%
  • Participation (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 Bayes Book Chapters 1, 2 HW1 Released
Tue 13-Jan Motion and Sensor Models Models Book Chapter 5, 6
Thu 15-Jan Particle Filters ParticleFilters Book Chapter 6, 8, Pose-RBPF Project 1 Released
Tue 20-Jan Kalman Filters Kalman Book Chapter 3,7
Thu 22-Jan Mapping Mapping Book Chapter 9,10
Tue 27-Jan Mapping Book Chapter 9,10
Thu 29-Jan Path Planning Det-planning Motion planning, A* slides from 473
Tue 03-Feb Path Planning RRT RRT paper
Thu 05-Feb Guest lecture by Luca Carlone: From SLAM to Spatial AI: Enabling Human-level Scene Understanding for Robotics HW1 Due, Project 1 Due
Section 2: Learning-based Robotics Manipulation
Tue 10-Feb Introduction to Manipulation Manipulation Robot hardware overview
Thu 12-Feb Spatial Algebra & Rigid-Body Transformation Transformations Modern Robotics Chapter 3 HW2 Released, Project 2 Proposals Due
Tue 17-Feb Homogeneous Matrix Transformation & Kinematics
Thu 19-Feb Guest Lecture: Wenlong Huang (Stanford)
Tue 24-Feb Basics of Reinforcement Learning (TD Learning, Q-Learning, Bellman Equations)
Thu 26-Feb Basics of Imitation Learning (Behavior Cloning, Covariate Shift, DAgger)
Tue 03-Mar Model Architectures for Robot Learning (Part 1)
Thu 05-Mar Guest Lecture: Homanga Bharadhwaj (JHU) HW2 Due
Tue 10-Mar Model Architectures for Robot Learning (Part 2)
Thu 12-Mar Recap of Section 1 & Section 2 Project 2 Report Due
Mon 16-Mar 10:30am-12:20pm: Poster + Demo Session Project 2 Presentations