CSE 571: AI-Robotics

Spring 2024

Tue & Thu 9:00-10:20 @ LOW 101

Instructor:

Dieter Fox
Office hours: 10:30 - 11:30 AM Tue
Gates 204
fox@cs.washington.edu

Teaching Assistants:

  • Yi Li
    Office hours: 5:00 - 6:00 PM Wed
    Gates 274
    yili18@cs.washington.edu
  • Wentao Yuan
    Office hours: 10:30 - 11:30 AM Thu
    Gates 374 | Zoom
    wentaoy@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 50% (15%, 20%, 15%)
  • Final Project (50%)
  • Participants (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: Sensing the World
Tue 26-Mar Introduction
Thu 28-Mar Particle Filters / Bayes Filters PF, Bayes Filters Book Chapters 1 and 8.3
Tue 02-Apr Bayes Filters Motion and sensor models Book Chapter 2, 5, 6 HW1 release
Thu 04-Apr Motion and Sensor Models, PF revisited Sensor models, Particle filters Book Chapter 6, 8, Pose-RBPF
Tue 09-Apr Kalman Filters Kalman filters Book Chapters 3 and 7.4
Thu 11-Apr Kalman Filters " - "
Tue 16-Apr Mapping Mapping Chapter 9 and 10, OctoMap HW1 due
Thu 18-Apr Mapping
Section 2: Acting in the World
Tue 23-Apr Exploration Exploration HW2 release
Thu 25-Apr Deterministic and Sampling-based Planning Project proposal due
Tue 30-Apr Manipulation and Grasping
Thu 02-May Manipulation and Grasping
Tue 07-May Markov Decision Process
Thu 09-May Reinforcement Learning
Tue 14-May Imitation Learning HW2 due HW3 release
Thu 16-May Behavior Cloning Project midterm report due
Section 3: Frontier Research
Tue 21-May Readings in Generative AI for Robotics
Thu 23-May Readings in Generative AI for Robotics
Tue 28-May Readings in Generative AI for Robotics HW3 due
Thu 30-May Readings in Generative AI for Robotics
Wed 5-Jun Project Poster Presentation
Fri 7-Jun Project final report due