New time: The final exam will be posted here at 10am on 5/8/2012. It will be due in the TA's departmental mail box or via email to the TA at noon on 5/9/2012. NO LATE EXAMS WILL BE ACCEPTED!

As I have mentioned several times in class, I will drop the lowest grade of the five homework assignments assigned. Therefore, only the best four assignments will count toward your final grade.

Course Description

Many of the big challenges in robotics research today are algorithmic. Given today's robot hardware, how can robots be programmed to do useful tasks and chores? This course will introduce the student to a few key problems and algorithms in robotics. The student will apply the algorithms to simulated robotics problems in Matlab. We will cover: 1) topics in optimal control including: the Bellman equation, value iteration, reinforcement learning, linear quadratic regulation; 2) topics in planning including: trajectory optimization, randomized planners; 3) topics in estimation including: Kalman filtering/smoothing, particle filters, mapping, object localization; 4) topics in optimization including: non-linear optimization, convex optimization, polynomial optimization. There will be approximately five homework or Matlab assignments and a final exam. Prerequisites: linear algebra, familiarity with Matlab.

Course Information:

Timeslot: MWF 10:00 -- 10:50
Room: Clemens 322
Instructor: Robert Platt
ubit: robplatt
Office: 330 Davis Hall
Office Hours: MW 11--12
Textbook: None. Readings assigned on syllabus below.

TA: Zihe Chen
TA email: zihechen@buffalo.edu

Grading:

• One exam: 25%
• 5 Homeworks/Labs: 75%. Out of the five homework assignments, I will drop the lowest grade.
• In order to get credit for cse510, you must complete a small additional projct. Project topic should be pre-approved by me.

Course Policies:

• Homeworks and labs are to be completed by individual students unless otherwise specified. You may study with other students or ask other students to explain general concepts. However, the solution that is turned in must be completely your own.
• Late homeworks will be docked 5% per day late. Please put late homeworks in my mailbox in the main office.
• All assignments are to be submitted in class.

Course Goals:

• Gain an understanding of the basic problems in robotics.
• Understand a few key robotics algorithms in detail.
• Learn mathematical/algorithmic tools that can be applied elsewhere. Improve your analysis skills.

Topics to be covered:

• Markov Decision Processes
• The Bellman equation
• Value iteration
• Reinforcement learning
• Linear quadratic regulation
• Model predictive control

• Non-linear model predictive control
• Iterative LQR, Shooting, collocation methods
• Optimization algorithms
• Configuration space, representation of rotation
• Dynamic Programming methods in planning
• Randomized planning methods

• Extended Kalman filtering, Kalman smoothing
• Particle filtering
• Linear Quadratic Gaussian control
• Sensor models, scan matching
• Mapping

• Standard convex optimization problems, approximate complexity
• Semi-definite relaxations in control
• Polynomial optimization via sums of squares
• Lypanunov functions and stability certificates

Syllabus

Homeworks are due on the date where they appear in the syllabus.

Final project here (needed for graduate credit only).

Somewhat related courses elsewhere:

Standford EECS, Advanced Robotics
RPI CS, Robotics II