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:
Statistical techniques in roboticd is a new course which has not been offered at Carnegie Mellon University before. It has previously been taught at Stanford University, where it led to a number of publications at top conferences in the field. Detailed information on the Stanford course can be found here.
Prerequisites: This is a graduate level course tha tis pretty much self-contained. 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.