Time/Location: Monday 4:30-7:10pm,   Robinson Hall B368
Instructor: Jana Kosecka
Office hours:  2-3pm Tuesday
Office: 4444 Engineering Building
e-mail: kosecka@gmu.edu, 3-1876
Course web page: http://www.cs.gmu.edu/~kosecka/cs685/

The course covers basic principles of design and practice of intelligent robotics systems. We will cover algorithms for the analysis of the data obtained by vision and range sensors, basic principles of modelling kinematics and dynamics and design of basic control strategies. Issues of uncertainty modelling, state estimation, probabilistic inference will be introduced and examined in the context of localization and map making problems. The last part of the course covers the basics and some examples of learning approaches where robotic agents can learn how to achieve complex goals in reinforcement learning framework.

The topics and techniques covered are relevant for students interested in robotics, computer vision and artificial intelligence. The course will comprise of lectures by the instructor, homeworks and presentations of the selected research publications by students. The grade will be based on homeworks and final presentation of the project. The projects will involve implementation of a systems in a robot simulator and/or the actual (mobile) robot.

Schedule, Homeworks, Handouts

Prerequisites:

CS 580 Artificial Intelligence
optional prerequisites - Computer Vision, Analysis of Algorithms
Students taking the class should be comfortable with linear algebra, calculus and probability and some optimization

Recommended Textbooks:

R. Siegwart and I. Nourbakhsh: Autonomous Mobile Robots, Second Edition, MIT Press, 2011, http://www.mobilerobots.org

Additional Resources:

S. LaValle: Planning Algorithms, Cambridge Press, http://planning.cs.uiuc.edu/

Grading:

Homeworks and Projects 65%
Exam 35%  
The homeworks should be handed in on time.  Late submissions are accepted (as opposed to no submissions) but will incur late submission penalty.

Other recommended books:

S. Russell and P. Norvig: Artificial Intelligence,  Prentice Hall, 1995
R. Arkin: Behavior-Based Robotics, MIT Press, 1998
R. Sutton and A. G. Barto: Introduction to Reinforcemen Learning. MIT Press, 1998. web site

Academic Integrity:

The integrity of the University community is affected by the individual choices made by each of us. GMU has an Honor Code with clear guidelines regarding academic integrity. Three fundamental and rather simple principles to follow at all times are that: (1) all work submitted be your own; (2) when using the work or ideas of others, including fellow students, give full credit through accurate citations; and (3) if you are uncertain about the ground rules on a particular assignment, ask for clarification. No grade is important enough to justify academic misconduct. Plagiarism means using the exact words, opinions, or factual information from another person without giving the person credit. Writers give credit through accepted documentation styles, such as parenthetical citation, footnotes, or endnotes. Paraphrased material must also be cited, using MLA or APA format. A simple listing of books or articles is not sufficient. Plagiarism is the equivalent of intellectual robbery and cannot be tolerated in the academic setting. If you have any doubts about what constitutes plagiarism, please see me.

CS department Honor Code can be found here.