Course Title: Robot Learning: From Fundamentals to Foundation Models

Semester: Spring 2026

Lecturer : Oier Mees

Teaching assistants : Alexey Gavryushin, Jonas Pai, Liam Achenbach

Catalogue Link: 263-5911-00L

Lecture: Mon 16:15-18:00, room ML F 36

Thursday Practice: Thu 14-16, room CHN D 29, D 42 and D 46

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
Week 1: Feb 16 Introduction to Robot Learning No paper discussion.
Week 2: Feb 23 MDPs & Learning Dynamics Simple random search provides a competitive approach to RL (Mania et al., 2018), Deep RL Doesn’t Work Yet (Irpan, 2018)
Week 3: Mar 02 Imitation Learning Causal Confusion in IL (Den Haan et al, 2019), The surprising effectiveness of representation learning for visual imitation (Pari et al. 2021)
Week 4: Mar 09 Reinforcement Learning I Evolution Strategies as a Scalable Alternative to RL (Salimans et al., 2017), Learning Synergies between Pushing and Grasping (Zeng et al., 2018)
Week 5: Mar 16 Reinforcement Learning II End-to-End Training of Deep Visuomotor Policies (Levine et al, 2015), Eureka: Human-Level Reward Design via Coding LLMs (Ma et al 2023)
Week 6: Mar 23 Generative Models Planning with Diffusion for Flexible Behavior Synthesis (Janner & Du et al., 2022), Steering Your Diffusion Policy with Latent Space RL (Wagenmaker et al 2025)
Week 7: Mar 30 Sequence Modeling and Transformers Decision Transformer: RL via Sequence Modeling (Chen et al., 2021), Learning Fine-Grained Bimanual Manipulation (ALOHA) (Zhao et al., 2023)
Week 8: Apr 13 World Models Learning Universal Policies via Text-Guided Video Generation (Du et al, 2023), Training Agents Inside of Scalable World Models (Hafner et al 2025)
Week 9: Apr 27 Generalist Robot Policies A Generalist Agent (Gato) (Reed et al., 2022), π∗0.6: a VLA That Learns From Experience (Physical Intelligence, 2025)
Week 10: May 04 Embodied Reasoning and Test-time Scaling Visual Language Maps for Robot Navigation (Huang et al., 2023), VOYAGER: An Open-Ended Embodied Agent with LLMs (Wang et al., 2023)
Week 11: May 11 Frontier & Open Problem A Path Towards Autonomous Machine Intelligence (LeCun, 2022), The Bitter Lesson (Sutton, 2019), Intelligence without Representation (Brookes, 1991)
Week 12: May 18 Guest Lecture I TBD / Readings relevant to Guest Topic
Week 13: May 28 Guest Lecture II TBD / Readings relevant to Guest Topic
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
Week 1: Feb 19 Pytorch & Numpy Tutorial Code