Robot Learning: Integrated Project 1+2

The IP Project will continue during the Summer Semester 2025. The Introduction presentation will be held both in-person and via Zoom on 24.04.2025 at 15:20 (see details below in the Meetings section). In case you require more information, please contact ip-projekt@ias.informatik.tu-darmstadt.de asap.

Outstanding students who have completed 20-00-0753-pj Robot Learning: Integrated Project - Part 1 and 20-00-0754-pj Robot Learning: Integrated Project - Part 2 will be considered for the 20-00-1108-pp Expert Lab on Robot Learning.

In the Robot Learning: Integrated Project, we offer the possibility for highly motivated students to increase their knowledge in robotics and machine learning. This project will allow participants to understand these fields in greater depth and provide hands-on experience. For this purpose, new methods from the Robot Learning lecture are to be implemented. These methods are then proven useful for solving learning problems regarding robotics applications. Participants join together in groups of 2-3 people and get supervision from one or more members of the Intelligent Autonomous Systems Lab. Students may focus on more theoretical or practical work depending on their preference and on the available projects. We are always delighted to answer questions, so please contact us, for example by writing an email to ip-projekt@ias.informatik.tu-darmstadt.de!

Part 1: Literature Review, Toy Evaluations and Simulation Studies. In the Robot Learning: Integrated Project - Part 1, a current problem in robot learning is tackled by students with the aid of their supervisor. The students conduct a literature review that corresponds to their research interests. Based on this preliminary work, a project plan is developed, the necessary algorithms are tested and a prototypical realization in simulation is created.

Part 2: Evaluation and Submission to a Conference In the Robot Learning: Integrated Project - Part 2 the solutions from Part 1 are completed and applied to a real robot. A scientific article is written on the issue, methods and results, and submitted to a high quality scientific conference or journal. For Part 2, there are numerous exciting robot systems available to students.

Requirements and Background Knowledge. Mathematics from your undergraduate studies (calculus, statistics), programming (project dependent but usually C/C++, Python), and computer science fundamentals (algorithms). Simultaneous or previous attendance of the Statistical Machine Learning and Robot Learning lectures is recommended!

Meetings & Deadlines

Project Application

Please apply for a project from the list of offered projects by writing a short motivation letter. In the letter, please address the following questions:

1. Which project would you like to try and why?
2. Why do you think this project is important?
3. What helpful background do you have for the project and what makes you special for that project?
4. Your academic aspirations: 1 semester? 2 semesters? Future thesis?

See the Introduction and Topic slides here for the rules and the list of projects! Participants can only apply for two projects. Please specify your priority for the two projects. If you already have a group, please send a joint email.

To apply for a project, please send your project wishes by email to the project supervisor and cc (ip-projekt@ias.informatik.tu-darmstadt.de) by 30.04.2025. After an internal discussion with the potential supervisors, it will be decided which topics are assigned to which students. Unfortunately, it is possible that some students do not get a topic! Please get in contact with the supervisor beforehand to know more about the project.

Report, Review and Presentation

At the end of the IP project (either part 1 or 2), your group is expected to produce three outputs:

1. Submit a final report in the ICML style or IROS style as a PDF file. Papers are limited to eight pages, including figures and tables; and additional pages containing only cited references and an appendix (no page limit) are allowed.
2. Send your report to ip-projekt@ias.informatik.tu-darmstadt.de, and your supervisors.
3. Review and evaluate a report of another group. This process emulates the standard procedure at scientific conferences.
4. Give a final presentation on the work you did - 10 minutes + 5 minutes questions.

Your project will be marked based on the following Grading Criteria:

• Creativity and effort on the project
• Technical quality and robustness of the solution
• Has the student grasped and presented the big picture?
• Quality of the report
• Quality of the presentation

To see the offered projects, please login with your TU-ID!

Ongoing IP Projects

Completed IP Projects

Lecturer

Jan Peters heads the Intelligent Autonomous Systems Lab at the Department of Computer Science at the TU Darmstadt. Jan has studied computer science, electrical, control, mechanical and aerospace engineering. You can find Jan Peters in the Robert-Piloty building in S2 | 02 find in room E314.

!!Exercises Exercise 1: Download and install SL. Follow the instructions in the readme file (attached in the source code). If there are any problems, please get in touch with one of the advisers. (This exercise can be completed before the first lecture. Alternatively, if you bring a laptop with a Unix-based system, the advisers will help you with the installation during the first lecture.)
Exercise 2: Solve the ball following task (sl -> barrett -> src -> ball_following). You are required to implement a movement pattern where the robot follows a ball with its end effector. The task is partially written (see ball_following_task.c), you have to implement only the controller.
Exercise 3: Solve the ball on a beam task (sl -> barrett -> ball_on_beam). You have to implement the finite-difference gradient method in Matlab (BallOnBeam_example.m) to find the PD parameters that allow the robot to balance a ball on a beam.