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