My research interests lie in the intersection of robotics and AI. My research aims to bridge the gap between robot motion planning and adversarial machine learning. It focuses on enhancing robustness of motion planners by using adversarial machine learning. Studying the capabilities and limitations of the planners contributes to the explainability in robot motion planning.
I chose to pursue a PhD with the STAI CDT because it is an excellent platform with a cluster of researchers in artificial intelligence. The concept of the cohort enables more communication and collaborations. With students focusing on a variety of topics in this field, we can learn and get inspired from each other. I believe that joining the cohort will make the PhD study a fulfilling and rewarding journey.
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Masters Qualification: M.Sc. Electrical Engineering and Information Technology, Technical University of Munich
Work Experience: Research Intern at the Munich Institute of Robotics and Machine Intelligence (MIRMI) working on robot motion planning algorithms
Conferences/Papers:
- On Safe and Time Efficient Robot Motion Planning. 2023 IEEE International Conference on Robotics and Automation (ICRA)
- Coordinate Invariant User-Guided Constrained Path Planning with Reactive Rapidly Expanding Plane-Oriented Escaping Trees. 2022 IEEE International Conference on Robotics and Automation (ICRA)
