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Student Projects & Theses


We have several opportunities for students to work with us on the latest research in Reinforcement Learning.

The projects are listed below, and you are also encouraged to propose your own topic and cooperate with us. 

For potential candidates, please send us an email to 


Current Projects

Vision Transformers for efficient policy learning

Description Learning policies from raw videos is often infeasible in real world robotics, as current approaches require large amounts of training data. Extracting object keypoints, can make training significantly faster, unlocking a plethora of interesting tasks. However, they currently require specialized pretraining.
Using vision transformers can remove the need for specialized training and thus make the technique widely available.
In this project, the student(s) first evaluate the keypoint quality for state-of-the-art methods and then extend the technique to more challenging situations.
Hands-on policy learning on a real robot is possible and encouraged.

Contact Jan Ole von Hartz


Keypoints for efficient policy learning

Description As in the project above, we use object keypoints to learn policies more efficiently.
In this project, the student(s) combine object keypoints with the novel SAC-GMM algorithm for policy learning on a real robot.

Contact Jan Ole von Hartz



Previous Projects

Uncertainty-driven Offline model-based RL

Description In this project, the development and usage of world-models in combination with uncertainty estimations for offline Reinforcement Learning is to be explored (more details).

State    full

Application of Recurrent Neural Network in Autonomous Driving

Description  The state observation is sometimes noisy and partially observed in autonomous driving, which is challenging to solve with usual RL architectures. The recurrent neural network (RNN) is a simple and potential representation for this partial observation. In this project, students are encouraged to explore the usage of RNN in autonomous driving applications. 

State    full

Autoinflammatory Disease Treatment Recommendation

Description  In cooperation with the foundation  Rhumatismes-Enfants-Suisse  , we develop algorithms for autoinflammatory disease treatment recommendation. The project mainly focuses on unsupervised deep learning, and depending on the progress, on basic deep reinforcement learning (  more details  ).

Contact  Maria Huegle

State  full

High-Level Decision Making in Autonomous Driving

Description  We develop deep reinforcement learning algorithms for autonomous lane changes using the open-source traffic simulator  SUMO  . We focus on various aspects, for example on mixed action spaces, constraints and including predictions of traffic participants.

Contact  Gabriel Kalweit  and  Maria Hügle

State  full

Machine Learning for Disease Progression Prediction in Rheumatoid Arthritis

Description  In cooperation with the  University Hospital in Lausanne,  we develop algorithms to predict the disease progression in arthritis based on the  Swiss Quality Management (SCQM)  database, including lab values, medication, clinical data and patient reported outcomes.

Contact  Maria Huegle

State  full

Unsupervised Skill Learning from Video

Description In his thesis, Markus Merklinger introduces a model to leverage information from multiple label-free demonstrations in order to yield a meaningful embedding for unseen tasks. A distance measure in the learned embedding space can then be used as a reward function within a reinforcement learning system.

Contact Oier Mees and Gabriel Kalweit

Unsupervised Learning for Early Seizure Detection

In cooperation with the Epilepsy Center in Freiburg, we develop unsupervised learning algorithms to detect epileptic seizures based on intracranial EEG (EcoG) data .

Contact Maria Huegle