Research

Autonomous system development is challenging due to dynamic environments, perception uncertainties, and model disturbances. Machine learning (ML) can solve complex automation tasks, yet purely ML-based controllers typically lack interpretability, safety guarantees, and require large high-quality training datasets. In contrast, model-based methods provide guarantees and explainability for autonomous systems, but often require substantial engineering and expert knowledge, leading to low transferability.

In my research, I aim to unlock the potential of ML-based techniques for real-world systems by incorporating formal methods to achieve data-efficient, reliable and interpretable autonomous systems. Using this hybrid control approach, I currently focus on methods that enable the use of abstract system knowledge to guide the learning process. Abstract system knowledge is rich in insight yet difficult to codify, for example traffic rules or natural-language descriptions of system behavior. I leverage formal methods, such as temporal logic and reachability analysis, to make abstract knowledge computationally tractable and design ML-based algorithms that learn from it.

Applications

I demonstrate the effectiveness of my control approaches on a variety of autonomous systems, with a special focus on motion planning for maritime vessels. Automation of maritime traffic holds immense potential to improve safety, prevent environmental damage, and enhance economic efficiency. At the same time, autonomous vessels provide a challenging safety-critical control problem due to low-frequency traffic data with significant uncertainty, complex dynamical behavior, as well as abstract knowledge implicit in legal documents or expert handbooks, which currently remains unused.

More recently, my research expanded into systems biology, where I apply my approaches to biomolecular systems. While large-scale genetic and protein data is available in biology, certain steps in diagnostics and drug discovery still lack adequate models due to data-scarcity. For example, in situ measurement often remains unfeasible or is prohibitively costly. My research aims to infer and synthesize biomolecular models by codifying abstract knowledge and qualitative observations, which enables using hybrid approaches to uncover cell-cell or gene-gene interaction.

In brief, I work at the intersection of formal methods, machine learning, and robotics. My work can be clustered into four thrusts:

  1. Hybrid algorithms for reliable machine learning: Integrate guarantees into learning algorithms to ensure task or safety requirements are met.
  2. Formal methods for model guidance: Codify abstract system knowledge with formal methods to efficiently guide model learning and enhance performance.
  3. Automation of complex real-world tasks: Design and validate hybrid approaches by applying them to maritime vessel motion planning and biomolecular models.
  4. Software for autonomous vessel navigation: Develop and publish open-source software for benchmarking and evaluation of vessel motion planners.

Selected Publications

2024

  1. preprint_safeRLvessels.png
    Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Sea
    Hanna Krasowski, and Matthias Althoff
    IEEE Transactions on Intelligent Vehicles, 2024
  2. preprint_continuousActionMasking.png
    Excluding the Irrelevant: Focusing Reinforcement Learning through Continuous Action Masking
    Roland Stolz, Hanna Krasowski, Jakob Thumm, Michael Eichelbeck, Philipp Gassert, and Matthias Althoff
    In Proc. of the Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS), 2024

2023

  1. OJ-CSYS2023.png
    Provably Safe Reinforcement Learning via Action Projection Using Reachability Analysis and Polynomial Zonotopes
    Niklas Kochdumper, Hanna Krasowski, Xiao Wang, Stanley Bak, and Matthias Althoff
    IEEE Open Journal of Control Systems, 2023
  2. TMLR2023_ProvablySafeRLComparison.png
    Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarking
    Hanna Krasowski, Jakob Thumm, Marlon Müller, Lukas Schäfer, Xiao Wang, and Matthias Althoff
    Transactions on Machine Learning Research, 2023