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!)