We offer these current topics directly for Bachelor and Master students at TU Darmstadt. Note that we cannot provide funding for any of these theses projects.

We highly recommend that you do either our robotics and machine learning lectures (Robot LearningStatistical Machine Learning) or our colleagues (Grundlagen der Robotik, Probabilistic Graphical Models and/or Deep Learning). Even more important to us is that you take both Robot Learning: Integrated Project, Part 1 (Literature Review and Simulation Studies) and Part 2 (Evaluation and Submission to a Conference) before doing a thesis with us.

In addition, we are usually happy to devise new topics on request to suit the abilities of excellent students. Please DIRECTLY contact the thesis advisor if you are interested in one of these topics. When you contact the advisor, it would be nice if you could mention (1) WHY you are interested in the topic (dreams, parts of the problem, etc.), and (2) WHAT makes you special for the projects (e.g., class work, project experience, special programming or math skills, prior work, etc.). Supplementary materials (CV, grades, etc.) are highly appreciated. Of course, such materials are not mandatory, but they help the advisor to see whether the topic is too easy, just about right, or too hard for you.

FOR FB16+FB18 STUDENTS: Students from other departments at TU Darmstadt (e.g., ME, EE, IST), you need an additional formal supervisor who officially issues the topic. Please do not try to arrange your home dept advisor by yourself but let the supervisor get in touch with that person instead!

Adaptive Human-Robot Interactions with human Trust maximization

Scope: Master thesis
Advisor: Kay Hansel,Georgia Chalvatzaki
Start: April 2022

Topic: Building trust between humans and robots is a major goal of Human-Robot Interaction (HRI). Usually, trust in HRI has been associated with risk aversion: a robot is trustworthy when its actions do not put the human at risk. However, we believe that trust is a bilateral concept that governs the behavior and participation in the collaborative tasks of both interacting parties. On the one hand, the human has to trust the robot about its actions, e.g., delivering the requested object, acting safely, and interacting in a reasonable time horizon. On the other hand, the robot should trust the human regarding their actions, e.g., have a reliable belief about the human’s next action that would not lead to task failure; a certainty in the requested task. However, providing a computational model of trust is extremely challenging.

Therefore, this thesis explores trust maximization as a partially observable problem, where trust is considered as a latent variable that needs to be inferred. This consideration results in a dual optimization problem for two reasons: (i) the robot behavior must be optimized to maximize the human’s latent trust distribution; (ii) an optimization of the human’s prediction model must be performed to maximize the robot’s trust. To address this challenging optimization problem, we will rely on variational inference and metrics like Mutual Information for optimization.
Highly motivated students can apply by sending an e-mail expressing your interest to, attaching a letter of motivation and possibly your CV.


  • Good knowledge of Python and/or C++;
  • Good knowledge in Robotics and Machine Learning;
  • Good knowledge of Deep Learning frameworks, e.g, PyTorch

[1] Xu, Anqi, and Gregory Dudek. “Optimo: Online probabilistic trust inference model for asymmetric human-robot collaborations.” ACM/IEEE HRI, IEEE, 2015;
[2] Kwon, Minae, et al. “When humans aren’t optimal: Robots that collaborate with risk-aware humans.” ACM/IEEE HRI, IEEE, 2020;
[3] Chen, Min, et al. “Planning with trust for human-robot collaboration.” ACM/IEEE HRI, IEEE, 2018;
[4] Poole, Ben et al. “On variational bounds of mutual information”. ICML, PMLR, 2019.

Causal inference of human behavior dynamics for physical Human-Robot Interactions

Scope: Master Thesis
Advisor: Georgia Chalvatzaki, Kay Hansel

Topic: In this thesis, we will study and develop ways of approximating an efficient behavior model of a human in close interaction with a robot. We will research the extension of our prior work on the graph-based representation of the human into a method that leverages multiple attention mechanisms to encode relative dynamics in the human body. Inspired by methods in causal discovery, we will treat the motion prediction problem as such. In essence, the need for a differentiable and accurate human motion model is essential for efficient tracking and optimization of HRI dynamics. You will test your method in the context of motion prediction, especially for HRI tasks like human-robot handovers, and you could demonstrate your results in a real-world experiment.

Highly motivated students can apply by sending an e-mail expressing your interest to, attaching a letter of motivation and possibly your CV.

Minimum knowledge

  • Good knowledge of Python and/or C++;
  • Good knowledge of Robotics;
  • Good knowledge of Deep Learning frameworks, e.g, PyTorch


  1. Li, Q., Chalvatzaki, G., Peters, J., Wang, Y., Directed Acyclic Graph Neural Network for Human Motion Prediction, 2021 IEEE International Conference on Robotics and Automation (ICRA).
  2. Löwe, S., Madras, D., Zemel, R. and Welling, M., 2020. Amortized causal discovery: Learning to infer causal graphs from time-series data. arXiv preprint arXiv:2006.10833.
  3. Yang, W., Paxton, C., Mousavian, A., Chao, Y.W., Cakmak, M. and Fox, D., 2020. Reactive human-to-robot handovers of arbitrary objects. arXiv preprint arXiv:2011.08961.

Discovering neural parts in objects with invertible NNs for robot grasping

Scope: Master Thesis
Advisor: Georgia Chalvatzaki, Despoina Paschalidou

In this thesis, we will investigate the use of 3D primitive representations in objects using Invertible Neural Networks (INNs). Through INNs we can learn the implicit surface function of the objects and their mesh. Apart from extracting the object’s shape, we can parse the object into semantically interpretable parts. In our work our main focus will be to segment the parts in objects that are semantically related to object affordances. Moreover, the implicit representation of the primitive can allow us to compute directly the grasp configuration of the object, allowing grasp planning. Interested students are expected to have experience with Computer Vision and Deep Learning, but also know how to program in Python using DL libraries like PyTorch.

Highly motivated students can apply by sending an e-mail expressing your interest to


  1. Paschalidou, Despoina, Angelos Katharopoulos, Andreas Geiger, and Sanja Fidler. “Neural Parts: Learning expressive 3D shape abstractions with invertible neural networks.” arXiv preprint arXiv:2103.10429 (2021).
  2. Karunratanakul, Korrawe, Jinlong Yang, Yan Zhang, Michael Black, Krikamol Muandet, and Siyu Tang. “Grasping Field: Learning Implicit Representations for Human Grasps.” arXiv preprint arXiv:2008.04451 (2020).
  3. Chao, Yu-Wei, Wei Yang, Yu Xiang, Pavlo Molchanov, Ankur Handa, Jonathan Tremblay, Yashraj S. Narang et al. “DexYCB: A Benchmark for Capturing Hand Grasping of Objects.” arXiv preprint arXiv:2104.04631 (2021).
  4. Do, Thanh-Toan, Anh Nguyen, and Ian Reid. “Affordancenet: An end-to-end deep learning approach for object affordance detection.” In 2018 IEEE international conference on robotics and automation (ICRA), pp. 5882-5889. IEEE, 2018.

Cross-platform Benchmark of Robot Grasp Planning

Scope: Master Thesis
Advisor: Georgia Chalvatzaki, Daniel Leidner

Grasp planning is one of the most challenging tasks in robot manipulation. Apart from perception ambiguity, the grasp robustness and the successful execution rely heavily on the dynamics of the robotic hands. The student is expected to research and develop benchmarking environments and evaluation metrics for grasp planning. The development in simulation environments as ISAAC Sim and Gazebo will allow us to integrate and evaluate different robotic hands for grasping a variety of everyday objects. We will evaluate grasp performance using different metrics (e.g., object-category-wise, affordance-wise, etc.), and finally, test the sim2real gap when transferring such approaches from popular simulators to real robots. The student will have the chance to work with different robotic hands (Justin hand, PAL TIAGo hands, Robotiq gripper, Panda gripper, etc.) and is expected to transfer the results to at least two robots (Rollin’ Justin at DLR and TIAGo++ at TU Darmstadt). The results of this thesis are intended to be made public (both the data and the benchmarking framework) for the benefit of the robotics community. As this thesis is offered in collaboration with the DLR institute of Robotics and Mechatronics in Oberpfaffenhofen near Munich, the student is expected to work in DLR for a period of 8-months for the thesis. On-site work at the premises of DLR can be expected but not guaranteed due to COVID-19 restrictions. A large part of the project can be carried out remotely.

Highly motivated students can apply by sending an e-mail expressing your interest to and


  1. Collins, Jack, Shelvin Chand, Anthony Vanderkop, and David Howard. “A Review of Physics Simulators for Robotic Applications.” IEEE Access (2021).
  2. Bekiroglu, Y., Marturi, N., Roa, M. A., Adjigble, K. J. M., Pardi, T., Grimm, C., … & Stolkin, R. (2019). Benchmarking protocol for grasp planning algorithms. IEEE Robotics and Automation Letters, 5(2), 315-322.
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