CSE 571: Robotics

Spring 2022

Tue & Thr 10:00-11:20AM @ CSE2 G04


Dieter Fox
Office hours: 11:30-12:30pm Tue
Gates 204

Teaching Assistants:

Canvas: https://canvas.uw.edu/courses/1545402

Please access Zoom class lectures and recordings via Canvas.

Discussion board: https://edstem.org/us/courses/21585/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.

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.

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 10% of the maximum grade is applied per day.

The projects will be done in teams of 2-3 and will account for 40% of your grade. This quarter we provide two project options:
(1) complete two guided projects OR
(2) do a single open-ended project. See the timeline below for deliverables.

Guided Projects: You will explore some classic problems in robotics via completing a set of tasks designed by the TAs.

  • Guided Project #1, due Mon, May 2, 11:59PM (PDF, Code)
  • Guided Project #2, due Tue, Jun 7, 11:59PM (PDF, Code)

Open-ended Project: You will propose your own problem, design the solution and present the results. We encourage ideas related to robotics and your own research.

  • Open-Ended Project, see timeline below (PDF)

Final Presentations: Open-Ended and Guided Project 2 presentations will be held on Mon, Jun 6, 10:30am-12:30pm.

You will submit your assignments (both writeups and code) to gradescope. You have all been enrolled into the class. Please check your email.
  • Homeworks (60%)
  • Project (40%)

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

Course Outline


Date Topic Slides Reading (textbook/papers) Homeworks & Projects
29-Mar Introduction Intro Chapter 1, 2 -
31-Mar Probabilities, Bayes Rule and Filters Prob Chapters 1, 2 -
4-Apr HW1 Posted
5-Apr Bayes Filters and Gaussian Processes GP Chapter 2 (Rasmussen book), GP-BayesFilters, GP-Control -
7-Apr Gaussian Processes, Neural Networks Neural-nets Chapter 6, 9 of Deep Learning -
11-Apr Guided Project 1 Posted
12-Apr Motion and Sensor Models Motion, Sensor Chapter 5, 6 -
14-Apr Sensor Models, Kalman Filters (linear, EKF) Kalman Chapters 3 and 7 (skip 3.5 & derivations) -
15-Apr Guided Project 1 Teams Due
Open-Ended Teams & Proposal Due
19-Apr Kalman Filters (linear, EKF) Chapters 3 and 7 (skip 3.5 & derivations) HW1 Due
21-Apr Particle Filters I Particle Filters Chapters 4 and 8 (skip derivations) -
22-Apr HW2 Posted
26-Apr Particle Filters, Occupancy Mapping Occupancy Mapping Chapter 9, Octomaps -
28-Apr SLAM SLAM Chapter 10 -
2-May Guided Project 1 Due
Guided Project 2 Posted
3-May SLAM and Graph-SLAM - GTSAM, Chapter 11 and 12 -
5-May Fast-SLAM Fast-SLAM+Pose-RBPF - -
6-May Open-Ended Project Mid-term Report Due
10-May Pose-RBPF, Exploration Exploration - -
11-May HW2 Due
12-May Exploration Exploration Multi-robot, Curiosity-driven learning, Object modeling -
17-May Deterministic Planning Det Planning Motion planning, A* slides from 473 -
19-May Sampling-based Planning Sampling-based Planning Complex motion planning, Anytime and replanning A*, RRT -
24-May Task and Motion Planning
(Guest Lecture by Caelen Garett)
26-May Learning to Grasp
(Guest Lecture by Arsalan Mousavian)
Grasping GraspNet, Clutter -
31-May MDP, Inverse Reinforcement Learning MDP-IOC Book Chapter 14 -
2-Jun Recap Recap
3-Jun HW3 Due
6-Jun Guided Project 2 Presentation
Open-Ended Project Presentation
7-Jun Guided Project 2 Report Due
Open-Ended Project Final Report Due