Bachelor and Master theses

In the following list are topics for both Master or Bachelor theses, please get in touch with the contact person listed, to find out more (last update August 2022).

Master Theses

Topics already taken

Topic: Using Affordance-based Attention to Optimize Depth Data Processing in Mobile Robot Teleoperation Scenarios (already taken)

Supervisors: Constantin Uhde, Nicolas Berberich

Abstract: Bandwidth is a major factor when controlling robotic systems remotely. Using high-density pointcloud data to visualize the surroundings of the system makes optimization necessary, if one wants to maintain responsive control. The aim of this project is to utilize affordance information about the environment [1], in order to apply foveation [2] as a form of lossy compression to the pointcloud data stream from the robot to the operator. The methods will be implemented on an Nvidia Jetson Nano, which will be installed on a PR2 robot platform. Two implementations for region-of-interest selection will be compared in an experimental setup.


- Solid knowledge in C++

- Some experience with pointclouds, robotics, virtual reality


[1] Lueddecke, T., Kulvicius, T., & Woergoetter, F. (2019). Context-based affordance segmentation from 2D images for robot actions. Robotics and Autonomous Systems, 119, 92-107.

[2] Ude, A., Atkeson, C. G., & Cheng, G. (2003, October). Combining peripheral and foveal humanoid vision to detect, pursue, recognize and act. In Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003)(Cat. No. 03CH37453) (Vol. 3, pp. 2173-2178). IEEE.

Topic: Neuromorphic object recognition and scene representation for robotic applications (already taken)

Supervisor: Elvin Hajizada

Abstract: In collaboration with the Neuromorphic Computing Lab of Intel Labs, we offer Bachelor and Master thesis projects, as well as semester projects on the topic of neuromorphic computing in robotics.

The object recognition task in robotic applications significantly differs from that in image classification scenarios. The difference in the nature of available visual data makes deep learning methods poor fit for the robotic object recognition scenarios [1]. Aim of this project is to build spiking neuronal network (SNN) algorithms and systems for continual learning of objects and building a scene representation [2,3]. In contrast to the backpropagation-based methods, the developed architecture should learn new objects incrementally, without requiring the retraining the network each time a new object is presented [4]. The developed SNN will be implemented on the Intel’s neuromorphic research chip Loihi to leverage the event-based processing, on-chip learning, fine-grained parallelism, and energy efficiency of these chips. Subsequently, the network will be tested in experiments with a humanoid robot iCub.


  • Solid knowledge of python
  • Some experience with robotics and/or computer vision. C++/ROS/Yarp/OpenCV
  • Knowledge about the basics of machine learning and neuronal networks will be advantageous.


[1] Pasquale, Giulia & Ciliberto, Carlo & Odone, Francesca & Rosasco, Lorenzo & Natale, Lorenzo. (2017). Are we Done with Object Recognition? The iCub robot's Perspective. Robotics and Autonomous Systems. 112. 10.1016/j.robot.2018.11.001.

[2] M. Mozafari, S. R. Kheradpisheh, T. Masquelier, A. Nowzari-Dalini and M. Ganjtabesh, "First-Spike-Based Visual Categorization Using Reward-Modulated STDP," in IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 12, pp. 6178-6190, Dec. 2018, doi: 10.1109/TNNLS.2018.2826721.

[3] Gerstner, Wulfram & Lehmann, Marco & Liakoni, Vasiliki & Corneil, Dane & Brea, Johanni. (2018). Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules. Frontiers in Neural Circuits. 12. 10.3389/fncir.2018.00053.

[4] Parisi, German & Kemker, Ronald & Part, Jose & Kanan, Christopher & Wermter, Stefan. (2019). Continual Lifelong Learning with Neural Networks: A Review. Neural Networks. 113. 54-71. 10.1016/j.neunet.2019.01.012.

Bachelor Theses

Topic: Influence of biofeedback in back posture (already taken)

Advisor: Natalia Paredes

Poor back posture weakens the tissues in the lower back causing pain and complications if not treated. Nowadays, pose estimation can be detected by wearable sensors. It is still unknown if a constant feedback of pose estimation has an effect on the pose of the person.

In this study you will research on previous studies on back posture estimation and you will work with wearable sensors, biofeedback and app development to analyze long term pose estimation. If you are interested please send an email with your CV, grades, and explaining your motivation!


- Solid knowledge of C++

- Previous experience with Android/iOS app development

- Some experience with microcontrollers

- (desired) Experience with soldering


[1] Simpson, L., Maharaj, M.M. & Mobbs, R.J. The role of wearables in spinal posture analysis: a systematic review. BMC Musculoskelet Disord 20, 55 (2019).