CS 685
Autonomous Robotics
Time/Location: Wednesday 4:30-7:10pm,
Planetary Hall 212
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, design of basic control
strategies and motion planning.
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 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
S. LaValle: Planning Algorithms, Cambridge Press, http://planning.cs.uiuc.edu/
Grading:
Homeworks and Projects 65%
Exam 35%
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
Course Outcomes:
Students will gain understanding of theory and computational principles
of robotics systems. These include:
Motion control, sensor and motion models
Bayes filters, Kalman Filter, Particle filters
Simultaneous localization and mapping
Manipulation and motion planning
Markov Decision Processes
Reinforcement learning
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.
GMU Honor Code System and Policies
at
https://oai.gmu.edu/mason-honor-code/.