David Meger
Office: McConnell 112N
Office Hours: Tuesdays after class (10-11am)
Dave's office MC112N
X =
Teaching Assistant
Raihan Seraj
Office: McConnell 438
Office Hours: Wednesdays (10:30-11:30am)
Y =



COMP 765 is a research seminar on Intelligent Robotics. We will see how classical ideas starting from Gauss and Newton intersect with the latest machine learning on-board systems that can sense the world and act upon it. The class will begin with lectures on definitional problems and algorithms in robotics. We will then transition to mixed student-lead and instructor-lead discussions of recent developments in research and in practice. The emphasis is on algorithms, probabilistic reasoning, learning to improve behaviors using data, and decision making under uncertainty, as opposed to electromechanical systems design. We will broadly cover the following areas:



Lectures will be on Tuesdays and Thursdays in McConnell Engineering Room 103, 8:35-9:55am each week from Jan 7th until April 9th except March 2-6, McGill's study break.
Week Topics Slides References
1 Introduction
Kinematics and Dynamics
Lecture 1 - (pdf) (pptx)
Lecture 2 - (pdf) (pptx)
PR Chapter 1.
Craig Chapters 1-4.
2 Optimal Control Formulation
Lecture 3 - (pdf) (pptx)
Lecture 4 - (pdf) (pptx)
3 Optimal Control
Formulation and algorithms
Lecture 5 - (pdf) (pptx)
Lecture 6 - (pdf) (pptx)
4 Planning
Lecture 7 & 8 - (pdf) (pptx)
5 Perception and Estimation
Probabilistic estimation: particle and Kalman filters
Lecture 8 - (pdf) (pptx)
Lecture 9 - (pdf) (pptx)
EKF, graph SLAM, visual navigation
Lecture 10 - (pdf) (pptx)
7 Decisions Under Uncertainty
POMDP, black-box optimization for robotics, coverage and exploration
Lecture 11 - (pdf) (pptx)
Lecture 12 - (pdf) (pptx)
8 Model Learning
Model-based RL, Gaussian Processes for robotics, black-box optimization
Lecture 14 - (pdf) (pptx)
Probabilistic Inference and Learning for Control (PILCO)
9 Imitation Learning and Interaction
Inverse Optimal Control, active imitation, Dagger. Multi-agent systems of robots and humans.
10 Student project proposals
11 Research talks and papers
Presented by students and guests
12 Research talks and papers
Presented by students and guests
13 Project presentations
Final demos and show-and-tell

Marking scheme


These are all optional, but give great background on the subject. We will sometime assign readings from the material that is available freely on the web.

Related courses

Diversity and Inclusion

Robotics is one of the most imporant technologies in our world today and will be one of the most important skill-sets that people will use to influence the world in our lifetimes. This knowledge should be shared equally by all agents. Our goal is to make this content equally accessible to students of all backgrounds and we work to pro-actively acknowledge and address any bias that may occur during the term. Equal treatment of students from every gender, race and orientation is a top priority. We openly welcome suggestions on how to improve inclusion, by contacting the TAs or instructor either with your name or anonymously.


McGill University values academic integrity. Therefore all students must understand the meaning and consequences of cheating, plagiarism and other academic offenses under the Code of Student Conduct and Disciplinary Procedures (see (this link) for more information). In accord with McGill University's Charter of Students' Rights, students in this course have the right to submit in English or in French any written work that is to be graded. In the event of extraordinary circumstances beyond the University's control, the content and/or evaluation scheme in this course is subject to change.