Course Schedule

Date Topic Readings Due Dates (11:59pm)
Mon, Jan 4 Introduction, Successful Robot Systems, Warmup Asssignment Discussion Chapter 1  
Wed, Jan 6 Review: Bayesian Statistics, Bayes Rule, Bayes Filters, basic math! Chapter 2  
Mon, Jan 11 Our first example: Monte Carlo Localization and efficient variants Chapter 4.1, 4.3; Chapter 8.1, 8.3-8.5  
Wed, Jan 13 Discussion class: Interesting data sets for your project; discussion of potential projects   Warm-up Assignment due
Wed, Jan 20 Slides on motion and perception and Kalman Filters 1: Intuition, basic math Chapter 3.1-3.3  
Mon, Jan 25 Kalman Filters 2: Application to Localization and Tracking; Probabilistic models of robotic sensors, robotic motion Chapter 4 and 5 (okay to skip derivations) Project proposals submitted.
Wed, Jan 27 Some final thoughts on historgram filters and Binary bayes filters and occupancy grid maps Chapter 4.2, Chapter 9.1-9.3  
Mon, Feb 1 SLAM: Basic problem, Kalman Filter solution, data association Chapter 10.1-10.2  
Wed, Feb 3
Alex Teichman
SLAM: Graphical methods for SLAM and information form of the Kalman Filter; relation to nonlinear convex optimization Chapter 11 project snippet #1
Mon, Feb 8 SLAM: Particle Filter and Rao-Blackwellization (FastSLAM) All of Chapter 13, except 13.4 and FastSLAM 2.0 project snippet #2
Wed, Feb 10
Alex Teichman
Exam prep session Practice Exam, solutions  
Mon, Feb 15 (no class, President's day)   project snippet #3
Wed, Feb 17 Exam    
Mon, Feb 22 Discrete data association and information form   project snippet #4
Wed, Feb 24
Alex Teichman
Mon, Mar 1 Probabilistic Motion Planning and Navigation   project snippet #5
Wed, Mar 3 POMDPs    
Mon, Mar 8 Policy search techniques & Course Summary    
Wed, Mar 10 Project Presentations    
Sun, Mar 14, 23:59pm     Final Project Report (absolutely no extensions)

Course overview
Time and location
Course materials