Studies of electrical engineering and information technology at the Karlsruhe Institute of Technology (KIT) with majoring in control technology. Active membership in the Formula Student team KA-RaceIng with responsibility for data acquisition in the season 2016 and team leadership of electronics department in the season 2017. Practical experience at Mercedes-AMG in the development of high-performance high voltage batteries and subsequent bachelor thesis on electro-thermal modeling and optimization of high voltage batteries for hybrid vehicles (2019).

Research assistant at FZI on function development for visualizations of driver assistance functions(2020). Research stay and Master's thesis at the University of Waterloo in Canada on " Cooperative State Estimation for Autonomous Mobile Robots" (2022).
 
Since June 2022, he has been a research scientist in the Control in Information Technology (CIT) group within the Embedded Systems and Sensors Engineering (ESS) department. His research focuses on navigation and motion planning for autonomous mobile robots in dynamic, crowded environments, with a particular emphasis on deep reinforcement learning (DRL) based approaches.

Since July 2026, he has been Head of the CIT department. In close collaboration with Prof. Dr.-Ing. Sören Hohmann, one of the department’s key research areas, is motion planning for autonomous robotic systems in the context of human-robot interaction. 

Socially Integrated Navigation of Autonomous Robots 

Autonomous mobile robots increasingly operate in the same space as humans and take over their tasks. Existing Deep Reinforcement Learning (DRL) approaches exhibit a fixed, predefined social behavior and treat humans as obstacles rather than individuals with their own goals and preferences. My research closes this gap with a novel Socially Integrated (SI) navigation approach. The robot learns to adapt to human preferences through interaction. This approach marks a shift in perspective for navigation in dynamic systems and outperforms learning-based and non-learning-based state-of-the-art methods in navigation efficiency and adaptability. This new approach and shift in perspective are a further step toward a scalable, human-accepted deployment of autonomous robots.
 
Motion Planning of Robotic Systems:
 
Current research addresses hardware-agnostic motion planning for robotic systems in unstructured environments. A shared interface connects mobile platforms and legged robots with planning methods such as Model Predictive Control (MPC), Deep Reinforcement Learning (DRL), and Vision-Language-Action (VLA) models. Application fields range from intralogistics to rough terrain to urban environments.