CSE 571: Robotics

Autumn, 2016

MW 1:30-2:50, EEB 045

Dieter Fox

Office hours: W 3:00-4:00PM, CSE 586

TA: Arunkumar Byravan

Office hours: M 10:00-11:00AM, CSE 503

Email and discussion:
Class email list: cse571a_au16 at uw     [archives]

Discussion board (moderated by TA)

Use this board to discuss the content of the course. Feel free to discuss homeworks and projects from past incarnations of the course, 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 reading material from:

Probabilistic Robotics, S. Thrun, W. Burgard, and D. Fox., MIT Press, Cambridge, MA, September 2005.


Date Topic Slides Reading (textbook/papers) Homework
28-Sep Introduction Intro, Prob Chapters 1, 2 -
3-Oct Gaussian Processes GP Chapter 2 of (Rasmussen book) -
5-Oct Bayesian Filtering Bayes Filters Chapters 1, 2 HW1 assigned
10-Oct Motion and sensor models Motion, sensors Chapter 5 (skip 5.3 and derivations), Chapter 6 -
12-Oct Kalman Filters I (Linear, EKF) Kalman Chapters 3 and 7 (skip 3.5 and derivations) -
17-Oct Kalman Filters II (EKF, UKF) Chapters 3 and 7 (skip 3.5 and derivations) -
19-Oct Particle Filters Particle filters Chapters 4 and 8 (skip derivations) HW2 assigned
24-Oct Occupancy Mapping Occupancy mapping Octomaps, Chapter 9 -
26-Oct Mapping: Signed distance functions SDF KinectFusion -
31-Oct Mapping: EKF SLAM SLAM Chapter 10 -
2-Nov Mapping: Graph-SLAM and Fast-SLAM Fast-SLAM Chapter 11 and 13 -
7-Nov RGBD-Mapping RGBD-Mapping RGBD-Mapping paper -
9-Nov Rao-Blackwellized Particle Filters for tracking RBPF BallTracking
14-Nov Exploration, deterministic planning Exploration, Det-Planning Motion planning, Multi-robot -
16-Nov Deterministic planning: D* Det-Planning D* -
21/22-Nov Project - Midterm meetings - - -
21-Nov Sampling-based Planning Samping-Planning RRT paper, Non-Holonomic RRT HW #3 assigned
23-Nov Markov Decision Processes, IOC MDPs, IOC Book chapter 14 -
28-Nov Manipulation and Motion Planning Manipulation - -
30-Nov Deep Learning DeepRL Silver DQN 1, DQN_2 -
5-Dec Deep Learning DeepRL Silver - -


Assignments will be submitted via Catalyst dropbox.

Late policy: Assignments may be handed in up to five days late, at a penalty of 10% of the maximum grade per day.


Projects will be done in teams of two or three. We encourage any ideas related to robotics or from your own research. More details on the project teams and their topics can be found here.

Grading (Tentative):

  • Homeworks (45%)
  • Project (45%)
  • Class participation (10%)

Anonymous Feedback:

You may submit anonymous feedback at any time on any aspect of the course here.