COMP 150PR - Syllabus

Description and Objective:
This course is a continuation of COMP 131 (Artificial Intelligence) with an emphasis on probabilistic methods that can be applied to robotics. The first part of the course will introduce the Kalman filter and related Bayesian filters for state estimation. These techniques will then be applied to topics such as localization, mapping, planning, and control.

Prerequisites: Comp 131 or permission of instructor. Some knowledge of linear algebra will be helpful.

The textbook for the course is Probabilistic Robotics, by Sebastian Thrun, Wolfram Burgard, and Dieter Fox, MIT Press (2006) ISBN: 978-0-262-20162-9

Anselm Blumer

ablumer (at) cs dottufts dot edu
Halligan Hall, Room 211
Office Hours:
by appointment.
If requesting an appointment, please send an email suggesting some possible times for appointments.
Home page

Teaching assistant:
Tom Williams
williams (at) cs dottufts dot edu
Office: 200 Boston Avenue, Room 2510
Tom is available on an as-needed basis for help with the robot.


The robot, TwoPi, is built on a Create platform from iRobot. It has a Raspberry Pi processor and a laser rangefinder.

Students are encouraged to communicate frequently with the instructor regarding any issues with the course. Students are encouraged to use email and office hours frequently. Any announcements regarding the course will be made via the course webpage or in class so be sure to check it frequently and be sure to get material for any class you miss.

Homework will be assigned regularly in the course. While reading assignments will not be directly assigned it is important that students use the textbook to supplement their understanding of the material presented in the lecture. Many of the assignments will be written assignments due on Wednesdays at the beginning of class on the due date specified. This work can be handwritten with the assumption that these assignments are legible. (A student may be asked to type their assignments if grading is not possible.)

There will also be a significant number of programming assignments, probably interleaved with the written assignments. These will usually involve programming TwoPi in Java. These will be submitted via "provide" and will usually be due at 11:30 PM on the due date.

Late Homeowork:
Because of the size of the class and the amount of homework 20% of the total number of points for the assignment will be deducted per weekday for written assignments and per calendar day for programming assignments. No homework will be accepted after one week.

There will be no exams.

Your thoughts and concerns on this course are important. You are encouraged to give feedback to the instructor and teaching fellow throughout the term. As always students will be asked to fill out a course evaluation at the end of the term.

Academic Misconduct:
Students should read the Tufts brochure on academic integrity located at:

A few highlights are presented to emphasize importance:

Absolute adherence to the code of conduct is demanded of the instructor, teaching fellow, and students. This means that no matter the circumstance any misconduct will be reported to Tufts University.

While students are encouraged to discuss course materials, no collaboration is allowed on homework. Specifically you may discuss assignments and projects verbally, but must write up or work on the computer alone. In addition any discussion should be documented. An example on the homework would be "Thanks to Ray for helping me understand Kolmogorov complexity." Another important example is citing a source, this could be "This information was adapted from"

While computers enable easy copying and collaboration both with other students and materials from the Internet, it is possible to use these same computers to detect plagiarism and collaboration.

If any student does not understand these terms or any outlined in The Academic Code of Conduct it is his/her responsibility to talk to the instructor.