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 a lecture on the core topic, followed by a paper discussion led 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
Week Monday Paper Discussion Guest Spotlight
Week 1: Feb 16 Introduction to Robot Learning Slides Recording No paper discussion. -
Week 2: Feb 23 Robot Control & MDPs Slides Recording Simple 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 Recording
Week 3: Mar 02 Imitation Learning Slides Recording Causal 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 Recording
Week 4: Mar 09 Reinforcement Learning I Slides Recording Evolution 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 Recording
Week 5: Mar 16 Reinforcement Learning II Slides Recording End-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 Recording
Week 6: Mar 23 Generative Models Slides Recording Planning 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 Recording
Week 7: Mar 30 Sequence Modeling and Transformers Slides Recording Decision 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 Recording
Week 8: Apr 13 World Models Slides Recording Learning 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 Recording
Week 9: Apr 27 Generalist Robot Policies Slides Recording Language 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 Recording
Week 10: May 04 Embodied Reasoning and Test-time Scaling Slides In-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 Recording
Week 11: May 11 Frontier & Open Problem A Path Towards Autonomous Machine Intelligence (LeCun, 2022), The Bitter Lesson (Sutton, 2019), Intelligence without Representation (Brooks, 1991) Lucas Beyer (Meta) (confirmed)
Week 12: May 18 Guest Lectures Dieter Fox (Prof. University of Washington & Director AI2) (confirmed) & Pieter Abbeel (Prof. UC Berkeley & VP Amazon) (tentative) -
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.

Week Topic Material Due Date
Week 1: Feb 19 Pytorch & Numpy Tutorial Code March 5
Week 2: Feb 26 Robot Control & MDPs Code March 12
Week 3: Mar 02 Imitation Learning Code March 26
Week 5: Mar 29 Reinforcement Learning Code April 16