Self-Driving Cars with Duckietown

Module 0: Welcome to the course

• Welcome to the course, by Prof. Emilio Frazzoli
• You will familiarize yourself with the logistics and navigation interface of the course resources
• You will start a learning journey in the world of robot autonomy with Duckietown

Module 1: Introduction to self-driving cars

• The potentials and challenges
• Levels of autonomy
• The vision for autonomous vehicles (AVs)
• Activities: You will set up your learning environment, and your Duckiebot, and make your first challenge submission

Module 2: Towards autonomy

• Making a robot
• Sensorimotor architectures
• Stateful architectures
• Logical and physical architectures
• Application: You will create a reactive "Braitenberg" agent to avoid duckies and see how your agent compares to other submissions

Module 3: Modeling and Control

• Introduction to control systems
• Representations and models
• PID control
• Application: You will design an odometry function and PID controller to command your Duckiebot's angular velocity

Module 4: Robot Vision

• Introduction to projective geometry
• Camera modeling and calibration
• Image processing
• Application: You will develop image processing techniques necessary for visual lane servoing - controlling your Duckiebot to drive within markings

Module 5: Object Detection

• Introduction to neural networks
• Convolutional neural networks
• One and two-stage object detection
• Application: You will train a convolutional neural network (CNN) to detect duckies and integrate your model with ROS to run onboard your Duckiebot and avoid duckies

Module 6: State Estimation and Localization

• Bayes filtering framework
• Parameterized methods (Kalman filter)
• Sampling-based methods (particle and histogram filter)
• Application: You will build a state estimation algorithm combining the dynamics and sensor data of your Duckiebot in order to predict its pose as it travels through the world

Module 7: Planning I

• Formalization of the planning problem
• Application: You will create a collision checker to determine if your Duckiebot is crashing into an obstacle

Module 8: Planning II

• Graphs
• Graph search algorithms
• Application: You will tackle a variety of path-planning challenges and leverage all the skills you've built thus far to navigate your Duckiebot in a variety of simulated environments

Module 9: Learning by Reinforcement

• Markov decision processes
• Value functions
• Policy gradients
• Domain randomization
• Application: You will explore the capabilities and limitations of reinforcement learning models when applied to real-world robotics tasks such as lane following