CSE 571: Robotics

Spring 2020

TTh 10:00-11:20, online meetings via Zoom.


Dieter Fox
Office hours: Fri 9-10

Teaching Assistants:

Email and discussion:
Canvas: https://canvas.uw.edu/courses/1372020

Please access Zoom class lectures via Canvas.

Discussion board: https://us.edstem.org/courses/424/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 4 homeworks with programming components done in Python.
Late policy: Assignments may be handed in up to five days late, at a penalty of 10% of the maximum grade per day.

  • Homework #1, due Mon, Apr 20, 11:59PM (PDF, Code)
  • Homework #2, due Wed, May 13, 11:59PM (PDF, Code)
  • Homework #3, due Sun, June 7, 11:59PM (PDF, Code)

Projects will be done in teams of two or three. We encourage ideas related to robotics and your own research. Project details and deliverables are now available.

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) Homework/Project
31-Mar Introduction Intro Chapter 1, 2 of Probabilistic Robotics -
2-Apr Bayesian state estimation, Filtering Prob Chapters 1, 2 of Probabilistic Robotics -
7-Apr Gaussian Processes GP Chapter 2 (Rasmussen book), GP-BayesFilters, GP-Control HW1 posted
9-Apr Gaussian Processes, Neural Networks Neural-nets Chapter 6, 9 of Deep Learning -
14-Apr Neural Networks Neural-nets Chapter 6, 9 of Deep Learning -
16-Apr Motion and Sensor Models Motion models, sensor models Chapter 5, 6 of Probabilistic Robotics -
17-Apr Guided Project 1 Proposal Due
Open-ended Project Proposal Due
20-Apr HW1 Due
21-Apr Kalman Filters (linear, EKF) Kalman filters Chapters 3 and 7 (skip 3.5 and derivations) -
23-Apr Particle Filters Particle filters Chapters 4 and 8 (skip derivations) -
28-Apr Particle Filters Guided Project 1 Mid-progress Report Due
30-Apr Occupancy maps, SLAM Occupancy mapping, SLAM Chapters 9 and 10, Octomaps
5-May -
7-May Graph-SLAM and Fast-SLAM Fast-SLAM GTSAM, Chapter 11 and 13 -
10-May Guided Project 1 Final Report Due
Open-ended Project Mid-progress Report Due
11-May HW3 posted
Guided Project 2 posted
12-May Fast-SLAM, Exploration Exploration Multi-robot, Curiosity-driven learning, Object modeling -
14-May Deterministic planning Det-Planning Motion planning, A* slides from 473 -
15-May HW2 Due
19-May Sampling based planning w/ RRTs RRT Complex motion planning, Anytime and replanning A*, RRT page -
21-May RRTs, Markov Decision Processes MDPs Book chapter 14 -
26-May MDPs, Inverse Reinforcement Learning IOC -
28-May Task and motion planning TAMP -
2-Jun Learning to grasp Grasp GraspNet, clutter -
4-Jun Summary Summary SE3-Nets, Langrangian Networks -