Course Schedule

Date Topic Readings Level of difficulty Due Dates
Wed, March 31 Introduction, Overview, and Problem Definition   *  
Mon, April 5 Probabilistic estimation of state, with application in robot localization Chap 2 and Chap 8 **  
Wed, April 7 Particle filters and Monte Carlo Localization Chap 4, Chap 5, and Chap 6 **  
Mon, April 12 More on Monte Carlo localization (pitfalls, extension to variable numbers of entities) -- no new slides   ***  
Wed, April 14 The Kalman Filters Solution with Maximum Likelihood Data Association Chap 3, and Chap 7 ****  
Mon, April 19 Brainstorming session: What are cool projects?   * Warn-Up Project
Wed, April 21 Scaling up: Binary bayes filters and occupancy grid maps Chap 9 *  
Mon, April 26 Scaling up: the SLAM Problem and the classical EKF solution Chap 10 *** Project Proposal and Assignment 1
Wed, April 28 Information filters approaches 1 (the Lu Milios Algorithm) Chap 11 ***  
Mon, May 3 Information filters approaches and data association   ****  
Wed, May 5 SLAM and Particle Filters FastSLAM paper ****  
Mon, May 10 Probabilistic Planning and Control: Markov Decision Processes and probabilistic robot path planning and robot explorationPOMDP slides This paper is optional, and not required for the midterm: Paper on Parti-Game ** Assignment 2
Wed, May 12 Action Mechanisms for Multi-Robot Coordination in MDPs   ****  
Mon, May 17 Midterm Exam      
Wed, May 19 Robot motion in partially observable domains: the standard POMDP solution   *****  
Mon, May 24 Scalable POMDP techniques for robot control   ****  
Wed, May 26 Guest lecture   ****  
Wed, June 2 Summary and Wrap-up   *  
Sat, June 5, 10am- Prohect Presentations(?)      
Mon, June 7       Project Report due (no extensions!)

Course overview
Time and location