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Robot Learning: From Fundamentals to Foundation Models
Last Updated: 2026-06-03 00:14:13
Abstract
This course provides a comprehensive introduction to modern robot learning, combining classical techniques with the latest advances in large-scale models: Students will start by learning the fundamentals of imitation learning, reinforcement learning, and policy optimization, and gradually progress to advanced topics including Vision-Language-Action (VLA) models and foundation models for robotics
Objective
After attending this course, students will: 1. Understand the core principles of imitation learning, reinforcement learning, and policy learning. 2. Implement basic robot learning systems in simulation and on real robots. 3. Explore state-of-the-art Vision-Language Action and foundation models for robotics. 4. Design and evaluate scalable robot learning pipelines integrating perception, control, and multi-modal reasoning.
Content
The course covers classical robot learning methods, policy optimization, and imitation learning. Students will apply these concepts through hands-on assignments and projects. In the latter part, the course introduces VLAs and foundation models, showing how large-scale, multi-modal models can be used for perception, decision-making, and action in robotic systems.
Resources
Learning Materials (Links)
- Main link
- Lecture Material
General Information
- Language
- English
- Levels
- MSC
Examination
- Type
- graded semester performance
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Robot Learning: From Fundamentals to Foundation Models |
|
3 h weekly |
| independent project | Robot Learning: From Fundamentals to Foundation Models | No time listed | 2 h weekly |
Offered In
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Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed. All courses listed as core courses (not electives) for one of the following ETH MSc programmes, MSc Statistics, MSc Physics, MSc Computer Science, MSc (Applied) Mathematics, MSc Neural Systems and Computation, MSc Robotics, Systems, and Control, MSc Data Science, MSc Electrical Engineering and Information Technology, can be taken as an elective course in the MSc CSE without prior permission.)
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