Robotics Course Syllabus

Books

THRUN - Probabilistic Robotics by Sebastian Thrun, Wolfram Burgard, and Dieter Fox, 2005. Required textbook - the foundational text for modern robotics.
LYNCH - Modern Robotics: Mechanics, Planning and Control. Free to download. Oriented towards manipulation with foundational motion algebra. See also the Python package that implements the book’s algorithms.
CORKE - Robotics, Vision and Control: Fundamental Algorithms in PYTHON by Peter Corke, 3rd edition, 2023. Hands-on complement to THRUN and LYNCH. See also the robotics-toolbox-python repository.

Some of the assigned reading sits on the O’Reilly platform, which your university provides at no cost. See O’Reilly access if you have not signed in yet.

Learning Outcomes

After completing this course, students will be able to:

Design the various subsystems involved in robotic agents with egomotion
Implement perception using sensor fusion (computer vision with LiDAR and other sensors)
Implement planning algorithms for path planning and motion/trajectory planning
Train robotic control policies in simulation and transfer them to reality
Instruct robots using natural language
Program robotic systems using the ROS2 framework

Planned Schedule

This course emphasizes mobile robots (not manipulation) and covers:

Part I: Robotic Perception (Weeks 1-5)
Part II: Motion and Kinematics (Week 6)
Part III: Mapping and Localization (Week 8)
Part IV: Planning and Sequential Decisions (Weeks 9-10)
Part V: Reinforcement Learning, Instruction Following and Transfer (Weeks 11-14)

Week numbers match the weekly study guide, which carries the readings, videos and deliverables for each week.

Week	Topic	Description
1	Introduction	AI and robotics systems perspective with autonomous vehicles focus. Prerequisites review.
2	Statistical Learning Theory	The learning problem, linear regression, gradient descent, maximum likelihood and classification.
3	Dense and Convolutional Neural Networks	Multi-modal sensing, maximum likelihood optimization, fully connected and convolutional architectures.
4	Object Detection	MaskRCNN, YOLO, transfer learning with pretrained feature extractors.
5	State Estimation	Recursive estimation, Dynamic Bayesian Networks, Kalman filters. Reading: THRUN Chapters 2-3
6	Motion and Wheeled Robots	Configuration space, rotation and motion representations, Lie groups, and the kinematics and odometry of wheeled robots. Reading: Lynch and Park Chapters 2, 3, 13
7	Midterm Exam	No lecture. A take-home, programming-oriented midterm covering Week 6.
8	SLAM	Simultaneous Localization and Mapping, including localization and egomotion, and Visual SLAM with monocular cameras. Reading: THRUN Chapters 5, 7, 9-10; CORKE Chapters 4, 6
9	Global Planning	Optimal planning under uncertainty, A, D, RRT*, PRM algorithms.
10	MDPs and POMDPs	Sequential decisions, reward signals, Bellman equations.
11	Deep Reinforcement Learning	Model-free methods for robotic control policies.
12	Instruction Following	Vision Language Models for human-robot collaboration.
13	VLA Models	End-to-end Vision-Language-Action models for instruction parsing, perception, and action.
14	Sim-to-Real Transfer	Closing the gap between simulated training and physical deployment.

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Course

Assignments

Study Guides

Robotics Course Syllabus

Books

Learning Outcomes

Planned Schedule

​Books

​Learning Outcomes

​Planned Schedule

Books

Learning Outcomes

Planned Schedule