Foto von Hanna Krasowski

Hanna Krasowski

Technische Universität München

Informatik 6 - Professur für Cyber Physical Systems (Prof. Althoff)

Postadresse

Postal:
Boltzmannstr. 3
85748 Garching b. München

Curriculum Vitae

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). She visited the research group of Prof. Aaron Ames at the California Institute of Technology from July to December 2022. 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.

Offered Thesis Topics

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.

Currently Available

Ongoing

  • [MT] Long-term Horizon Planning for Underactuated Autonomous Vessels (co-advised with Marius Wiggert, UC Berkeley)
  • [MT] Safe Motion Planning for Underactuated Autonomous Vessels (co-advised with Marius Wiggert, UC Berkeley)
  • [BT] Traffic Rule Compliant Simulation Environment for Marine Motion Planning

Finished

 

Teaching

Lectures

  • Formal Methods for Cyber-Physical Systems [WiSe 20/21, WiSe 21/22] – Safe Reinforcement Learning
  • Cyber-Physical Systems [SoSe 21, SoSe 22] – Discrete Systems

Practical Course – Motion Planning for Autonomous Vehicles [WiSe 20/21, SoSe 21, WiSe 21/22, SoSe 22]

  • Benchmarking Marine Motion Planning
  • Reinforcement Learning for Autonomous Vessels
  • Set-based Prediction of Vessels
  • Developing an Autonomous Vessel Simulation
  • Motion Planning for Autonomous Vessels

Seminar – Cyber-Physical Systems [WiSe 20/21, SoSe 21, WiSe 21/22, SoSe 22]

  • Review on Motion Planning and Control Strategies for Autonomous Vessels
  • Safe Reinforcement Learning for Motion Planning
  • Dynamic Vessel Models and their Applications
  • Safe Reinforcement Learning with Logical Specifications
  • Safe Multi-Agent Reinforcement Learning

 

Tool CommonOcean

CommonOcean is a collection of composable benchmarks for motion planning of autonomous vessels and provides researchers with means of evaluating and comparing their motion planners. A benchmark consists of a scenario with a planning problem, a vessel model including vessel parameters, and a cost function composing a unique id. Along with benchmarks, we provide tools for motion planning.

 

Publications

2022

2021

2020

  • Hanna Krasowski; Xiao Wang; Matthias Althoff: Safe Reinforcement Learning for Autonomous Lane Changing Using Set-Based Prediction. 2020 IEEE International Conference on Intelligent Transportation Systems (ITSC), 2020 mehr… BibTeX Volltext ( DOI ) Volltext (mediaTUM)