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Seminar: Learning with Limited Supervision

Deep learning has become a key enabler of real world autonomous systems. While classical supervised learning methods typically rely on ground truth information, the area autonomous robotics requires less dependence on manual supervision. The research directions of semi- and self-supervised learning instead aim to learn representations without explicit and potentially even manual supervision. Especially, the domain of robot learning requires scaling to large amounts of unlabeled data in a lifelong manner. Self- and semi-supervised learning already have had a significant impact on fields such as perception, state estimation, control, or graph representation learning, thereby making important progress in object manipulation, scene understanding, visual recognition, object tracking, and learning-based control, amongst others. In this seminar, we will study a selection of state-of-the-art works that propose deep learning techniques for tackling various challenges in autonomous systems. In particular, we will analyze contributions in architecture design and learning paradigms in the areas of computer vision, reinforcement learning, imitation learning, and deep graph learning.

selfsup-seminar

Course Information

Details:
Course Number: 11LE13S-7310-M
Places: 20
Location: Georges-Köhler-Allee 80, Room Number 00.021
Course Program:
Introduction: 23/04/2026 @ 14:00
How to make a presentation: 25/06/2026 @ 16:00 -->
Block Seminar: 24/07/2026 @ 09:00 - 17:00
Evaluation Program:
  Seminar Presentation: 24/07/2026 @ 9:00 - 17:00
  Summary Due Date: 02/08/2026 @ 23:59 CEST
Requirements:
  Basic knowledge of Deep Learning or Reinforcement Learning
Remarks:
Topics will be assigned for the seminar via preference voting. If there are more interested students than places, places will be assigned based on priority suggestions of the HisInOne system and motivation (tested by asking for a short summary of the preferred paper). The date of registration is irrelevant. In particular, we want to avoid that students grab a topic and then leave the seminar. Please have a coarse look at all available papers to make an informed decision before you commit. 

Course Material

Google Form:
Literature Review Guide:
Slides:
Lecture 1: Introduction
Lecture 2: TBD
Templates:

Additional Information

Enrollment Procedure

  • Enroll through HISinOne, the course number is 11LE13S-7310-M.
  • The registration period for the seminars are from 20/04/2026 to 27/04/2026.
  • Attend the introductory session on 23/04/2026.
  • Select three topics from the topic list and complete this form by 27/04/2026.
  • Places will be assigned based on the priority suggestions of HISInOne and the student's motivation on 29/04/2026.

Evaluation Details

  • Students are expected to prepare a 30-minute long presentation (as groups of three) and draft a summary (individual).
  • The seminar will be held as a "Blockseminar" on 24/07/2026, 9:00 - 17:00 .
  • The slides of your presentation should be discussed with the supervisor two weeks before the Blockseminar.
  • The summary should not exceed seven pages (excluding bibliography and images) and is due on 02/08/2026. Significantly longer summaries will not be accepted.
  • Ensure you cite all work you use including images and illustrations. Where possible, try to use your own illustrations.
  • The final grade is based on the literature review, oral presentation, the summary, and participation in the blockseminar.

What should the Summary contain?

The summary should address the following questions:

  • What are the connections and distinctions of the papers in your topic area?
  • What is your paper's main contribution and why is it important?
  • How does it relate to other techniques in the literature?
  • What are the strengths and weaknesses of the paper?
  • What would be some interesting follow-up work? Can you suggest any possible improvements in the proposed methods? Are there any further interesting applications that the authors might have overlooked?

Graded Component Submission

  • Save your document as a PDF and directly submit it to your topic supervisor via email.
  • The filename should be in the format "FirstName_LastName_X.pdf" where X is the evaluation component (Summary / Presentation).

Topics

  1. Text Adaption of Self-Supervised Vision Foundation Models Supervisor: Yao Lu
  2. Auto-Vocabulary Semantic Segmentation: Open Vocabulary Without Text Prompts Supervisor: Yao Lu
  3. Feed-forward 3D Geometry Foundation Models Supervisor: Dr. Simon Bultmann
  4. Label-Efficient Semantic 3D Occupancy Prediction Supervisor: Dr. Simon Bultmann
  5. Learning through World Models Supervisor: Dr. Iman Nematollahi
  6. Learning via Latent Actions Supervisor: Dr. Iman Nematollahi
  7. Training-Free Segmentation methods on ViT-Features Supervisor: Adrian Röfer
  8. Efficient Robotic Imitation Learning Supervisor: Adrian Röfer

Questions?

If you have any questions, please direct them to Simon Bultmann before the topic allotment, and to your supervisor after you have received your topic.