CS226 Statistical Techniques in Robotics
CS 226 is a graduate-level course
that introduces students to the fascinating world of probabilistic robotics.
Probabilistic robotics is a hot research area in robotics. In the
1980, the dominant paradigm in robotics software research 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 students 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 probabilistic algorithms.
As in past years, we seek to leverage student projects to a conference-publishable level.
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.
This is a 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++. However, the most important
prerequisite will be creativity and enthusiasm, and a desire to explore.