CSE 571: AI-Robotics

Spring 2025

Tue & Thu 10:00-11:20 @ JHN 026

Instructor:

Dieter Fox
Office hours: 11:30 - 12:30 AM Tue
Gates 204
fox@cs.washington.edu

Teaching Assistants:

  • Jiafei Duan
    Office hours: 5:00 - 6:00 PM Tue
    Gates 325
    duanj1@cs.washington.edu
  • Chaoyuan Zhang
    Office hours: 2:00 - 4:00 PM Tue
    | Zoom
    cz86@cs.washington.edu


Discussion:
Canvas: https://canvas.uw.edu/courses/1716679

Please access Zoom class lectures and recordings via Canvas.

Discussion board: https://edstem.org/us/courses/56888/discussion

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 3 homeworks with programming components done in Python.

Late policy: You have a total of 6 late days for the whole quarter. But each assignment may be handed in up to 5 days late, after which a penalty of 20% of the maximum grade is applied per day.

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:
The projects will be done in teams of 2-3 people and will account for 50% of your grade. Project can be investigating any question related to robotics. We encourage ideas from your own research. Make sure your problem is well-defined with clear objectives.
More details can be fund in this document. Deliverables include a proposal, a midterm milestone and a final report. 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 45% (15%, 15%, 15%)
  • Final Project (50%)
  • Participants (10%)


Anonymous Feedback:
Please send your feedback via https://feedback.cs.washington.edu/


Course Outline

Lectures

Date Topic Slides Reading (textbook/papers) Homeworks & Projects
Tue 01-Apr Introduction
Thu 03-Apr Particle Filters / Bayes Filters
Tue 08-Apr Bayes Filters
Thu 10-Apr Motion and Sensor Models, PF revisited
Tue 15-Apr Kalman Filters
Thu 17-Apr Kalman Filters
Tue 22-Apr Mapping
Thu 24-Apr Mapping
Tue 28-Apr Mapping
Thu 01-May Exploration
Tue 05-May Sampling-based Planning
Thu 08-May Motion-planning for Manipulators
Tue 13-May Deterministic Planning
Thu 15-May Deterministic Planning -
Tu 20-May MDP, Inverse Reinforcement Learning
Thu 22-May Behavior Cloning
Tu 27-May Imitation Learning and Policy Gradient
Thu 29-May Readings in Generative AI for Robotics
Mon 02-Jun Project Poster Presentation
Thu 05-Jun Project Poster Presentation
Fri 06-Jun End of Course