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Technische Universität München
Technische Universität München

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

In summer 2024, Hanna Krasowski will be joining Dr. Murat Arcak’s group at UC Berkeley as a postdoctoral researcher.  Currently, she is a PhD candidate under the supervision of Prof. Dr.-Ing. Matthias Althoff and a member of the Cyber Physical Systems Group. From 2020 - 2022, she was 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. You can find more information on her personal webpage.

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.

Offered Thesis Topics

  • [MT] Falsification-guided Reinforcement Learning for Autonomous Vessels (co-advised with Florian Finkeldei) - to be submitted December 2024
  • [MT] Toward Consistency of Marine Traffic Rules for Multiple Vessel Encounters (co-advised with Maximilian Schäffeler) - to be submitted August 2024
  • [MT] How to Achieve Optimality in Safe Reinforcement Learning? (co-advised with Hannah Markgraf and Lukas Schäfer) - submitted September 2023
  • [MT] Long-term Horizon Planning for Underactuated Autonomous Vessels (co-advised with Marius Wiggert, UC Berkeley) - submitted May 2023
  • [MT] Safe Motion Planning for Underactuated Autonomous Vessels (co-advised with Marius Wiggert, UC Berkeley) - submitted May 2023
  • [BT] Traffic Rule Compliant Simulation Environment for Marine Motion Planning - submitted April 2023
  • [MT] Motion Planning for Sustainable Autonomous Vessel (co-advised with CargoKite) - submitted January 2023
  • [MT] Generalizing Marine Traffic Rules for Multiple Vessel Types and Emergencies  - submitted August 2022
  • [MT] Generating Near-collision Scenarios from Marine Traffic Data - submitted May 2022
  • [BT] Control Benchmarking of Ship Models  (co-advised with Victor Gaßmann) - submitted March 2022
  • [BT] Reachset Conformance for Dynamic Vessel Models - submitted February 2022
  • [MT] Multi-agent Reinforcement Learning for Autonomous Vessels - submitted January 2022
  • [BT] Benchmarking Provably Safe Reinforcement Learning Approaches (co-advised with Xiao Wang) - 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

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, SoSe 23]

  • 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

Publications

2023

  • Andreas Doering; Marius Wiggert; Hanna Krasowski; Manan Doshi; Pierre F.J. Lermusiaux ; Claire J. Tomlin: Stranding Risk for Underactuated Vessels in Complex Ocean Currents: Analysis and Controllers. IEEE Conference on Decision and Control (CDC), 2023 mehr… BibTeX
  • Hanna Krasowski; Jakob Thumm; Marlon Müller; Lukas Schäfer; Xiao Wang; Matthias Althoff: Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarking. Transactions on Machine Learning Research, 2023 mehr… BibTeX
  • Hanna Krasowski; Prithvi Akella; Aaron D. Ames; Matthias Althoff: Safe Reinforcement Learning with Probabilistic Guarantees Satisfying Temporal Logic Specifications in Continuous Action Spaces. IEEE Conference on Decision and Control (CDC), 2023 mehr… BibTeX Volltext (mediaTUM)
  • Niklas Kochdumper; Hanna Krasowski; Xiao Wang; Stanley Bak; Matthias Althoff: Provably Safe Reinforcement Learning via Action Projection using Reachability Analysis and Polynomial Zonotopes. IEEE Open Journal of Control Systems, 2023 mehr… BibTeX Volltext ( DOI ) Volltext (mediaTUM)

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)
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