Research Interests

I am interested in developing resilient autonomy for robots operating in uncertain, unstructured, and data-limited environments. My research focuses on combining machine learning, control theory, and information-driven decision-making to enable robots to learn, adapt, and act reliably in challenging domains such as rough terrain and underwater environments.

• Learning-Based Control – Integrating methods from machine learning and control theory to develop data-driven controllers for complex dynamical systems, with a focus on robustness, adaptation, and uncertainty-aware decision-making.
• Active Learning and Informative Planning for Dynamical Systems – Developing strategies that actively collect task-relevant data to identify system dynamics, reduce model uncertainty, and improve downstream control or navigation performance.
• Perception-Aware Planning and Control – Designing planning and control methods that explicitly account for perception quality, such as visibility, observability, object detection confidence, localization uncertainty, and information gain.

Student Projects and Thesis

I am looking for motivated students interested in learning-based control, robot learning, and data-driven control. I particularly welcome inquiries for Forschungspraxis (FP) and Master’s theses (MA). Bachelor’s thesis (BA) or Ingenieurpraxis (IP) topics may occasionally be available when there is a suitable well-defined project.

Please include your preferred starting date as well as your CV and transcript of records in your e-mail. This helps me to select a topic matching your background.

Potential Topics

The following topics are possible starting points. The exact scope can be adjusted depending on the project type and the student's background.

• Task-Oriented and Value-Aware Learning for Robotic Control (FP/MA)
  This topic studies how learning and data acquisition can be guided by their relevance to a downstream control task rather than by prediction accuracy alone. Possible directions include task-oriented active learning, value-aware model learning, reinforcement-learning-based formulations, and sensitivity- or value-of-information-based data selection. Applications may include navigation, autonomous vehicles, and robotic control under model uncertainty.
• Online Residual Dynamics Learning for Robotic Systems (FP/MA)
  This topic investigates online learning of unknown or residual robot dynamics using probabilistic and neural models, such as Gaussian processes, feed-forward neural networks, recurrent models, or neural differential equations. Research questions may include uncertainty calibration, heteroscedastic noise, online adaptation, multi-step prediction, computational efficiency, and robustness under changing operating conditions. Applications may include off-road, aerial, or underwater vehicles.
• Differentiable Simulation and Policy Optimization (FP/MA)
  • This topic investigates policy-learning methods that exploit gradients through differentiable robot dynamics and simulation. Possible directions include first-order policy optimization, residual policy learning, analysis of gradient quality and stability, robustness to model mismatch, and comparisons with model-free reinforcement learning. Applications may include legged robots, manipulation, and aerial robotics. Strong interest in reinforcement learning, optimization, and mathematical modeling is particularly useful.
• Perception-Aware Planning and Active Perception for Robotics (FP/MA)
  This topic studies planning and control methods that explicitly account for the quality and task relevance of perception. Possible directions include active viewpoint selection, observability-aware planning, perception uncertainty, and task-oriented information gathering. Applications may include mobile robots, aerial robots, and underwater manipulation.

Desired Background

Useful background depending on the topic: control theory, numerical optimization, reinforcement learning, probabilistic machine learning, deep learning, Python/JAX/PyTorch, or robotics. You do not need to have experience in all of these areas.