Course Description
Title: IFT 6757 Autonomous Vehicles (aka. Duckietown)
Instructor: Liam Paull - Office AA3341 (but probably best contact me some other way)
Class TAs: Charlie Gauthier, Miguel Saavedra
Email: paulll@iro.umontreal.ca
Lab Space: AA3256
Website: duckietown.org, liampaull.ca/ift6757
Office Hours (in AA3256):
Liam: Monday’s 2-3pm
Charlie: Wednesday’s 3-4pm
Miguel: Wednesday’s 10-11am
Background and Description:
Self-driving vehicles are poised to become one of the most pervasive and impactful applications of autonomy, and have received a great deal of attention recently.
This course considers problems in perception, navigation, planning and control, and their systems-level integration in the context of self-driving vehicles through an open-source curriculum for autonomy education that emphasizes hands-on experience. Integral to the course, students will collaborate to implement concepts covered in lecture on a low-cost autonomous vehicle with the goal of navigating a model town complete with roads, signage, traffic lights, obstacles, and citizens.
Important Note 1: Every student will be given their own robot to personalize and love for the semester.
Important Note 2: This is a completely open source class. As a result, this class will never be the same twice since we always build on what already exists. Students who do a great job have the potential that their work will become the new repository standard for others to try and beat in subsequent iterations.
Outcomes:
The teaching objectives that we most care about are:
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Have the students learned the basic abstractions that comprise an autonomous vehicle? For example, if they pick up a new research paper they be able to put it in context.
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Do the students understand how methods from heterogeneous disciplines such as control theory, machine learning, computer vision, and artificial intelligence are integrated together to create a complex autonomous system?
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Have the students familiarized themselves with the standard tools that are used in the class, such as Git, ROS, Docker, and others?
Syllabus:
The course will cover the theory and application of probabilistic techniques for autonomous mobile robotics with particular emphasis on their application in the context of self-driving vehicles. Topics include probabilistic state estimation and decision making for mobile robots; stochastic representations of the environment; dynamics models and sensor models for mobile robots; algorithms for mapping and localization; planning and control in the presence of uncertainty; cooperative operation of multiple mobile robots; mobile sensor networks; deep learning for perception; imitation from expert trajectories; reinforcement learning.
Following is a list of topics discussed in the class:
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Autonomy architectures
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Sensors, models, and representations (projective geometry, kinematics/dynamics)
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Computer vision (intrinsic/extrinsic calibration, illumination invariance, feature extraction, line detection, place recognition)
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Nonlinear filtering and state estimation (Bayes filter, Kalman filter, particle filter, SLAM)
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Navigation and planning (mission planning, motion planning and control basics)
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Complex perception pipelines (use of object detection, reading traffic signs, and tracking)
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Tools for making robots work (Docker, ROS, Git, network basics)
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Reinforcement learning and sim2real transfer
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Deep learning for perception
Details
Meeting Times: M 10:30-12:30, W 11:30am–1:30pm
Location: M Z-337, W Z-260
Pre-requisites: Permission of the instructor. Please come to the first class and fill out an application and/or email the instructor to discuss.
Intended Enrollment: TBD
Intended Degree Level: Senior Undergraduates and Graduates
Tentative Grading Scheme:
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Exercises (best 5 of 6) - 25%
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Setup, perception, estimation, planning, control, end-to-end
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Simulator version submitted to challenge server (2/5)
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Hardware implementation and demonstration (2/5)
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Inspection of your code (1/5)
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Project - 45% (Will be in groups):
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Design Documents and Presentations - 10%
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Implementation and final demo - 25%
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Final Report - 10%
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30 min Seminar - 15%
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“Being a Good Citizen” - 15%
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Feedback on others’ design reports and seminars - 5%
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Responding to questions, filing issues, fixing documentation etc - 5%
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Allocated by by the TAs and Professor - 5%
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Late Policy: Exercises are due on Sunday. -10% for two days and then 0% after. You should demonstrate the functionality of your agent to one of the course staff in the office hours.