We have structured our curriculum in terms of “modules” where each module is supported by several types of materials to reinforce it. All modules have slides.

In the “Extra Materials” column below:
are links to notes in the duckiebook
are links to exercises in the duckiebook
are links to Jupyter notebooks in the duckiebook
are links to demos to be run on the Duckiebot
are links to additional videos that have been created

Topic	Lecture Slides	Lecture Recordings	Extra Materials
Introductory Materials
Introduction – Duckietown			Autonomous Vehicles From TTIC 2017 (Matt Walter)
Introduction – Modern Robotics Systems			Modern Robotic Systems From TTIC 2017 (Matt Walter)
Introduction – Architectures			System architecture basics Autonomy architectures From MIT 2016 (Misha Novitzky)
Introduction – Representations			Representations
Tools – Networking
Tools – Version Control			Git and Github
Tools – Middlewares			ROS installation and reference Taking and verifying a log From MIT 2016 (Shih-Yuan Liu)
Modeling, Kinematics, and Dynamics
Modeling			Duckiebot modeling From TTIC 2017 (Matt Walter)
Calibration – Odometry			Wheel calibration
Signal Processing			Modern signal processing
Basics			Basic image operations Instagram filters OpenCV basics From TTIC 2017 (Matt Walter)
Camera Models			Augmented Reality From TTIC 2017 (Matt Walter)
Feature Extraction			SIFT
Image Filtering			Image FilteringFrom TTIC 2017 (Matt Walter)
Line Detection			From TTIC 2017 (Matt Walter)
Place Recognition			From TTIC 2017 (Matt Walter)
RANSAC
Camera Calibration			Camera calibration and validation
Estimation
Filtering			Probability basics Lane Filtering – Particle Filter Lane Following From TTIC 2017 (Matt Walter) From MIT 2016 (John Leonard) From MIT 2016 (Liam Paull)
Kalman Filter			Lane Filtering – Extended Kalman Filtering
SLAM			From TTIC 2017 (Matt Walter)
Planning and Control
Control			Make Way for Duckiebots Lane Following
Motion Planning			Indefinite Navigation
Testing, Validation, Verification			Who watches the watchmen?
Multi-Vehicle
Multi-Vehicle Coordination
Fleet-level Planning			Fleet planning
Introduction to Autonomous Mobility on Demand
Machine Learning
Machine Learning and Deep Learning – Introduction			How to install PyTorch on the Duckiebot How to install Caffe and Tensorflow on the Duckiebot How to use the Neural Compute Stick
Models vs. Data
End-to-End Imitation Learning			Lane control with supervised learning
Reinforcement Learning for Autonomous Vehicles
Human-Machine Interaction and Safety
Shared Control
Introduction to Safety
Advanced Safety and Formal Methods
Advanced Perception
Object Detection
Estimation from Motion Blur