Hanna Krasowski joined the Cyber Physical Systems Group as a PhD candidate under the supervision of Prof. Dr.-Ing. Matthias Althoff in 2020. She is a member of the DFG Research Training Group on Continuous Verification of Cyber-Physical Systems (ConVeY). Hanna received her master's degree in Robotics, Cognition, Intelligence from Technical University of Munich in 2020 and her bachelor's degree in Mechanical and Process Engineering from Technical University of Darmstadt in 2017.
Her research interests include safe reinforcement learning, motion planning and formal methods.
I am always looking for self-motivated students to solve interesting problems arising in my research areas. If you are interested in my research and want to write a thesis in this field, simply send me a mail briefly describing your motivation.
- [BT] Control Benchmarking of Ship Models
- [BT] Reachset Conformance for Dynamic Vessel Models
- [MT] Generalizing Marine Traffic Rules for Multiple Vessel Types and Emergencies
- [MT] Generating Near-collision Scenarios from Marine Traffic Data
- [MT] Multi-agent Reinforcement Learning for Autonomous Vessels
- [BT] Benchmarking Provably Safe Reinforcement Learning Approaches - submitted October 2021
- [BT] Simulation Environment for Marine Motion Planning - submitted September 2021
- [MT] Safe and Efficient Reinforcement Learning for Autonomous Driving in Urban Scenarios - submitted April 2021
- [BT] Generation of Benchmarks for Marine Motion Planning - submitted April 2021
- Formal Methods for Cyber-Physical Systems [WS 20/21, WS 21/22] – Safe Reinforcement Learning
- Cyber-Physical Systems [SS 21] – Discrete Systems
Practical Course – Motion Planning for Autonomous Vehicles [WS 20/21, SS 21, WS 21/22]
- Benchmarking Marine Motion Planning
- Reinforcement Learning for Autonomous Vessels
- Developing an Autonomous Vessel Simulation
Seminar – Cyber-Physical Systems [WS 20/21, SS 21, WS 21/22]
- Review on Motion Planning and Control Strategies for Autonomous Vessels
- Near-collision Detection for Marine Traffic
- Safe Reinforcement Learning for Motion Planning
- Temporal Logic Formalization of Marine Traffic Rules. 2021 IEEE Intelligent Vehicles Symposium (IV), 2021 mehr… BibTeX Volltext ( DOI ) Volltext (mediaTUM)
- CommonRoad-RL: A Configurable Reinforcement Learning Environment for Motion Planning of Autonomous Vehicles. IEEE International Conference on Intelligent Transportation Systems (ITSC), 2021 mehr… BibTeX Volltext ( DOI ) Volltext (mediaTUM)