CSE 571: Probablistic Robotics
Autumn, 2015
MW 1:30-2:50, EEB 045
Dieter Fox
Office hours: W 3:00-4:00, CSE 586
TA: Arunkumar Byravan
Office hours: M 3:30-4:30, CSE 503
Email and discussion:
Class email list: cse571a_au15 at uw [archives]Please send any e-mail about the course to cse571a_au15-instr at cs.
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.
Textbook:
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.
Lectures
| Date | Topic | Slides | Reading (textbook/papers) | Homework |
| 30-Sep | Introduction | Intro | - | - |
| 5-Oct | Bayesian state estimation, Filtering | Prob | Chapters 1, 2 | - |
| 7-Oct | Motion models, Gaussian Processes | Motion, GP | Chapters 5 (skip 5.3 & derivations), Chapter 2 (Rasmussen book) | HW #1 assigned |
| 12-Oct | Observation models | Observations | Chapter 6 (skip derivations) | - |
| 14-Oct | Kalman Filters (Introduction, EKF) | Kalman | Chapters 3 and 7 (skip 3.5 & derivations) | - |
| 19-Oct | Particle Filters | Particle filters | Chapters 4 and 8 (skip derivations) | HW #2 assigned |
| 21-Oct | Particle Filters contd, discrete filters | Particle filters | Chapters 4 and 8 (skip derivations) | - |
| 26-Oct | Gaussian Process Bayes Filters | GP-BayesFilters, Occupancy mapping | GP filtering, GP learning, Chapter 9 | - |
| 28-Oct | EKF-SLAM | SLAM | Chapters 10, 11 | - |
| 2-Nov | TBD | - | - | - |
| 4-Nov | RGBD-SLAM (Luis) | RGBD | - | - |
| 9-Nov | Fast-SLAM | Fast-SLAM | - | - |
| 11-Nov | NO CLASS: Veteran's day | - | - | - |
| 16-Nov | Rao-Blackwellized Particle Filters | RBPF | TransportationRoutines, BallTracking | - |
| 18-Nov | Mapping with Truncated Signed Distance Functions | TSDF | KinectFusion, DynamicFusion | - |
| 20-Nov | Project - Midterm meetings | - | - | - |
| 23-Nov | Exploration | Exploration | Multi-robot | - |
| 25-Nov | Deterministic Planning | Det-Planning | Deterministic planning | - |
| 30-Nov | Sampling-based Planning | Samping-Planning | RRT paper, Non-Holonomic RRT | HW #3 assigned |
| 2-Dec | Markov Decision Processes | MDPs | Book chapter 14 | |
| 7-Dec | (Inverse) Reinforcement Learning | RL, IOC | ||
| 9-Dec | Summary | Summary | ||
| 14-Dec | Project - Final presentations | - | - | - |
Homeworks:
Assignments will be submitted via Catalyst dropbox.
- Homework #1, due Sun, Oct 18, 11:59 PM (Code)
- Homework #2, due Wed, Oct 28, 1:30 PM (Code)
- Homework #3, due Wed, Dec 16, 11:59 PM (Code)
Project:
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 can be found here.
Grading:
- Homeworks (45%)
- Project (50%)
- Class participation (5%)
Anonymous Feedback:
You may submit anonymous feedback at any time on any aspect of the course here.