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News

• Jan 30, 2019: Seminar paper sign-up is today! Also Assignment 1 has just been released and check the updated schedule for precise dates of presentations to the end of class.
• Jan 23, 2019: Please sign-up for Piazza which we're going to use to discuss assignments and papers from here on: piazza.com/mcgill.ca/spring2019/comp765
• Jan 21, 2019: Slide links up to lecture 6 have just been refreshed (including the pptx versions). So please download fresh if you'd like the latest!
• Jan 21, 2019: The reading list has been posted just below. Sign-up happening soon!
• Jan 9, 2019: Please fill out the Welcome Survey which will be used to tailor the seminar to interests of the group this year.
• Jan 7, 2019: Welcome to the course!

Overview

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:

• Sensing and Estimation: robots (like humans) sense their surroundings through limited, imprecise measurements. What algorithms are needed to give robots the ability to reason accurately about the world?
• Control and Planning: how can we compute the state-(in)dependent commands that can bring a robotic system from its current state to a desired state, when models of the robot and its environment are available?
• Decision Making Under Uncertainty: how can the same problems be solved when our model knowledge is missing or imprecise? In particular, what motion strategies best complement our probabilistic estimators, for example by gathering useful information or acting conservatively?
• Learning for Robotics: how can we use data to learn models for a robot's continuous time series data in case these are not known in advance?
• Heirarchical Decision Making: how can tasks with longer time-scale or for larger target systems be factored productively into multiple levels of reasoning?
• Human-robot Interaction: how can we make programming robots easy? Can robots learn from the way humans perform and describe tasks? Can we achieve super-human performance? Can robots teach humans how to best perform or reason about tasks?

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.

The [READING LIST], along with the schedule and assigned presenters is located here. This link is edittable. Please feel free to offer your peers trades, the spreadsheet can be edited using this link.

Assignments

• Assignment 1: Robot Localization (on github)
• Assignment 2: Learning and Controlling Dynamic Systems (on github)

Schedule

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.