David Meger
Office: McConnell 112N
Office Hours: To Be Announced
X =

COMP 765 is a research seminar on Intelligent Robotics: the intersection of rocket science and 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:

Seminar Format

The class will be a mix between lecture-style teaching and student-lead discussion. In each unit, I will lead things off by presenting standard material given in textbooks, covering the theory and analysis with basic examples. We will then transition to the state-of-the-art in the area, with the end of the unit featuring presentations of important recent papers following the PROPONENT, OPPONENT, REPRODUCER format to be described in class.


NOTE: Following the seminar course style, the material is taylored to the interests of the class each year, and there are numerous opportunities for student-lead discussion. For now this is a rough initial schedule to allow course selection, to be refined after the Welcome Survey and discussion during the first few classes.

Date Topics Slides References
Jan 7 Introduction
Motivation and course sylabus
1 - Introduction
PR Chapter 1.
Jan 9 Spatial Representations
Kinematics and Dynamics
2 - Spatial Representation (pdf) (pptx) PR Chapter 5.
Jan 14 Estimation under uncertainty
Sensing uncertainty, Bayesian filters
3 - Estimation Intro PR Chapters 2.
Jan 16 Kalman Filters
Derivation and analysis, implementation concerns, EKF, sigma-points
4 - Particle and Kalman Filters PR Chapter 3
Jan 21 Particle Filters
Importance sampling, analysis, efficient resampling methods
4 - Particle and Kalman Filters PR Chapter 4
Jan 23 Modern Localization and Mapping
Rao-Blackwellized Particle Filtering, large-scale sparse graph optimizers, mapping an entire city.
5 - Mapping
PR Section 3
Jan 28 to Feb 13 Control and Planning
Control theory, ODE background, PID, MDPs, dynamic programming, LQR, trajectory optimization, stability, controllability, POMDPs. Discrete planning: search and DP, symbolic planning. Continuous planning: sampling-methods, geometric approaches.
PLAN text, selected chapters
Feb 18 to March 13 Learning for Robotics
Building blocks: Linear regression, Gaussian Processes, Bayesian Deep Nets. Learning to control: Model-based RL, End-to-end Deep RL. Active learning, black-box optimization applied to probabilistic search.
GPforML text, selected chapters
March 18 to April 3 Interacting with Humans
Imitation learning including LfD, IRL and AfP. HRI for home robots, self-driving and robots in the workplace. Safety assessment of intelligent robots. Collaborative and Cloud Robotics.
Dec 5 Project Demonstrations and Wrap-Up

Marking scheme

Recommended, but optional, textbooks

Related courses

Diversity and Inclusion

Robotics is one of the most technologies in our world today and this knowledge should be shared equally by all agents. , and indeed one of the most important skill-sets that people will use to influence the world in our lifetimes. 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.