ETH Zurich just open-sourced their entire 2026 robot learning course.

Not a MOOC. The actual course. Slides, lecture recordings, coding assignments, GitHub repo.

The curriculum goes from imitation learning and RL all the way to Vision-Language-Action models and foundation models for robotics.

Guest lectures from the co-founder of Physical Intelligence. The creator of Diffusion Policy. Pieter Abbeel. Dieter Fox.

12 weeks. Free. No signup.

If you want to understand where robot intelligence is actually heading… this is the reading list the field is using right now.

[http://cvg.ethz.ch/lectures/Robot-Learning…]

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Computer Vision and Geometry Group

Source: https://cvg.ethz.ch/lectures/Robot-Learning/ **Course Title:**Robot Learning: From Fundamentals to Foundation Models

**Semester:**Spring 2026

Lecturer:Oier Mees

Teaching assistants:Alexey Gavryushin,Jonas Pai,Liam Achenbach,Nicola Irmiger,Tianxu An,Šimon Sukup,Nicole Damblon,Zador Pataki,Carl Brandner,Aristotelis Sympetheros,Rohan Walia,Rajiv Bharadwaj,David Hohenstatt,Huanyu Guo

Catalogue Link:263-5911-00L

**Lecture:**Mon 16:15-18:00, room NO C 60.

• Format:Each session begins with alectureon the core topic, followed by apaper discussionled by students. On selected weeks, we will also host short guest lectures from experts in the field to conclude the session.

**Thursday Practice:**Thu 10:15-12:00, room CHN D 29, D 42, D 46, D 48 and IFW A 32.1.

Course GitHub:mees-robot-learning-course/ethz-course-2026

Course Objectives:

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 The objectives of this course are:

• Understand the core principles of imitation learning, reinforcement learning, and policy learning.
• Implement basic robot learning systems in simulation and on real robots.
• Explore state-of-the-art Vision-Language Action and foundation models for robotics.
• Design and evaluate scalable robot learning pipelines integrating perception, control, and multi-modal reasoning.

Examination:

• Paper Presentation & Moderation (Group): 20 %
• Practical Homework (Coding Assignments): 40 %
• Final Project (Group): 40 %

Lecture Tentative Schedule

WeekMondayPaper DiscussionGuest SpotlightWeek 1: Feb 16Introduction to Robot LearningSlidesRecordingNo paper discussion.-Week 2: Feb 23Robot Control & MDPsSlidesRecordingSimple random search provides a competitive approach to RL(Mania et al., 2018),Deep RL Doesn’t Work Yet(Irpan, 2018),Curiosity-driven Exploration by Self-supervised Prediction(Pathak et al., 2017)Abishek Gupta(Prof. University of Washington)YouTube RecordingWeek 3: Mar 02Imitation LearningSlidesRecordingCausal Confusion in IL(Den Haan et al., 2019),The surprising effectiveness of representation learning for visual imitation(Pari et al. 2021),Transporter Networks: Rearranging the Visual World for Robotic Manipulation(Zeng et al., 2020)Danfei Xu(Prof. Georgia Tech)YouTube RecordingWeek 4: Mar 09Reinforcement Learning ISlidesRecordingEvolution Strategies as a Scalable Alternative to RL(Salimans et al., 2017),Learning Synergies between Pushing and Grasping(Zeng et al., 2018),Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning(Luo et al., 2024)Aviral Kumar(Prof. Carnegie Mellon University & Google DeepMind)YouTube RecordingWeek 5: Mar 16Reinforcement Learning IISlidesRecordingEnd-to-End Training of Deep Visuomotor Policies(Levine et al., 2015),Eureka: Human-Level Reward Design via Coding LLMs(Ma et al., 2023),Latent Plans for Task Agnostic Offline Reinforcement Learning(Rosete-Beas et al., 2022)Andrew Wagenmaker(Postdoc UC Berkeley)YouTube RecordingWeek 6: Mar 23Generative ModelsSlidesRecordingPlanning with Diffusion for Flexible Behavior Synthesis(Janner & Du et al., 2022),Implicit Behavioral Cloning(Florence et al., 2021),Steering Your Diffusion Policy with Latent Space RL(Wagenmaker et al., 2025)Cheng Chi(Co-Founder Sunday Robotics, Lead of Diffusion Policy & UMI)YouTube RecordingWeek 7: Mar 30Sequence Modeling and TransformersSlidesRecordingDecision Transformer: RL via Sequence Modeling(Chen et al., 2021),Learning Fine-Grained Bimanual Manipulation (ALOHA)(Zhao et al., 2023),Humanoid Locomotion as Next Token Prediction(Radosavovic et al., 2024)Ted Xiao(Co-Founder Prometheus, ex-Google)YouTube RecordingWeek 8: Apr 13World ModelsSlidesRecordingLearning Universal Policies via Text-Guided Video Generation(Du et al, 2023),Training Agents Inside of Scalable World Models(Hafner et al., 2025),World Action Models are Zero-shot Policies(Ye et al., 2026)Scott Reed(Principal Research Scientist NVIDIA GEAR Lab)YouTube RecordingWeek 9: Apr 27Generalist Robot PoliciesSlidesRecordingLanguage Conditioned Imitation Learning over Unstructured Data(Lynch et al., 2021),A Generalist Agent (Gato)(Reed et al., 2022),π∗0.6: a VLA That Learns From Experience(Physical Intelligence, 2025)Quan Vuong(Co-Founder Physical Intelligence)YouTube RecordingWeek 10: May 04Embodied Reasoning and Test-time ScalingSlidesRecordingIn-Context Imitation Learning via Next-Token Prediction(Fu et al., 2024),VOYAGER: An Open-Ended Embodied Agent with LLMs(Wang et al., 2023),Training Strategies for Efficient Embodied Reasoning(Chen et al., 2025)Archit Sharma(Research Scientist Google DeepMind, Co-creator Gemini Deep Think series)YouTube RecordingWeek 11: May 11Frontier & Open ProblemSlidesRecordingA Path Towards Autonomous Machine Intelligence(LeCun, 2022),The Bitter Lesson(Sutton, 2019),Intelligence without Representation(Brooks, 1991)Lucas Beyer(Meta Superintelligence Labs)YouTube RecordingWeek 12: May 18Guest LecturesDieter Fox(Prof. University of Washington & Director AI2) (confirmed)-##### Tutorials

Each session is organized as follows. TAs first give a summary of the relevant course content and introduce the exercises. TAs then remain in the room to assist students in solving the exercises.

WeekTopicMaterialDue DateWeek 1: Feb 19Pytorch & Numpy TutorialCodeMarch 5Week 2: Feb 26Robot Control & MDPsCodeMarch 12Week 3: Mar 02Imitation LearningCodeMarch 26Week 5: Mar 29Reinforcement LearningCodeApril 16