Class announcements, discussions, assignments, and due dates will be posted on TBD.
This course will focus on the principles of decision-making in robotics. The motion planning course in Spring '17 focused on the problem of how to go from point A to point B. In this course, we will examine higher-level algorithms that make decisions as to what points A and B should be (for example) depending on the task at hand. Typical tasks that we will use as prototypical examples are where robots as mobile sensors. Specific research topics include:
The students will also work in groups on a research-oriented course project. More information about the project is here.
This is a qualifying course in artificial intelligence.
This is still tentative. Evaluation will be composed of the following:
No required textbook. Course materials will be drawn from research papers and notes that I will post online.
Each student will lead the discussion on TBD papers during the semester. You may choose a paper from the list given here.
The project can be any of the following types:
This is a seminar-style course that will feature a discussion of research papers. An ability to read and critique research papers is required.
There are no explicit prerequisites. However, students should be very comfortable in at least two of the following areas: Probability and Bayesian Statistics; Algorithms; Machine Learning/AI; Optimization.
Prior background in robotics is helpful, but not required.
You are encouraged to discuss the course materials with the instructor and other students in the class. However, any work that you submit (including but not limited to homeworks, paper reviews, project reports) must be your own. Give proper citations if you use any code or data from anyone else.
Any student who feels that he or she may need an accommodation because of a disability (learning disability, attention deficit disorder, psychological, physical, etc.), please make an appointment to see me during office hours.