CS226 Statistical Techniques in Robotics
CS 226 is a graduate-level course that
covers statistical techniques in robotics.
Probabilistic robotics is a hot research area in robotics these days.
In the 1980, the dominant paradigm in robotics was model-based. In the
1990s, the paradigm shifted to behavior based. Now one of the key new
direction in robotics takes place at the intersection of statistics
and robotics. Statistical techniques define the state of the art in
many robotic applications. They are robust in practice, and they also
have a sound mathematical basis.
The goal of this course is to expose you to the basics in
probabilistic robotics. Successful students will be able to understand
the mainstream literature, derive and prove the correctness of
statistical algorithms, and have gained in-depth experience with
practical statistical algorithms.
As in past years, we seek to leverage student projects to a conference-publishable level. You can find the Web page for a previous version of this course
Who Should Attend?
The course should be of
interest to anyone seeking to develop robust robot software, and anyone
who is interested in real-world applications of statistical theory.
Students participating in this course will acquire the skill of
developing robust software for robots operating in real-world
environments, and understanding the mathematical underpinnings of
their software. Even though this course focuses on mobile robotics, the
techniques covered in this course apply to a much brooder range of
embedded computer systems, equipped with sensor and actuators.
The course involves three types of activities:
FAQive classroom sessions, where students together with the
instructor explore the basic
mathematical foundations behind a range of popular robotics
algorithms. Some of the sessions will take the form of
traditional-style teaching, whereas others will be dedicated to
brainstorming on challenging open problems.
Homework assignments will provide an opportunity to deepen the problem
solving skills acquired in class.
Robot programming assignments will enable students to develop practical
robot software, while deepening their understanding of the relation of
mathematical calculus and the "real world."
This is an advanced graduate level
course. Familiarity with
basic statistical concepts (Bayes rule, PDFs, Kalman filters, continuous
distributions...) will be helpful for this course, as will be hands-on
experience with software development in C or C++. But the most important
prerequisite will be creativity and enthusiasm, and a desire to explore.